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Abstract

A 4-year prospective cohort study on patients with lung, gastric, hepatic, colorectal, breast, uterine, and ovarian cancer was conducted at the East-West Cancer Center (EWCC) of Daejeon Korean Medicine Hospital in Daejeon, Korea. We divided patients into 2 groups based on how long they had been receiving TKM oncotherapy and compared event-free survival (EFS), telomere length change, and quality of life (QoL). The study collected data on 83 patients from October 2016 to June 2020 and discovered no statistical differences in EFS based on the duration of TKM oncotherapy. In the analysis of changes in QoL outcomes, there were no statistically significant group differences between the groups. After controlling for covariates that could affect telomere length, the long-term TKM oncotherapy group had a higher daily telomere attrition rate. The study of the relationship between telomere length and prognostic factors discovered that patients with advanced N stage at the time of diagnosis and who had previously received radiotherapy had shorter telomere length. When examining associations between SNP genotype and percentile score of telomere length, this study was able to confirm an association between telomere length and rs4387287. This study is significant because it is the first to assess the effects of TKM oncotherapy and investigate telomere length-related factors. To assess the effects of TKM oncotherapy on cancer patients’ survival and QoL, a longer-term observational study with a larger sample size is required.

Keywords

traditional Korean medicine oncotherapy telomere length cancer patient quality of life

Introduction

Cancer patients have been increasingly seeking out complementary and alternative medicine (CAM) worldwide. It is estimated that 40% to 60% of adult cancer patients use CAM.^1-4 Cancer patients seek out CAM to mitigate side effects of conventional cancer treatment (CCT) such as chemotherapy and radiotherapy,^5-8 promote health, manage disease symptoms, prevent illness, or improve immune function.⁷ In Korea, traditional Korean medicine (TKM), which uses approaches involving herbal medicine, acupuncture, moxibustion, pharmacoacupuncture, and so on, has been frequently applied to treat cancer as one part of CAM.⁸ While the strategy of CCT is mainly to remove cancerous lesions by killing cancer cells, the key strategy of TKM oncotherapy is encouraging tumor suppression by boosting the immune system and targeting the tumor microenvironment by regulating inflammation and neovascularization.^9-11 A number of recent preliminary and clinical studies have supported the effect of TKM on cancer treatment, particularly in the areas of anticancer, chemoprotective effects, radioprotective effects, and cancer symptom management.^8,9 However, only a small amount of prospective cohort evidence has been reported on the effects of TKM oncotherapy on cancer patients’ survival and quality of life (QoL). To address this limitation, this study established a cancer cohort registry in October 2016 and evaluated the effect of TKM oncotherapy on survival and QoL outcomes in cancer patients, as well as analyzing demographic and clinical data. In addition to the usual outcomes of survival and QoL research, this study looked at the effect of TKM oncotherapy on telomere length attrition in cancer patients. The factors associated with telomere length change were also studied using demographic and clinical data from patients, as well as a microarray for single nucleotide polymorphism (SNP) genotyping. To summarize, this prospective cohort study investigated whether TKM oncotherapy influenced cancer patients’ survival, QoL outcomes, and telomere length change, as well as the factors associated with cancer patients’ telomere lengths.

Methods

Objectives

The primary objective of this study is to evaluate the effect of TKM oncotherapy on event-free survival (EFS). The events are defined as recurrence, metastasis, progression of cancer, and death. The secondary objectives are to describe demographic and clinical characteristics of cancer patients who had decided to receive TKM oncotherapy, to compare the change of telomere length, and QoL outcomes in patients who were offered ≥21 days of TKM oncotherapy to who were offered <21 days of TKM oncotherapy, and to find the factors associated with the change of telomere length.

Study Design and Participants

We conducted a prospective cohort study at the East-West Cancer Center (EWCC) of Daejeon Korean Medicine Hospital, a university-affiliated integrative cancer center, from October 2016 to June 2020. The protocol for this study was approved by the Daejeon Korean Medicine Hospital’s Institutional Review Board (IRB) on September 19, 2016 (Study approval No. DJDSKH-16-BM-09). Participants who enrolled in the cohort registry between the 5th of October 2016 and the 21st of November 2019 were subjected to a baseline analysis. Follow-up visits were scheduled every 6 months (±1 month) until 1 year after the baseline, and then every year (±1 month). The longest period of follow-up allowed was 4 years. Analyses requiring data on the days of TKM oncotherapy in the duration of follow-up and the change in outcome measurements at 2 points were carried out for participants who completed at least 2 follow-up visits up to June 30th, 2020. Demographic and clinical data were extracted from electronic case report forms (eCRF).

The following were the eligibility requirements: (1) people between the ages of 18 and 80 who have primary lung, gastric, hepatic, colorectal, breast, uterine, and ovarian cancer; (2) persons diagnosed within 1 year of the primary cancer diagnosis, with no history of recurrence, and who do not correspond to code 7 on the SEER summary stage [distant site(s)/node(s) involved]; (3) persons who have medical records which include the date of first diagnosis, the stage of the cancer, and pathological results; (4) persons who are receiving conventional cancer treatments, who have finished conventional cancer treatments without residual mass, or who are receiving only traditional Korean medicine oncotherapy after ceasing or with no experience of conventional cancer treatments; (5) persons who agreed to receive traditional Korean medicine oncotherapy at the East-West Cancer Center, Daejeon Korean Medicine Hospital of Daejeon University; (6) persons with Eastern Cooperative Oncology Group Performance Status less than 2; (7) persons who voluntarily agree to participate in the study; (8) persons who consent to provide personal information. The exclusion criteria were the following: (1) Persons with life expectancy less than 3 months or whose Eastern Cooperative Oncology Group Performance Status more than 3; (2) Persons who do not have medical records that include the date of first diagnosis, stage, and pathological results; (3) Persons who are unable to participate in the study due to other medical conditions or mental problems; (4) Persons who are considered inappropriate to participate in the study by the physician.

The Definition of Groups by the TKM Oncotherapy duration

The TKM oncotherapy in this study is defined as the treatment containing at least one of the multi-herbal formula containing Panax Notoginseng Radix, Cordyceps militaris, Boswellia carteri Birdwood, and Panax ginseng C.A.Mey; (HAD), the herbal formula mainly containing Rhus verniciflua Stokes (Geonchil-Jung); and cultivated wild ginseng pharmacopuncture. By consensus, the EWCC research team set the threshold for TKM oncotherapy duration at 21 days, referring to previous EWCC studies.^12-14 At the time of analysis, participants were divided into 2 groups based on the duration of TKM oncotherapy exposure: those who received ≥21 days of TKM oncotherapy and those who received <21 days of treatment. Because the duration of TKM oncotherapy was determined by the patients’ willingness, the duration of TKM oncotherapy reflects the patients’ compliance with TKM oncotherapy. During the follow-up period, the duration of TKM oncotherapy was recorded, and the days of treatment in separate sessions were totaled.

Variables

To evaluate the factors associated with the duration of TKM oncotherapy, variables including gender, age, BMI, residence, marital status, occupation, education, income, private insurance status, cancer insurance status, expense insurance status, age at the first diagnosis, prior surgery, prior radiotherapy, prior chemo/immuno/hormonal therapy, disease status, number stage and T/N stage at the first diagnosis, number stage and T/N stage at enrollment, currently receiving cancer treatment, EORTC-QLQ-C30, BDI-2, and STAI-KYZ scores were collected and analyzed. To compare the change in telomere lengths and QoL outcomes between groups over the follow-up period, the telomere length at the baseline and last visit, the telomere length attrition rate, and QoL outcomes such as EORTC-QLQ-C30, BDI-2, and STAI-KYZ at enrollment and last visit records were collected. In telomere length, the daily attrition rate, or telomere length decrement per day, was calculated. Gender, smoking status, alcohol consumption status, age, BMI, income, BDI-2, marital status, and the insomnia item in the EORTC-QLQ-C30 were used as potential confounders of telomere length in the analysis of telomere length to adjust for pre-existing group differences. To evaluate the factors associated with the daily attrition rate in telomere length, duration of TKM oncotherapy, prior surgery, prior radiotherapy, prior chemo/immuno/hormonal therapy, number stage and T/N stage at the first diagnosis, number stage and T/N stage at the enrollment, currently receiving cancer treatment, ECOG PS, BDI-2, and STAI-KYZ scores were collected for analysis.

