MTHFR rs1801131 GG Genotype Combined with Serum LDH Level Predicts Lung Adenocarcinoma Susceptibility and Poor Prognosis in Guangxi Zhuang Population

Abstract

Introduction

Methylenetetrahydrofolate reductase (MTHFR) polymorphisms modulate folate metabolism and carcinogenesis, yet their role in lung adenocarcinoma (LUAD) susceptibility and prognosis, particularly in underrepresented populations, remains unclear.

Methods

We conducted a retrospective case-control study of 306 lung cancer (LC) patients (predominantly LUAD) and 156 healthy controls of Guangxi Zhuang ethnicity, with prognostic follow-up of the cases. Genotyping (rs1801131/rs1801133), serum lactate dehydrogenase (LDH) levels, and tumor MTHFR protein expression were analyzed. Associations with susceptibility (logistic regression), survival (Cox models/Kaplan-Meier), and molecular mechanisms were evaluated.

Results

The MTHFR rs1801131 GG genotype conferred increased LUAD susceptibility (adjusted OR = 2.14, 95% CI: 0.99–4.65, P = 0.054) and predicted poor overall survival (OS, P = 0.043) and progression-free interval (PFI, P = 0.023). Among all LC subtypes, the GG genotype was also associated with worse prognosis (P = 0.033). Patients with both GG genotype and elevated LDH (eLDH) had higher mortality OS risk than those with TT or GG genotype with eLDH in the LC and LUAD cohorts (P = 0.034 and 0.042). Mechanistically, the GG genotype reduced tumor MTHFR protein expression (P < 0.05). The GG (rs1801131-G/rs1801133-G) haplotype elevated LUAD risk by 1.53-fold (P = 0.022).

Conclusion

In the Guangxi Zhuang cohort, the MTHFR rs1801131 GG genotype may represent a population-specific marker associated with LUAD susceptibility and poorer prognosis. Its combination with elevated LDH may provide supplementary information for risk stratification, although further validation in larger multicenter and multi-ethnic cohorts is required.

Plain Language Summary

Background: Lung cancer, particularly a type called lung adenocarcinoma, is relatively common among the Zhuang ethnic population in Guangxi, China, including many non-smokers. We wanted to understand if, beyond smoking, other factors influence the risk of developing this cancer and survival outcomes. This study focused on a gene called MTHFR, which helps process an important nutrient (folate) in the body, and a common blood test marker known as LDH. Methods: We conducted a study within the Zhuang population in Guangxi, comparing 306 lung cancer patients with 156 healthy individuals. We tested everyone’s MTHFR gene type and blood LDH levels, and analyzed how these factors related to cancer risk and survival time. Results: We found that people carrying a specific type of the MTHFR gene (called the GG genotype) had a higher risk of developing lung adenocarcinoma and, after treatment, shorter survival times and a greater likelihood of the disease progressing. More importantly, if a person had both the GG genotype and a higher LDH level, their risk of death was the highest among all patients. This may be because the GG genotype leads to reduced MTHFR protein in the body, which affects normal cell metabolism. Conclusion: For the Zhuang population in Guangxi, testing for the MTHFR gene (rs1801131) type combined with a blood LDH test can help identify those individuals at the highest risk for lung cancer and with the poorest prognosis. This provides a simple and practical method for implementing closer health monitoring and follow-up for this high-risk group, contributing to more precise health management.

Keywords

MTHFR polymorphism lactate dehydrogenase lung adenocarcinoma susceptibility prognosis

Introduction

Lung adenocarcinoma (LUAD), the predominant histological subtype of lung cancer, remains the leading cause of global cancer mortality, with 5-year survival rates persistently below 20%.¹ In China, LUAD accounts for over 40% of all lung cancer cases, contributing significantly to the national burden of ∼870,000 new cases and ∼760,000 deaths annually.² While smoking is a major risk factor, LUAD frequently develops in non-smokers (particularly in Asian populations), underscoring the crucial role of germline genetic susceptibility.^3,4 Furthermore, despite refinements in TNM staging and identification of targetable drivers (e.g., EGFR, ALK), approximately 35%-40% of patients experience unanticipated therapeutic failure, indicating there is unexplained clinical heterogeneity.^5,6 This highlights an urgent need for novel biomarkers that elucidate subtype-specific mechanisms and refine risk stratification.