Measurement of Telomere Length

For the test of telomere length, 3 to 4 ml of blood was draw into an ethylenediaminetetraacetic acid (EDTA) tube. Then the blood sample was sent to Mediage Lab (635, 42, Changeop-ro, Sujeong-gu, Seongnam-si, Gyeonggi-do) and measured using quantitative polymerase chain reaction (Q-PCR).¹⁵ In Mediage Lab, the percentile score of telomere length per age group was calculated using the Korean adult standard.

SNP Genotyping Based on Microarray

Selection of SNPs

We selected independent 13 SNPs which had been reported to have a strong association with telomere length in previous studies.^16-18 A list of the selected SNPs with the relevant information can be found in Supplemental Table 1.

Genomic DNA extraction from buccal swabs

The freeze-dried plasma was sent to Eone-Diagnomics Genome Center (EDGC), a company specializing in genome analysis, to perform the microarray for SNP genotyping. DNA from buccal swabs was extracted using the AccuSaliva DNA preparation kit (AccuGene) according to the manufacturer’s protocol. DNA yield and quality was estimated using NanoDrop™ One (Thermo Fisher Scientific). DNA for concentration with minimum 20 ng/µl and purity with ranging 1.6 to 2.3 of A260/A280 ratio was used in the microarray.

Genotyping based on microarray

A total of 200 ng of DNA in each sample was used in microarray for SNP genotyping. Genotyping was performed on the Illumina platform using Infinium Global Screening Array-24+ v1.0 BeadChip which contains>680 000 SNPs, according to the manufacturer’s protocol (http://www.illumina.com). A whole genome amplification reaction was performed, followed by denaturation and hybridization to BeadChips. Un-hybridized and non-specifically hybridized DNA was removed through washing, following which a single base extension reaction was performed to incorporate differentially labeled nucleotides at the SNP sites. BeadChips were imaged and data were collected using the iScan System (Illumina), and then all the raw intensity data were analyzed using Illumina GenomeStudio Genotyping Module which does automated genotype clustering and calling. The SNPs were evaluated by GenCall score, which is a quality metric calculated for each genotype, and ranges from 0 to 1. To obtain the highest genotyping accuracy, all genotypes with GenCall score below 0.15 were considered as no-calls. Samples with overall genotype call rate more than 98% were further analyzed, and those of less than 98% were excluded.

Statistical Analysis

To describe demographic and clinical characteristics of patients, counts (%), means, standard deviations (SDs), and ranges were calculated. To assess the variables which can affect the duration of TKM oncotherapy and the change of telomere lengths, multiple regression analysis, and stepwise method were applied to determine the contribution of the variables. The effect of TKM oncotherapy on EFS was analyzed by Kaplan-Meier analysis and Log Rank test. To compare the change of QoL outcomes and telomere length between 2 groups, Student’s t-test or Mann-Whitney test were used. When the adjustment of potential confounders was required, analysis of covariance (ANCOVA) was conducted. To examine the association between the SNPs and percentile score of telomere length, analysis of variance (ANOVA) was conducted. To assess the association between SNPs and telomere length change, statistical differences between the shorten group and lengthen group were assessed using either the chi-squared or Fisher’s exact test. All the data were analyzed using the IBM SPSS Statistics (version 23.0) by an independent statistician.¹⁹ The statistical tests were two-sided, and statistical significance was defined as a P-value of less than .05.

Results

Descriptive Analyses of Demographic and Clinical Characteristics

This clinical trial began on October 5, 2016, and concluded on June 30, 2020. The participants were recruited over the course of nearly 3.5 years, and 83 cases were screened. Figure 1 depicts the participants’ detailed flow throughout the study. Eighty-three patients were included in the descriptive analyses. The demographic and clinical characteristics are shown in Table 1. These data represent the characteristics of cancer patients who visited an integrative cancer center voluntarily and chose TKM oncotherapy. The participants’ average age was 50.48 years (standard deviation: 9.10), with 80.7% (n = 67) being female. The population was married in 86.7% of cases, and the proportion of highly educated people outnumbered the proportion of less educated people. The vast majority of participants (97.6%) had private insurance, which is higher than Korea’s 2017 private insurance subscription rate of 78.7%.²⁰ Participants included 35 breast cancer survivors, 13 gastric cancer survivors, 12 uterine cancer survivors, 10 colorectal cancer survivors, 10 lung cancer survivors, 2 hepatic cancer survivors, and 1 ovarian cancer survivor. The proportion of breast cancer patients was the highest, at 42.2%. 95.2% of patients had no evidence of disease at the time of enrollment, but 4.8% had disease since diagnosis. Prior to enrollment, 90.1% of patients had undergone surgery, 9.9% had undergone radiotherapy, and 33.3% had undergone chemo/immune/hormonal therapy. At the time of enrollment, 32.5% were already receiving CCTs. In terms of ECOG PS, 34.9% of patients were grade 1 and 65.1% were grade 2. In the case of the EORTC QLQ-C30, the mean values of global health status, physical, role, emotional function, pain, and fatigue items indicated unmet needs based on recent study cut-off scores.^20,21 The mean BDI-2 score was 13.35 (SD, 8.06), indicating a low range of depression status.²² The mean value for STAI-KYZ, which is used to assess state anxiety and trait anxiety, represented moderate anxiety.²³ The telomere length was 7.09 ± 1.00 kilobase pairs (kbp) and the percentile score of telomere length was 50.32 ± 7.59%.

Figure 1.

A flow of participants throughout the study.

Table 1.

Demographic and Clinical Characteristics of Study Participants (N = 83).

Count (%)/mean ± SD, range
Demographic characteristics	All patients
Gender
Male	16 (19.3)
Female	67 (80.7)
Age (year)	50.48 ± 9.10	(33, 74)
Height (cm)	162.08 ± 7.05	(145.3, 184)
Weight (kg)	60.73 ± 12.46	(40.1, 105)
Body Mass Index (kg/m²)	23.07 ± 4.15	(15.9, 37.3)
Residence
Daejeon and Chungcheong Provinces	78 (94.0)
Outside Chungcheong Region	5 (6.0)
Marital status
Single	6 (7.2)
Married	72 (86.7)
Divorced/separated/widowed	5 (6.0)
Occupation
Professional	12 (14.5)
Housewife	25 (30.1)
Other	46 (55.4)
Education
High school graduation or below	32 (38.6)
University graduation or above	51 (61.4)
Income (10 000 KRW/month)
<100	2 (2.4)
100-199	3 (3.6)
200-299	11 (13.3)
300-399	11 (13.3)
400-499	16 (19.3)
500-599	8 (9.6)
600-699	7 (8.4)
700-799	4 (4.8)
≥800	12 (14.5)
Unknown	9 (10.8)
Private insurance
Yes	81 (97.6)
No	2 (2.4)
Cancer insurance
Yes	72 (88.9)
No	9 (11.1)
Expense insurance
Yes	71 (87.7)
No	10 (12.3)
Clinical characteristics	All patients
Primary cancer site
Ovarian	1 (1.2)
Liver	2 (2.4)
Colorectal	10 (12.0)
Gastric	13 (15.7)
Breast	35 (42.2)
Uterine	12 (14.5)
Lung	10 (12.0)
Age at the first diagnosis	49.89 ± 9.19	(32, 74)
Prior surgery
Yes	73 (90.1)
No	8 (9.9)
Prior radiotherapy
Yes	8 (9.9)
No	73 (90.1)
Prior chemo/immuno/hormonal therapy
Yes	27 (33.3)
No	54 (66.7)
Disease status
Alive with disease since diagnosis	4 (4.8)
No evidence of disease	79 (95.2)
Presence of hypertension
Yes	7 (8.4)
No	76 (91.6)
Presence of diabetes mellitus
Yes	2 (2.4)
No	81 (97.6)
Active smoking status
Never smoker	64 (78.0)
Current smoker	1 (1.2)
Former smoker	17 (20.7)
Passive smoking status
No exposure	51 (62.2)
Former exposure	14 (17.1)
Current exposure	17 (20.7)
Alcohol consumption status
Never drinker	30 (36.6)
Current alcohol drinker	14 (17.1)
Past alcohol drinker	38 (46.3)
Number stage at the first diagnosis
0	3 (3.8)
1	45 (57.0)
2	20 (25.3)
3	11 (13.9)
T stage at the first diagnosis
0	3 (3.8)
1	47 (59.5)
2	19 (24.1)
3	9 (11.4)
4	1 (1.3)
N stage at the first diagnosis
0	53 (67.1)
1	19 (24.1)
2	3 (3.8)
3	4 (5.1)
Number stage at the enrollment
0	3 (3.8)
1	45 (57.0)
2	20 (25.3)
3	11 (13.9)
T stage at the enrollment
0	3 (3.8)
1	48 (60.8)
2	18 (22.8)
3	9 (11.4)
4	1 (1.3)
N stage at the enrollment
0	53 (67.1)
1	19 (24.1)
2	3 (3.8)
3	4 (5.1)
Currently receiving cancer treatment
Yes	27 (32.5)
No	56 (67.5)
Duration of TKM oncotherapy (Days)	33.75 ± 106.64	(0, 919)
Telomere length
Telomere length (kbp)	7.09 ± 1.00	(4.24, 8.95)
The percentile score of telomere length (%)	50.32 ± 7.59	(34.71, 69.35)
ECOG PS
1	29 (34.9)
2	54 (65.1)
EORTC-QLQ-C30
GH (Global health status)	48.39 ± 25.25	(0, 100)
PF (Physical functioning)	67.55 ± 22.01	(0, 100)
RF (Role functioning)	62.85 ± 30.61	(0, 100)
EF (Emotional functioning)	72.29 ± 21.67	(0, 100)
CF (Cognitive functioning)	72.69 ± 22.63	(16.67, 100)
SF (Social functioning)	58.03 ± 29.83	(0, 100)
FA (Fatigue)	51.41 ± 26.64	(0, 100)
NV (Nausea and vomiting)	14.06 ± 21.70	(0, 100)
PA (Pain)	36.55 ± 30.07	(0, 100)
DY (Dyspnea)	24.90 ± 27.96	(0, 100)
SL (Insomnia)	50.20 ± 34.29	(0, 100)
AP (Appetite loss)	36.14 ± 31.75	(0, 100)
CO (Constipation)	28.51 ± 30.41	(0, 100)
DI (Diarrhea)	16.87 ± 23.50	(0, 100)
FI (Financial difficulties)	23.29 ± 28.39	(0, 100)
BDI-2
BDI-2	13.35 ± 8.06	(1, 34)
STAI-KYZ
State anxiety	42.08 ± 11.52	(20, 79)
Trait anxiety	45.11 ± 11.78	(21, 83)

Abbreviations: BDI-2, Beck Depression Inventory-II; ECOG PS, the Eastern Cooperative Oncology Group Performance Status; EORTC-QLQ-C30, the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire; kbp, kilobase pair; SD, Standard deviation; STAI-KYZ, Spielberger’s State-Trait Anxiety Inventory form Korean YZ.

Predictor Variables Related to Duration of TKM Oncotherapy

We used a multiple regression analysis with the stepwise method to estimate the relationship between the predictor factors and the days of TKM oncotherapy. When variables were chosen in a stepwise manner, occupation (profession) among the demographic values and T stage at first diagnosis among the clinical values were entered into the regression model with statistical significance of P = .002 and .001, respectively (Table 2). According to the adjusted R2 value, the variation in occupation as profession and T stage at the first diagnosis explains 18.6% of the variation in the days of TKM oncotherapy. This finding suggests that patients with a professional occupation and a more advanced T stage at the time of diagnosis are more willing to receive TKM oncotherapy for a longer period of time.

Table 2.

Multiple Regression Model Examining the Associations between the Duration of TKM Oncotherapy and Prognostic Factors.

	Unstandardized coefficients		Standardized coefficients	t	P-value	Collinearity statistics
	β	SE	β	t	P-value	Tolerance	VIF
(Constant)	−8.218	9.419		−0.872	.386
T stage at the first diagnosis	18.218	5.491	0.345	3.318	.001^†	0.991	1.009
Occupation = Professional	39.940	12.530	0.331	3.188	.002^†	0.991	1.009
Dependent variable: the duration of TKM oncotherapy
Adjusted R² = .186, F = 9.684 (P < .001)

Abbreviations: β, beta-coefficient; SE, standard error; VIF, variance inflation factor.

P < .05. ^†P < .01.

TKM Oncotherapy and Event-free Survival

We found no difference in EFS between the ≥21 days group and <21 days group in Kaplan-Meier analysis and Log Rank test (Figure 2 and Table 3). Of the total of 83 patients, 46 patients completed with the visit follow-up, and 37 patients were lost to follow-up. Because there were a lot of censored data and only one of the patients who completed follow-up reported the occurrence of the event, it was impossible to assess the impact of TKM oncotherapy on EFS of patients.

Figure 2.

Event-free survival between the groups.

Table 3.

Evaluation of the Effect of TKM Oncotherapy on Event-free Survival of Patients.

	Chi-square	df	P-value
Log Rank (Mantel-Cox)	0.900	1	.343
Breslow (Generalized Wilcoxon)	0.900	1	.343
Tarone-Ware	0.900	1	.343

TKM Oncotherapy and Change of Telomere Lengths and QoL Outcomes

The daily attrition rate of telomere length and the changes of EORTC-QLQ-C30, BDI-2, and STAI-KYZ from baseline to each visit (Visit 2, Visit 3, and Visit 4) were compared between the ≥21 days group and the <21 days group (Table 4). In a comparison from baseline to Visit 2, attrition rate of telomere length was calculated as −0.4 ± 0.8 base pair (bp) and −0.4 ± 1.3 bp in the <21 days group and in the ≥21 days group, respectively (P = .903). In a comparison from baseline to Visit 3, it was calculated as −0.3 ± 0.7 and −0.4 ± 0.8 bp, respectively (P = .807). Lastly, in a comparison from baseline to Visit 4, −0.2 ± 0.3 and −0.4 ± 0.3 bp, respectively (P = .094). Although there was no statistical significance, the <21 days group had generally less daily attrition rate of telomere length than the ≥21 days group. And no statistical group differences were found in the changes of EORTC-QLQ-C30, BDI-2, and STAI-KYZ.

Table 4.

The Change of Telomere Length and QoL Outcomes between the Groups.

Variables	Comparison between Visit 2 and Visit 1					Comparison between Visit 3 and Visit 1					Comparison between Visit 4 and Visit 1
	Days < 21		Days ≥ 21		P-value	days < 21		Days ≥ 21		P-value	Days < 21		Days ≥ 21		P-value
	n1	Mean ± SD	n2	Mean ± SD	P-value	n1	Mean ± SD	n2	Mean ± SD	P-value	n1	Mean ± SD	n2	Mean ± SD	P-value
The change of telomere length	41	−0.07 ± 0.15	23	−0.07 ± 0.22	.903	30	−0.11 ± 0.24	22	−0.13 ± 0.28	.916	17	−0.13 ± 0.21	12	−0.27 ± 0.21	.085
ATTD	41	−0.0004 ± 0.0008	23	−0.0004 ± 0.0013	.925	30	−0.0003 ± 0.0007	22	−0.0004 ± 0.0008	.807	17	−0.0002 ± 0.0003	12	−0.0004 ± 0.0003	.094
The change of GH	41	10.77 ± 25.90	26	13.14 ± 29.64	.731	41	17.48 ± 23.85	26	11.54 ± 31.89	.596	41	−11.38 ± 41.24	26	−11.54 ± 24.84	.735
The change of PF	31	16.67 ± 20.41	23	17.39 ± 31.47	.924	31	23.66 ± 28.15	23	26.09 ± 32.11	.891	31	−13.98 ± 33.08	23	−23.19 ± 36.84	.356
The change of RF	17	20.10 ± 16.94	13	25.64 ± 35.92	.614	17	29.41 ± 26.70	13	30.77 ± 37.79	.909	17	−21.57 ± 40.73	13	−17.95 ± 29.24	.788
The change of EF	41	8.13 ± 16.57	26	6.92 ± 23.96	.352	41	−6.78 ± 23.69	26	−8.12 ± 23.00	.778	41	−15.45 ± 33.42	26	−7.69 ± 40.34	.366
The change of CF	31	12.04 ± 17.59	23	9.85 ± 22.01	.374	31	−15.41 ± 25.93	23	−12.08 ± 26.36	.716	31	−24.73 ± 34.39	23	−15.94 ± 34.63	.380
The change of SF	17	18.43 ± 18.64	13	16.92 ± 24.74	.423	17	−23.53±27.75	13	−14.53±30.22	.512	17	−29.41 ± 35.12	13	−12.82 ± 37.36	.163
The change of FA	41	16.26 ± 28.01	26	9.62 ± 32.38	.305	41	−2.85 ± 17.04	26	−14.10 ± 29.32	.265	41	−4.06 ± 24.94	26	−8.97 ± 27.58	.753
The change of NV	31	22.58 ± 30.90	23	15.94 ± 30.35	.452	31	−3.23 ± 15.17	23	−6.52 ± 23.43	.429	31	-7.53±29.45	23	−2.90 ± 19.88	.604
The change of PA	17	32.35 ± 29.74	13	12.82 ± 33.44	.102	17	−1.96 ± 11.61	13	−5.13 ± 12.52	.535	17	3.92 ± 26.04	13	0.00 ± 23.57	.655
The change of DY	41	4.67 ± 17.19	26	4.81 ± 20.97	.798	41	−11.79 ± 26.42	26	−13.46 ± 29.82	.787	41	0.81 ± 32.05	26	2.56±28.17	.899
The change of SL	31	8.06 ± 21.57	23	8.70 ± 22.40	.892	31	−18.28 ± 28.98	23	-19.56±33.20	.620	31	−4.30 ± 22.35	23	−4.35 ± 27.16	.853
The change of AP	17	6.37 ± 15.18	13	12.82 ± 28.59	.622	17	−23.53±23.61	13	−19.23 ± 37.17	.352	17	−5.88 ± 17.62	13	5.13 ± 18.49	.152
The change of CO	41	2.84 ± 20.04	26	1.28 ± 19.39	.840	41	−4.88 ± 20.49	26	−3.85 ± 33.10	.724	41	−9.76 ± 26.08	26	−1.28 ± 19.96	.195
The change of DI	31	4.30 ± 23.16	23	3.62 ± 21.88	.945	31	−4.30 ± 25.45	23	−10.15 ± 33.99	.346	31	−8.60 ± 21.02	23	−10.14 ± 21.16	.501
The change of FI	17	−1.96±17.56	13	0.00 ± 23.57	.756	17	−5.88 ± 17.62	13	−15.38 ± 39.94	.175	17	−7.84 ± 18.74	13	−15.38 ± 22.01	.238
The change of BDI-2	41	−5.39 ± 17.25	26	−2.19 ± 15.72	.666	31	−6.26 ± 20.72	23	−2.48 ± 21.24	.211	17	-7.53±16.66	13	−9.15 ± 23.94	.718
The change of state anxiety	41	−3.39 ± 9.52	26	−0.81 ± 8.87	.375	31	−3.45 ± 10.99	23	−1.22 ± 12.30	.486	17	−6.35 ± 10.01	13	−6.23 ± 12.68	.977
The change of trait anxiety	41	−2.00 ± 8.77	26	−1.38 ± 8.47	.778	31	−2.81 ± 10.39	23	−1.26 ± 9.58	.161	17	−1.18 ± 7.96	13	−2.92 ± 11.62	.749