Dysregulated epigenetic control, particularly aberrant DNA methylation, and compromised genomic integrity are established as central drivers of lung cancer pathogenesis and disparate outcomes.⁷ DNA methylation, essential for genomic stability and transcriptional regulation, is dependent on folate-mediated one-carbon metabolism for the supply of methyl groups via S-adenosylmethionine (SAM).⁸ Methylenetetrahydrofolate reductase (MTHFR), a pivotal enzyme in this pathway, catalyzes the irreversible conversion of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate, directing folate pools towards homocysteine remethylation and SAM synthesis.⁹ Functional polymorphisms in MTHFR (notably C677T [rs1801133] and A1298C [rs1801131]) significantly reduce enzymatic activity.¹⁰ This impairment is mechanistically linked to genome-wide DNA hypomethylation which is a well-established precursor to chromosomal instability, oncogene activation, and tumor suppressor silencing due to altered cellular folate distribution and reduced SAM availability.^11-13 Accumulating evidence suggests that these mechanisms contribute to the pathogenesis of LUAD. Nevertheless, epidemiological studies on the association between MTHFR variants and LUAD susceptibility or prognosis remain inconclusive, particularly among underrepresented populations such as the Zhuang ethnic group, the largest minority group in China, with a distinct genetic background.^14-16 Existing studies suffer from ethnic bias, focusing predominantly on European or Han Chinese populations while neglecting the Zhuang ethnic group. There is a mechanistic gap: no prior LUAD research integrates germline MTHFR genotypes (e.g., rs1801131/rs1801133) with functional tumor phenotypes and key metabolic biomarkers like serum lactate dehydrogenase (LDH). Moreover, prognostic synergy between MTHFR variants and LDH which is a well-established marker of tumor glycolysis and LUAD progression,^17,18 remains clinically unexplored, limiting biomarker-driven risk stratification.

Here, we address these questions through an integrated analysis of 306 Zhuang patients with lung cancer. We hypothesize that the MTHFR rs1801131 GG genotype contributes to LUAD susceptibility and adverse prognosis via reduced protein expression, with this effect potentially strengthened by elevated LDH levels. Our study integrates genotyping, tumor MTHFR protein quantification, and serum LDH measurements to investigate whether the rs1801131 GG genotype may serve as a population-specific marker for risk stratification in this underrepresented population.

Materials and Methods

Study Population and Ethics

This was a retrospective, single-center, hospital-based case-control study, with additional prognostic follow-up analyses among cases. The reporting of this study conforms to the STROBE guidelines.¹⁹ A total of 700 consecutive patients with clinically suspected lung cancer (LC) were initially screened at the First Affiliated Hospital of Guilin Medical University (July 2021–March 2024). All lung cancer diagnoses adhered to the Chinese Society of Clinical Oncology (CSCO) Clinical Guidelines for Lung Cancer (2022) and were pathologically verified according to the WHO Classification of Thoracic Tumours (5th edition, 2021). Exclusion criteria included: (1) history of autoimmune disorders; (2) acute hepatic/renal dysfunction; (3) pregnancy; (4) familial relationships within the cohort; (5) histological classification failure (e.g., adenosquamous carcinoma or unclassifiable NSCLC); (6) non-primary lung malignancies; (7) genotyping failed; (8) Other groups of non-Zhuang nationality. The final analytical cohort comprised 306 histologically confirmed LC cases classified: 160 lung adenocarcinoma (LUAD) patients, 77 lung squamous cell carcinoma (LUSC) patients and 63 small cell lung cancer (SCLC) patients. A total of 156 age- and sex-frequency-matched Zhuang healthy controls (HC) were recruited from the health screening program of the same center. The study protocol was approved by the Institutional Review Board of the First Affiliated Hospital of Guilin Medical University (approval No. 2023QTLL-36; approved on December 15, 2023). Written informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki of 1975, as revised in 2024. All patient data were de-identified before analysis. Overall Survival (OS): defined as the time from diagnosis to death from any cause. Progression-Free Interval (PFI): defined as the duration from treatment initiation to radiological/clinical disease progression or cancer-related re-hospitalization or death from any cause (whichever occurred first). The median follow-up duration was estimated using the reverse Kaplan–Meier method.

SNP Selection and Genotyping

Functional polymorphisms were prioritized based on: (1) functional genomic regions: all SNPs localize to key regulatory or coding regions: MTHFR rs1801131 (T>G, p.E429A) and rs1801133 (G>A, p.A222V): exonic missense variants (alter enzymatic activity) (2) population frequency: minor allele frequency (MAF) >5% in East Asians (NCBI SNP database). (3) pathogenic evidence: established roles in lung carcinogenesis from the literature.

Genotyping was performed using the SNPscan™ platform with locus-specific probes (three per SNP: two allele-specific 5′-probes and one common 3′-probe) (Supplemental Table S1). DNA samples (30-50 ng/μL verified by agarose electrophoresis) underwent thermal lysis (4 μL DNA + 2.5 μL buffer at 98°C/5 min), followed by ligation reactions (10 μL lysate + 1 μL probe mix + 0.5 μL ligase; 94°C/1 min → 58°C/4 h → 72°C/2 min). Multiplex PCR amplified ligation products (1 μL) with touchdown cycling: 95°C/2 min; 9 cycles of 94°C/20 s → 62-57°C (-0.5°C/cycle)/40 s → 72°C/1.5 min; 25 cycles of 94°C/20 s → 57°C/40 s → 72°C/1.5 min. PCR products (1:10 diluted) were electrophoresed on ABI 3730xl with Liz600™ size standard after denaturation (95°C/5 min). Genotypes were called using GeneMapper 4.1, with quality controls including interplate negatives (n=3/96-well), 10% duplicate replicates (>99% concordance).