Abbreviations: AP, appetite loss; ATTD, attrition rate of telomere length (day); BDI-2, Beck Depression Inventory-II; CF, cognitive functioning; CO, constipation; DI, diarrhea; DY, dyspnea; EF, emotional functioning; EORTC-QLQ-C30: the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire; FA, fatigue; FI, financial difficulties; PA, pain; PF, physical functioning; QL: Global health status; RF, role functioning; SD, standard deviation; SE, standard error; SF, social functioning; SL, insomnia; STAI-KYZ: Spielberger’s State-Trait Anxiety Inventory-form Korean YZ.

Adjusted Group Difference in the Daily Attrition Rate of Telomere Lengths by ANCOVA

ANCOVA was conducted to remove the influence of some variables that may affect the telomere length. The research team selected the items including gender, age, marital status, income, BMI, smoking status, drinking status, BDI-2 score, and insomnia item in EORTC-QLQ-C30 based on previous studies.^24-33

As a result of adjusting the aforementioned covariates by ANCOVA, no statistically significant group difference in the daily attrition rate of telomere lengths was detected in comparison from baseline to Visit 2, and comparison between from baseline to Visit 3 (Table 5). However, in comparison from baseline to Visit 4, there was significant difference in the daily attrition rate of telomere lengths between groups, after adjusting covariates (P = .019). That is, if the group differences in gender, age, marital status, income, BMI, smoking status, drinking status, BDI-2 score, and insomnia are adjusted, the daily attrition rate of telomere is significantly higher in long-term TKM oncotherapy group.

Table 5.

Adjusted Group Difference in the Daily Attrition Rate of Telomere Lengths by ANCOVA.

Comparison between Visit 2 and Visit 1
Source	Type III SS	df	MS	F	P-value
Intercept	7.846E−07	1	7.846E−07	0.813	.374
Group by the duration of TKM oncotherapy	5.233E−07	1	5.233E−07	0.542	.467*
Gender	9.947E−09	1	9.947E−09	0.010	.920
Active smoking	5.238E−06	2	2.619E−06	2.714	.082
Passive smoking	5.875E−07	2	2.938E−07	0.304	.740
Alcohol drinking	1.066E−06	2	5.331E−07	0.552	.581
Marital status	1.167E−08	1	1.167E−08	0.012	.913
Age	2.949E−09	1	2.949E−09	0.003	.956
BMI	4.991E−08	1	4.991E−08	0.052	.822
Income	2.984E−06	1	2.984E−06	3.092	.088
BDI-2	1.338E−09	1	1.338E−09	0.001	.971
Insomnia	1.403E−06	1	1.403E−06	1.454	.237
Error	3.088E−05	32	9.650E−07
Adjusted R² = −.066
Comparison between Visit 3 and Visit 1
Source	Type III SS	df	MS	F	P-value
Intercept	1.187E−07	1	1.187E−07	0.277	.603
Group by the duration of TKM oncotherapy	5.452E−07	1	5.452E−07	1.274	.270*
Gender	3.788E−08	1	3.788E−08	0.089	.768
Active smoking	1.500E−06	2	7.502E−07	1.754	.194
Passive smoking	8.108E−08	2	4.054E−08	0.095	.910
Alcohol drinking	2.554E−07	2	1.277E−07	0.299	.745
Marital status	1.276E−08	1	1.276E−08	0.030	.864
Age	5.331E−08	1	5.331E−08	0.125	.727
BMI	4.445E−09	1	4.445E−09	0.010	.920
Income	9.961E−08	1	9.961E−08	0.233	.634
BDI-2	3.374E−07	1	3.374E−07	0.789	.383
Insomnia	8.343E−07	1	8.343E−07	1.950	.175
Error	1.069E−05	25	4.278E−07
Adjusted R² = −0.071
Comparison between Visit 4 and Visit 1
Source	Type III SS	df	MS	F	P-value
Intercept	1.142E−07	1	1.142E−07	1.804	.209
Group by the duration of TKM oncotherapy	4.886E−07	1	4.886E−07	7.723	.019*
Gender	0.000E+00	0	-	-	-
Active Smoking	0.000E+00	0	-	-	-
Passive Smoking	4.767E−07	2	2.383E−07	3.767	.060
Alcohol Drinking	2.999E−07	2	1.500E−07	2.370	.144
Marital Status	0.000E+00	0	-	-	-
Age	1.180E−07	1	1.180E−07	1.866	.202
BMI	1.284E−07	1	1.284E−07	2.029	.185
Income	1.163E−07	1	1.163E−07	1.838	.205
BDI-2	4.631E−08	1	4.631E−08	0.732	.412
Insomnia	8.971E−08	1	8.971E−08	1.418	.261
Error	6.327E−07	10	6.327E−08
Adjusted R² = 0.269

Abbreviations: BDI-2, Beck Depression Inventory-II; BMI, Body Mass Index; TKM, traditional Korean medicine.

P < .05.

The Factors Associated with Change of Telomere Length

Multiple regression analysis of the relationship between the daily attrition rate of telomere lengths and predictor variables

A multiple regression analysis by the stepwise method was carried out for estimating the association between the daily attrition rate of telomere length and predictor variables (Table 6). N stage at the first diagnosis and experience of prior radiotherapy were entered to the regression model with P = .023 and .024. Based on the adjusted R² value, 11.2% of the variation in the daily attrition rate of telomere length can be explained by the variation in N stage at the first diagnosis and experience of prior radiotherapy. That is, the patients who have more advanced N stage at the first diagnosis, and have experienced prior radiotherapy, are likely to have shorter telomere length than the patients in same age group.

Table 6.

Multiple Regression Model Examining the Associations between the Daily Attrition Rate of Telomere Lengths and Prognostic Factors.

	Unstandardized coefficients		Standardized coefficients	t	P-value	Collinearity statistics
	β	SE	β	t	P-value	Tolerance	VIF
(Constant)	−0.223	0.045		−4.932	<.001
N stage at the first diagnosis	.106	0.046	0.259	2.33	.023*	0.999	1.001
Prior radiotherapy	−0.291	0.126	−0.257	−2.313	.024*	0.999	1.001
Dependent variable: the daily attrition rate of telomere lengths
Adjusted R² = 0.112, F = 5.532 (P = .006)

Abbreviations: β, beta-coefficient; SE, standard error; VIF, variance inflation factor.

P < .05.

SNPs associated with the percentile score of telomere length

ANOVA was conducted for estimating the association between the 13 SNPs and percentile score of telomere length (Table 7). Among the 13 SNPs, rs4387287 had statistically significant association with percentile score of telomere length (P = .026). The other 12 SNPs, despite association with telomere length demonstrated in previous studies, were not associated with the percentile score of telomere length in this study

Table 7.

SNPs Associated with the Percentile Score of Telomere Length.

SNP genotype	n	Mean ± SD	P-value (T/F)
rs412658 (C/T)
MM	32	50.88 ± 7.83	.276
Mm	37	49.08 ± 7.35
mm	11	53.10 ± 7.73
rs4387287 (C/A)
MM	57	49.19 ± 7.14	.026*
Mm	21	53.56 ± 8.50
mm	1	(excluded from analysis)
rs10936601 (T/C)
MM	47	50.24 ± 7.90	.862
Mm	27	50.68 ± 7.46
mm	6	49.77 ± 7.36
rs755017 (A/G)
MM	25	48.84 ± 7.27	.494
Mm	34	51.01 ± 7.15
mm	21	51.09 ± 8.83
rs2736100 (A/C)
MM	24	51.70 ± 7.83	.314
Mm	46	49.24 ± 7.73
mm	10	52.24 ± 6.32
rs3027234 (C/T)
MM	74	50.63 ± 7.71	.261
Mm	6	46.97 ± 6.06
mm	0	(excluded from analysis)
rs7675998 (G/A)
MM	52	51.25 ± 7.68	.360
Mm	26	48.74 ± 7.60
mm	2	48.06 ± 5.29
rs11125529 (C/A)
MM	61	50.00 ± 7.84	.155
Mm	17	50.42 ± 6.38
mm	2	60.59 ± 6.00
rs10936599 (T/C)
MM	32	50.88 ± 8.02	.874
Mm	37	50.10 ± 7.19
mm	11	49.68 ± 8.53

Abbreviations: M, major allele; m, minor allele; SNP, single-nucleotide polymorphisms.

P < .05.

SNPs Associated with the Change of Telomere Length

Table 8 represents the relationship with genotype of 13 SNPs and change (shorten/lengthen) of telomere length. The statistical differences between the shorten group and lengthen group were assessed using either the chi-squared or Fisher’s exact test. There were no significant differences in 13 SNPs genotype between the groups both for 6 months and 1 year.

Table 8.

SNPs Associated with Increase and Decrease of Telomere Length. Count (%).

SNP (M/m)	Genotype	Change in telomere length for 6 month			Change in telomere length for 1 year
SNP (M/m)	Genotype	Shorten	Lengthen	P-value	Shorten	Lengthen	P-value
rs412658 (C/T)	MM	20 (46.5)	7 (38.9)	.804	19 (50.0)	5 (38.5)	.908
	Mm	17 (39.5)	8 (44.4)		14 (36.8)	6 (46.2)
	mm	6 (14.0)	3 (16.7)		5 (13.2)	2 (15.4)
rs4387287 (C/A)	MM	27 (64.3)	16 (88.9)	.066	26 (70.3)	11 (84.6)	.469
	Mm	15 (35.7)	2 (11.1)		11 (29.7)	2 (15.4)
	mm	0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)
rs10936601 (T/C)	MM	25 (58.1)	14 (77.8)	.456	25 (65.8)	8 (61.5)	.616
	Mm	14 (32.6)	3 (16.7)		12 (31.6)	4 (30.8)
	mm	4 (9.3)	1 (5.6)		1 (2.6)	1 (7.7)
rs755017 (A/G)	MM	17 (39.5)	3 (16.7)	.233	12 (31.6)	4 (30.8)	1.000
	Mm	18 (41.9)	10 (55.6)		18 (47.4)	6 (46.2)
	mm	8 (18.6)	5 (27.8)		8 (21.1)	3 (23.1)
rs9419958 (C/T)	MM	43 (100.0)	18 (100.0)		38 (100.0)	13 (100.0)
	Mm	0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)
	mm	0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)
rs2736100 (A/C)	MM	12 (27.9)	7 (38.9)	.733	12 (31.6)	5 (38.5)	.908
	Mm	24 (55.8)	9 (50.0)		20 (52.6)	6 (46.2)
	mm	7 (16.3)	2 (11.1)		6 (15.8)	2 (15.4)
rs3027234 (C/T)	MM	38 (88.4)	18 (100.0)	.309	34 (89.5)	13 (100.0)	.561
	Mm	5 (11.6)	0 (0.0)		4 (10.5)	0 (0.0)
	mm	0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)
rs6028466 (G/A)	MM	42 (97.7)	18 (100.0)	1.000	37 (97.4)	13 (100.0)	1.000
	Mm	1 (2.3)	0 (0.0)		1 (2.6)	0 (0.0)
	mm	0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)
rs6772228 (T/A)	MM	43 (100.0)	18 (100.0)		38 (100.0)	13 (100.0)
	Mm	0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)
	mm	0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)
rs7675998 (G/A)	MM	28 (65.1)	10 (55.6)	.514	26 (68.4)	6 (46.2)	.224
	Mm	14 (32.6)	7 (38.9)		10 (26.3)	7 (53.8)
	mm	1 (2.3)	1 (5.6)		2 (5.3)	0 (0.0)
rs9420907 (A/C)	MM	43 (100.0)	18 (100.0)		38 (100.0)	13 (100.0)
	Mm	0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)
	mm	0 (0.0)	0 (0.0)		0 (0.0)	0 (0.0)
rs1112552 (C/A)	MM	33 (76.7)	14 (77.8)	1.000	29 (76.3)	10 (76.9)	1.000
	Mm	9 (20.9)	4 (22.2)		8 (21.1)	3 (23.1)
	mm	1 (2.3)	0 (0.0)		1 (2.6)	0 (0.0)
rs10936599 (T/C)	MM	19 (44.2)	9 (50.0)	.450	20 (52.6)	6 (46.2)	.893
	Mm	16 (37.2)	8 (44.4)		14 (36.8)	6 (46.2)
	mm	8 (18.6)	1 (5.6)		4 (10.5)	1 (7.7)

Abbreviations: M, major allele; m, minor allele; SNP, single-nucleotide polymorphi.

Discussion

This study is the final analysis of a longitudinal observational study on patients with lung, gastric, hepatic, colorectal, breast, uterine, and ovarian cancer over 4 years. A prospective cohort study design was used to determine whether TKM oncotherapy affects cancer patients’ survival, QoL outcomes, and telomere length. This study also looked at factors related to telomere length in cancer patients.

According to an analysis of a total of 785 patients who visited EWCC in 2015, conducted internally by EWCC in early 2016, the top 9 cancers based on the number of patients were breast, gastric, lung, and thyroid, uterine, ovarian, colorectal, pancreatic, and hepatic cancer. According to the most up-to-date cancer statistics in Korea when designing this study, the 5-year relative survival rate of pancreatic and thyroid cancer patients was 9.0% and 99.5%, respectively.³⁴ Because this study was designed as a 4-year longitudinal observation, pancreatic cancer was deemed difficult to follow-up on, while thyroid cancer was deemed less important. As a result, the remaining 7 cancers were chosen for this study. In addition, this prospective cohort study enrolled homogeneous people who did not experience the event under investigation and followed them prospectively based on exposure factors. As a result, participants in this study were chosen within 1 year of their primary cancer diagnosis, had no history of recurrence, and did not correspond to code 7 on the SEER summary stage.

The result of analysis on the correlated factors to the duration of TKM oncotherapy provides interesting suggestions: the patients who have professional occupations, and who have more advanced T stage at the first diagnosis, are willing to receive TKM oncotherapy longer. Prior studies reported that patients who have higher education and higher income were more likely use CAM,^35-37 but not many studies reported the association of professional occupations and CAM use. Although there were previous studies that reported the association between the advanced cancer stage and CAM use,^38,39 these studies reported the association of the onset of CAM use. Relative to most of the previous studies reporting the factors associated with CAM use, this study suggests that the profile of cancer patients can affect the continuity of treatment.

This study evaluated the EFS rather than overall survival because this study was a 4-year longitudinal observational study and assessed the patients within 1 year after diagnosis, who did not have distant metastasis. However, no statistical differences in TKM oncotherapy duration were found in this study. This result could be attributed to a large amount of censored data. Of the 83 patients, 49 completed the visits, while 34 were dropped from the study due to withdrawal of consent or failure to follow-up during the study. Only one patient out of the total number of patients reported the occurrence of the event. Rather than explaining that only one patient experienced the event, it is more reasonable to assume that the majority of patients who experienced events such as metastasis, recurrence, and progression decided to discontinue participation in the study. The inability to assess the effects of TKM oncotherapy on EFS, which was set as the primary goal of this study, is regarded as a major limitation.

There were no statistical differences between the groups in the analysis of changes in QoL outcomes. However, many previous studies have shown that TKM improves the QoL of cancer patients. For example, acupuncture and moxibustion treatment may be used alone or in conjunction with CCT in cases of pharmacological failure to control cancer-related pain, according to some studies.⁴⁰ Acupuncture treatment has been shown to be beneficial for a variety of cancer-related conditions, including cancer-related fatigue,⁴¹ dyspnea,⁴² hot flashes,⁴³ and leukopenia.⁴⁴ Bojungikki-tang, an herbal formulation, has been shown to be effective in reducing fatigue in cancer patients.⁴⁵ The effectiveness of Bojungikki-tang has been also proven in anorexia in clinical observation.⁴⁶ Some medicinal plants such as Astragali radix and Atractylodes rhizome have also been suggested as an effective treatment alleviating cachexia and anorexia.^47,48 In contrast to the aforementioned general evidence, the inability of this study to demonstrate effects of TKM on QoL of cancer patients may be due to the TKM medications defined in this study, HAD-B1, Geonchil-jung, and cultivated wild ginseng pharmacopuncture, being the medications prescribed primarily for anticancer effects.

Telomeres are nucleoprotein structures that are found at the ends of each chromosome arm and serve to keep the genome stable. Telomere length decreases with each cell division, and telomere attrition has been linked to replicative capacity, the aging process, and the pathogenesis of many diseases.⁴⁹ Although many previous studies have reported a link between telomere length and a variety of cancers, the link is uncertain and contradictory across studies. While some studies show that shorter telomeres increase cancer risk,^50,51 others show that longer telomeres increase cancer risk.^52,53 Telomere length has been linked to cancer risk by maintaining genomic stability. Genetic instability has been associated with variety types of cancers, including breast cancer.^54,55 In previous studies to identify factors affecting telomere lengths, being male,²⁴ older,²⁵ unmarried,²⁶ low-income,²⁷ having high BMI,²⁸ drinking and smoking,^29,31 or having insomnia³² or depression³³ have been reported as factors shortening telomere lengths. Recent studies reported that certain SNPs were closely associated with telomere length in the general population, and genetically increased telomere length was related with increased risk of several cancers.^16-18 In general, studies on CAM have rarely evaluated the effect of CAM treatment on telomere length. Only a few in vitro studies were conducted to evaluate the effect of some medicinal plants on telomere length and telomerase activity.^56-63 There was no clinical study that examines the effect of TKM on telomere length in cancer patients.

In the analysis on the daily attrition rate of telomere length between the groups, the daily attrition rate of telomere was higher in the long-term TKM oncotherapy group after adjusting covariates that were able to affect telomere length. This means that the patients who belonged to the long-term TKM oncotherapy group had a larger decline of telomere length compared to the patients in the short-term TKM oncotherapy group. Interpreting this result as cause-and-effect relationship can be a hasty judgment. Generally short telomere length has a significant association with poor prognosis in cancer patients.⁶⁴ When referring to previous research findings showing that people with a poorer prognosis sought more CAM care,⁶⁵ the more appropriate interpretation is that patients with the worse prognosis have received TKM tumor treatment for longer times.

The analysis on the association between the telomere length and prognostic factors showed that the patients who have more advanced N stage at the first diagnosis, and have previously received prior radiotherapy, were likely to have shorter telomere length. The present finding that telomere shortening is associated with more advanced N stage, which means more metastases in lymph node, is in agreement with the results of some of previous studies.^66,67 These results suggest that telomere length may reflect the prognosis of cancer patients. The other finding that telomere shortening is associated with prior radiotherapy, can be explained by several studies. Radiation can cause the telomere length attrition of somatic cells,^68,69 and radiotherapy can affect not only localized cancer lesions but also normal tissue surrounding the cancer lesion.⁷⁰ A study reported increased chromosomal abnormality in circulating lymphocytes after radiotherapy for a localized cancer.⁷¹ Thus, it can be hypothesized that some genomic regions, including telomeres, in circulating leukocytes may be affected by radiotherapy. However, the results of this study were contrary to one clinical study that reported that the telomere length and telomere length distribution of peripheral leukocytes were not affected after radiotherapy.⁷²

Previous data found that some SNPs, including rs11125529, rs10936599, rs10936601, rs6772228, rs7675998, rs2736100, rs4387287, rs9419958, rs9420907, rs3027234, rs412658, rs6028466, and rs755017 are associated with telomere length.^16-18 This study was able to confirm an association between telomere length and rs4387287 when evaluating associations between the SNP genotype and the percentile score of telomere length. The rs4387287 locates in OBFC1 gene, which codes a subunit protein of a telomere-associated complex including C17ORF68 and TEN1.⁷³ OBFC1 gene is also one of the components of an alpha accessory factor which promotes the activity of DNA polymerase alpha primase that initiates DNA replication. Importantly, genetic mutations in OBFC1 locus are involved in telomere biology²⁵ and confer a risk of cancers including thyroid cancer, pancreatic cancer, epithelial ovarian cancer, uterine leiomyoma, and melanoma.^18,74-78 This result adds new lines of evidence highlighting the role of OBFC1 in change of telomere length.

This study has several limitations. First, the effects of TKM oncotherapy on EFS, which was the primary objective of this study, were unable to be assessed because of the number of censored data. Second, the effectiveness of TKM oncotherapy on QoL outcomes and telomere length could not be proved. In addition, only 3 kinds of interventions were defined as TKM oncotherapy, so the effects of other TKM interventions such as herbal medicine, acupuncture, and moxibustion could not be evaluated in this study. Lastly, it is difficult to expect accurate results of the analysis applying SNP genotyping due to the insufficient sample size. Future study with a larger sample size may have greater statistical power and present more accurate results.

This study, on the other hand, has several obvious advantages. First, this is the first prospective cohort study in Korea that reflects the current clinical states of TKM oncotherapy. Through long-term follow-up observation, this prospective cohort study established a disease registry and evaluated the effects of TKM oncotherapy. Furthermore, this is the first study to measure telomere length in cancer patients who received TKM treatment. Furthermore, this study differs from other studies on the effects of complementary and alternative medicine (CAM) in cancer patients in that it used a genetic analysis based on a microarray. This study’s findings show that T stage at first diagnosis and professional occupation are correlated factors associated with the duration of TKM oncotherapy. Another finding from this study is that N stage at first diagnosis, as well as prior radiotherapy experience, can affect telomere length in cancer patients. These findings point to factors that can predict patient adherence to treatment as well as prognosis in relation to telomere length. And the results of SNP genotyping add to the evidence that the OBFC1 gene plays a role in telomere length change. To demonstrate the more accurate effects of TKM oncotherapy on survival, QoL, and telomere length, a longer-term observational study with a larger sample size is required. This study is expected to secure the foundation for TKM oncotherapy and integrative cancer treatment, as well as serve as a precursor for future research.

Conclusions

This prospective cohort study has obvious value as the first prospective cohort study evaluating effects of TKM oncotherapy and investigating factors associated with telomere length. Although this study was unable to demonstrate the effects of TKM oncotherapy on survival, and changes of QoL, the result of this study suggested that demographic and clinical factors of patients can affect the continuity of TKM oncotherapy. And this study presented the 2 medical features and 1 SNP genotype as correlated factors for telomere length. To evaluate the effects of TKM oncotherapy on survival and QoL of cancer patients, further long-term observational study with larger sample size is needed.

Supplemental Material

sj-docx-1-ict-10.1177_15347354231154267 – Supplemental material for Effect of Traditional Korean Medicine Oncotherapy on the Survival, Quality of Life, and Telomere Length: A Prospective Cohort Study

Supplemental material, sj-docx-1-ict-10.1177_15347354231154267 for Effect of Traditional Korean Medicine Oncotherapy on the Survival, Quality of Life, and Telomere Length: A Prospective Cohort Study by Su-Jung Ha, Eunbin Kwag, Soodam Kim, Ji-Hye Park, So-Jung Park and Hwa-Seung Yoo in Integrative Cancer Therapies

Footnotes

Data Availability Statement

The data used to support the findings of this study are included within the article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI15C0006, HI19C1046).

ORCID iD

Hwa-Seung Yoo

Supplemental Material

Supplemental material for this article is available online.

References

Hyodo

Amano

Eguchi

, et al. Nationwide survey on complementary and alternative medicine in cancer patients in Japan. J Clin Oncol. 2005;23:2645-2654.

Cassileth

Vickers

AJ.

High prevalence of complementary and alternative medicine use among cancer patients: implications for research and clinical care. J Clin Oncol. 2005;23:2590-2592.

Kim

Park

Hahm

Lim

Choi

KS.

Initiation and discontinuation of complementary therapy among cancer patients. J Clin Oncol. 2007;25:5267-5274.

McDermott

Blough

Fedorenko

, et al. Complementary and alternative medicine use among newly diagnosed prostate cancer patients. Support Care Cancer. 2010;20:65-73.

Kessel

Lettner

Kessel

, et al. Use of complementary and alternative medicine (CAM) as part of the oncological treatment: survey about patients’ attitude towards CAM in a university-based Oncology Center in Germany. PLoS One. 2016;11:e0165801.

Zaid

Silbermann

Amash

Gincel

Abdel-Sattar

Sarikahya

NB.

Medicinal plants and natural active compounds for cancer chemoprevention/chemotherapy. Evid Based Complement Alternat Med. 2017;2017:1-2.

Buckner

Lafrenie

Dénommée

Caswell

Want

DA.

Complementary and alternative medicine use in patients before and after a cancer diagnosis. Curr Oncol. 2018;25:e275-e281.

Lee

Kim

Min

Lee

Cho

SH.

Traditional herbal medicine for cancer pain: a systematic review and meta-analysis. Complement Ther Med. 2015;23:265-274.

Yoon

Jeong

Kim

Aggarwal

BB.

Cancer prevention and therapy: integrating traditional Korean medicine into modern cancer care. Integr Cancer Ther. 2014;13:310-331.

10.

Balkwill

Capasso

Hagemann

The tumor microenvironment at a glance. J Cell Sci. 2012;125:5591-5596.

11.

Hanahan

Weinberg

RA.

Hallmarks of cancer: the next generation. Cell. 2011;144:646-674.

12.

Yoo

Jeon

Kim

Cho

Lee

YW.

Wheel balanced cancer therapy for longer than 21 days can have a positive effect on the survival of patients with stage IV cancer. J Pharmacopuncture. 2015;18:19-31.

13.

Kim

Cho

Yoo

HS.

Survival analysis of advanced non-small cell lung cancer patients treated by using wheel balance cancer therapy. Integr Cancer Ther. 2016;15:467-477.

14.

Bae

Kim

Choi

Kim

Yoo

HS.

The effectiveness of anticancer traditional Korean medicine treatment on the survival in patients with lung, breast, gastric, colorectal, hepatic, uterine, or ovarian cancer: a prospective cohort study protocol. Medicine. 2018;97:e12444.

15.

O’Callaghan

Fenech

A quantitative PCR method for measuring absolute telomere length. Biol Proced Online. 2011;13:3. doi:10.1186/1480-9222-13-3

16.

Mangino

Hwang

Spector

, et al. Genome-wide meta-analysis points to CTC1 and ZNF676 as genes regulating telomere homeostasis in humans. Hum Mol Genet. 2012;21:5385-5394.

17.

Telomeres Mendelian Randomization Collaboration; Haycock

Burgess

, et al. Association between telomere length and risk of cancer and non-neoplastic diseases: a mendelian randomization study. JAMA Oncol. 2017;3:636-651.

18.

Campa

Matarazzi

Pezzilli

, et al. Genetic determinants of telomere length and risk of pancreatic cancer: a PANDoRA study. Int J Cancer. 2017;17:S22-1283.

19.

An overview of IBM SPSS statistics. In: IBM SPSS Statistics 23 Step by Step. Routledge. 2022:15-21.

20.

Korea Health Panel Survey. A report on the Korea health panel survey of 2017. Korea Institute for Health and Social Affairs. 2019.

21.

Snyder

Blackford

Sussman

, et al. Identifying changes in scores on the EORTC-QLQ-C30 representing a change in patients’ supportive care needs. Qual Life Res. 2015;24:1207-1216.

22.

Beck

Steer

Brown

GK.

Beck Depression Inventory. 2nd ed. The Psychological Corporation; 1996.

23.

Spielberger

CD.

State-Trait Anxiety Inventory. The Corsini Encyclopedia of Psychology. John Wiley & Sons, Inc.; 2010.

24.

Gardner

Bann

Wiley

, et al. Gender and telomere length: systematic review and meta-analysis. Exp Gerontol. 2014;51:15-27.

25.

Rode

Nordestgaard

Bojesen

SE.

Peripheral blood leukocyte telomere length and mortality among 64,637 individuals from the general population. J Natl Cancer Inst. 2015;107:djv074.

26.

Mainous

3rd Everett

Diaz

, et al. Leukocyte telomere length and marital status among middle-aged adults. Age Ageing. 2011;40:73-78.

27.

Yen

Lung

FW.

Older adults with higher income or marriage have longer telomeres. Age Ageing. 2013;42:234-239.

28.

Müezzinler

Zaineddin

Brenner

Body mass index and leukocyte telomere length in adults: a systematic review and meta-analysis. Obes Rev. 2014;15:192-201.

29.

Astuti

Wardhana

Watkins

Wulaningsih

; PILAR Research Network. Cigarette smoking and telomere length: a systematic review of 84 studies and meta-analysis. Environ Res. 2017;158:480-489.

30.

Johnman

McGlynn

Mackay

Shiels

Pell

JP.

Association between exposure to second-hand smoke and telomere length: cross-sectional study of 1303 non-smokers. Int J Epidemiol. 2017;46:1978-1984.

31.

Wang

Kim

Baik

Associations of alcohol consumption and alcohol flush reaction with leukocyte telomere length in Korean adults. Nutr Res Pract. 2017;11:334-339.

32.

Jackowska

Hamer

Carvalho

Erusalimsky

Butcher

Steptoe

Short sleep duration is associated with shorter telomere length in healthy men: findings from the Whitehall II cohort study. PLoS One. 2012;7:e47292.

33.

Liu

Wei

Forsell

Lavebratt

Stress, depressive status and telomere length: does social interaction and coping strategy play a mediating role?

J Affect Disord. 2019;29:S848-S849.

34.

South Korea Central Cancer Registry. Annual Report of Cancer Statistics in Korea in 2014. National Cancer Center; 2016.

35.

Wode

Henriksson

Sharp

Stoltenberg

Hök Nordberg

Cancer patients’ use of complementary and alternative medicine in Sweden: a cross-sectional study. BMC Complement Altern Med. 2019;19:62.

36.

Keene

Heslop

Sabesan

Glass

BD.

Complementary and alternative medicine use in cancer: a systematic review. Complement Ther Clin Pract. 2019;35:33-47.

37.

Buyukhatipoglu

Balakan

Suner

, et al. Depression, anxiety and quality of life through the use of complementary and alternative medicine among breast cancer patients in Turkey. J Cancer Res Ther. 2014;10:962-966.

38.

Gerson-Cwilich

Serrano-Olvera

Villalobos-Prieto

Complementary and alternative medicine (CAM) in Mexican patients with cancer. Clin Transl Oncol. 2006;8:200-207.

39.

Jang

Kang

Kim

DU.

Complementary and alternative medicine use and its association with emotional status and quality of life in patients with a solid tumor: a cross-sectional study. J Altern Complement Med. 2017;23:362-369.

40.

Alimi

Rubino

Pichard-Léandri

Fermand-Brulé

Dubreuil-Lemaire

Hill

Analgesic effect of auricular acupuncture for cancer pain: a randomized, blinded, controlled trial. J Clin Oncol. 2003;21:4120-4126.

41.

Molassiotis

Bardy

Finnegan-John

, et al. Acupuncture for cancer-related fatigue in patients with breast cancer: a pragmatic randomized controlled trial. J Clin Oncol. 2012;30:4470-4476.

42.

Filshie

Penn

Ashley

Davis

CL.

Acupuncture for the relief of cancer-related breathlessness. Palliat Med. 1996;10:145-150.

43.

Hervik

Mjåland

Acupuncture for the treatment of hot flashes in breast cancer patients, a randomized, controlled trial. Breast Cancer Res Treat. 2009;116:311-316.

44.

Dean-Clower

, et al. Acupuncture for chemotherapy-induced leukopenia: exploratory meta-analysis of randomized controlled trials. J Soc Integr Oncol. 2007;5:1-10.

45.

Jeong

Ryu

Kim

Park

Choi

Yoon

SW.

Bojungikki-tang for cancer-related fatigue: a pilot randomized clinical trial. Integr Cancer Ther. 2010;9:331-338.

46.

Cheng

WKW

. Review of the effects and safety of traditional Chinese medicine in the treatment of Cancer Cachexia. Asia Pac J Oncol Nurs. 2021;8:471-486.

47.

Lee

JJ.

A phase II study of an herbal decoction that includes astragali radix for cancer-associated anorexia in patients with advanced cancer. Integr Cancer Ther. 2010;9:24-31.

48.

Liu

Jia

Dong

Wang

Qiu

A randomized pilot study of atractylenolide I on gastric cancer cachexia patients. Evid Based Complement Alternat Med. 2008;5:337-344.

49.

Turner

Vasu

Griffin

DK.

Telomere biology and human phenotype. Cells. 2019;8:73.

50.

Wentzensen

Mirabello

Pfeiffer

Savage

SA.

The association of telomere length and cancer: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2011;20:1238-1250.

51.

Anic

Sondak

Messina

, et al. Telomere length and risk of melanoma, squamous cell carcinoma, and basal cell carcinoma. Cancer Epidemiol. 2013;37:434-439.

52.

Sanchez-Espiridion

Chen

Chang

, et al. Telomere length in peripheral blood leukocytes and lung cancer risk: a large case-control study in Caucasians. Cancer Res. 2014;74:2476-2486.

53.

Pellatt

Wolff

Torres-Mejia

, et al. Telomere length, telomere-related genes, and breast cancer risk: the breast cancer health disparities study. Genes Chromosomes Cancer. 2013;52:595-609.

54.

Miki

[Cellular functions of BRCA genes - from basic science to therapeutics]. Gan To Kagaku Ryoho. 2012;39:498-501.

55.

Sens-Abuázar

Ferreira

ENE

Osório

, et al. Down-regulation of ANAPC13 and CLTCL1: early events in the progression of preinvasive ductal carcinoma of the breast. Transl Oncol. 2012;5:113-123.

56.

Yokoyama

Noguchi

Nakao

Pater

Iwasaka

The tea polyphenol, (-)-epigallocatechin gallate effects on growth, apoptosis, and telomerase activity in cervical cell lines. Gynecol Oncol. 2004;92:197-204.

57.

Gunduz

Biray

Kosova

, et al. Evaluation of Manisa propolis effect on leukemia cell line by telomerase activity. Leuk Res. 2005;29:1343-1346.

58.

Wang

Fang

MY.

Effect of ginseng saponin, arsenic trioxide, beta-elemene combined with CTX on telomere-telomerase system in K562 cell line. Zhongguo Shi Yan Xue Ye Xue Za Zhi. 2006;14:1089-1095.

59.

Park

Yoo

Jin

, et al. Induction of apoptosis and inhibition of telomerase activity in human lung carcinoma cells by the water extract of Cordyceps militaris. Food Chem Toxicol. 2009;47:1667-1675.

60.

Symonds

Konczak

Fenech

The Australian fruit Illawarra plum (Podocarpus elatus endl., Podocarpaceae) inhibits telomerase, increases histone deacetylase activity and decreases proliferation of colon cancer cells. Br J Nutr. 2013;109:2117-2125.

61.

Lei

Chen

Zhao

Yang

Resveratrol attenuates senescence of adipose-derived mesenchymal stem cells and restores their paracrine effects on promoting insulin secretion of INS-1 cells through pim-1. Eur Rev Med Pharmacol Sci. 2016;20:1203-1213.

62.

Merghoub

El Btaouri

Benbacer

, et al. Inula viscosa extracts induces telomere shortening and apoptosis in cancer cells and overcome drug resistance. Nutr Cancer. 2016;68:131-143.

63.

Qin

Jin

, et al. Tianshengyuan-1 (TSY-1) regulates cellular telomerase activity by methylation of TERT promoter. Oncotarget. 2017;8:7977-7988.

64.

Zhang

Chen

Zhou

Wang

Hou

The association between telomere length and cancer prognosis: evidence from a meta-analysis. PLoS One. 2015;10:e0133174.

65.

Kristoffersen

Fønnebø

Norheim

AJ.

Do cancer patients with a poor prognosis use complementary and alternative medicine more often than others?

J Altern Complement Med. 2009;15:35-40.

66.

Kammori

Sugishita

Okamoto

, et al. Telomere shortening in breast cancer correlates with the pathological features of tumor progression. Oncol Rep. 2015;34:627-632.

67.

Fordyce

Heaphy

Bisoffi

, et al. Telomere content correlates with stage and prognosis in breast cancer. Breast Cancer Res Treat. 2006;99:193-202.

68.

Sgura

Antoccia

Berardinelli

, et al. Telomere length in mammalian cells exposed to low- and high-LET radiations. Radiat Prot Dosimetry. 2006;122:176-179.

69.

Belloni

Latini

Palitti

Radiation-induced bystander effect in healthy G0 human lymphocytes: Biological and clinical significance. Mutat Res. 2011;713:32-38.

70.

Zelefsky

Fuks

Happersett

, et al. Clinical experience with intensity modulated radiation therapy (IMRT) in prostate cancer. Radiother Oncol. 2000;55:241-249.

71.

Hartel

Nikoghosyan

Durante

, et al. Chromosomal aberrations in peripheral blood lymphocytes of prostate cancer patients treated with IMRT and carbon ions. Radiother Oncol. 2010;95:73-78.

72.

Maeda

Nakamura

Atsumi

Hirakawa

Ueda

Makino

Radiation-associated changes in the length of telomeres in peripheral leukocytes from inpatients with cancer. Int J Radiat Biol. 2013;89:106-109.

73.

Miyake

Nakamura

Nabetani

, et al. RPA-like mammalian Ctc1-stn1-ten1 complex binds to single-stranded DNA and protects telomeres independently of the Pot1 pathway. Mol Cell. 2009;36:193-206.

74.

Phelan

Kuchenbaecker

Tyrer

, et al. Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat Genet. 2017;49:680-691.

75.

Duffy

Zhu

, et al. Novel pleiotropic risk loci for melanoma and nevus density implicate multiple biological pathways. Nat Commun. 2018;9:4774.

76.

Gudmundsson

Thorleifsson

Sigurdsson

, et al. A genome-wide association study yields five novel thyroid cancer risk loci. Nat Commun. 2017;8:14517.

77.

Välimäki

Kuisma

Pasanen

, et al. Genetic predisposition to uterine leiomyoma is determined by loci for genitourinary development and genome stability. eLife. 2018;7:e37110.

78.

Rafnar

Gunnarsson

Stefansson

, et al. Variants associating with uterine leiomyoma highlight genetic background shared by various cancers and hormone-related traits. Nat Commun. 2018;9:3636.

Supplementary Material

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Effect of Traditional Korean Medicine Oncotherapy on the Survival,Quality of Life,and Telomere Length: A Prospective Cohort Study

Abstract

Keywords

Introduction

Methods

Objectives

Study Design and Participants

The Definition of Groups by the TKM Oncotherapy duration

Variables

Measurement of Telomere Length

SNP Genotyping Based on Microarray

Selection of SNPs

Genomic DNA extraction from buccal swabs

Genotyping based on microarray

Statistical Analysis

Results

Descriptive Analyses of Demographic and Clinical Characteristics

Predictor Variables Related to Duration of TKM Oncotherapy

TKM Oncotherapy and Event-free Survival

TKM Oncotherapy and Change of Telomere Lengths and QoL Outcomes

Adjusted Group Difference in the Daily Attrition Rate of Telomere Lengths by ANCOVA

The Factors Associated with Change of Telomere Length

Multiple regression analysis of the relationship between the daily attrition rate of telomere lengths and predictor variables

SNPs associated with the percentile score of telomere length

SNPs Associated with the Change of Telomere Length

Discussion

Conclusions

Supplemental Material

sj-docx-1-ict-10.1177_15347354231154267 – Supplemental material for Effect of Traditional Korean Medicine Oncotherapy on the Survival, Quality of Life, and Telomere Length: A Prospective Cohort Study

Footnotes

Data Availability Statement

Declaration of Conflicting Interests

Funding

ORCID iD

Supplemental Material

References

Supplementary Material