Immunohistochemical (IHC) Validation

Stratified analysis of representative formalin-fixed paraffin-embedded lung cancer tissues across histological subtypes was performed based on genotype. Tissue sections (4 μm) underwent antigen retrieval in citrate buffer (pH 6.0), followed by incubation with primary antibody against MTHFR (UpingBio, #YP-Ab-04024). Immunoreactivity was visualized using DAB chromogen and independently scored by two board-certified pathologists blinded to genotype. Staining intensity was quantified as follows: 0 (no staining), 1+ (faint/discernible staining in ≤10% cells), 2+ (moderate staining in >10% cells), and 3+ (strong/diffuse staining in >10% cells). Integrated optical density (IOD) per unit area was determined via digital image analysis (ImageJ v1.53).

Baseline Assessment and Laboratory Procedures

Hepatic and renal function indices for all participants were quantified in our ISO 15189-accredited laboratory (Certification No. ML00036). All assays were performed in strict compliance with manufacturer protocols for reagent kits and instrument operation manuals using Roche Cobas E701/E801 immunoassay analyzers. Based on the established clinical threshold,¹⁹ values ≥180 U/L were classified as elevated lactate dehydrogenase (eLDH) and values <180 U/L as non-elevated LDH (neLDH). Blood specimens were collected contemporaneously with SNP genotyping to maintain batch-to-batch consistency.

Statistical Analysis

Statistical analyses were conducted using IBM SPSS 27.0 and R 4.2.1. Categorical data are presented as frequencies (n) or percentages [n (%)]. Pearson’s chi-square test assessed group differences in categorical variables, while the chi-square goodness-of-fit test evaluated Hardy-Weinberg equilibrium (HWE). Continuous data normality was assessed by the Kolmogorov-Smirnov test. Normally distributed data are expressed as mean ± standard deviation (SD) and analyzed using independent samples t-test (two groups) or one-way ANOVA (multiple groups). Non-normally distributed data are presented as median (interquartile range) [M (P25-P75)] and analyzed with the Mann-Whitney U test (two groups) or Kruskal-Wallis test (multiple groups). Binary logistic regression evaluated associations between biochemical markers, genetic SNPs, and LC susceptibility, reporting odds ratios (OR) with 95% confidence intervals (CI). Survival analyses were performed in R (survival package v3.3.1). Univariate Cox proportional hazards regression was first used to screen candidate variables, and only covariates with P < 0.10 in the corresponding univariate analysis were entered into the multivariate Cox regression model. This prespecified strategy was applied to reduce model complexity and limit potential overfitting. Kaplan–Meier survival curves were generated and visualized using survminer (v0.4.9) and ggplot2 (v3.3.6). Haplotype analysis was performed using SHEsis²⁰ (https://analysis.bio-x.cn/myAnalysis.php). Protein expression differences across genotypes were analyzed by ANOVA followed by Tukey’s post-hoc test. Statistical significance was defined as P < 0.05. No formal sample size calculation was performed; the sample size was determined by the number of eligible participants available during the study period.

Results

Comparative Analysis of Clinical and Biochemical Profiles

Table 1 compares clinical parameters between healthy controls (HC, n=156) and LC (n=306). LC patients exhibited significantly altered hepatic and renal function markers versus HC (all P < 0.001), including decreased bilirubin fractions (TBIL, DBIL, I-BIL), reduced albumin (ALB) and total protein (TP), alongside elevated globulin (GLO), alkaline phosphatase (ALP), and lactate dehydrogenase (LDH). Renal parameters showed lower creatinine (Cr) and uric acid (UA) in LC (P < 0.001), but comparable blood urea nitrogen (BUN, P = 0.462) and glomerular filtration rate (GFR, P = 0.470). Cystatin C (Cys-C) was significantly elevated in LC (P < 0.001). No significant differences were observed in gender distribution (P = 0.676) or age (P = 0.092).

Table 1.

Clinical Characteristics of the Study Participants

Variables	HC (n =156)	LC (n =306)	χ2/Z/t	P value
N (M:F)	105/51	200/106	0.175	0.676^a
Age (years)	59.50 (55.00-66.00)	61.00 (56.00-67.00)	-1.685	0.092^c
TBIL (μmol/L)	11.10 (8.60-14.05)	6.30 (4.36-8.60)	-11.589	<0.001^c
DBIL (μmol/L)	4.10 (3.30-4.80)	2.80 (2.15-3.75)	-8.774	<0.001^c
I-BIL (μmol/L)	7.00 (5.33-9.20)	3.20 (2.20-5.00)	-12.365	<0.001^c
TP (g/L)	73.77±3.72	72.31±6.16	2.720	<0.001^b
ALB (g/L)	45.05 (43.70-46.60)	39.07 (36.50-42.30)	-13.876	<0.001^c
GLO (g/L)	29.05 (26.92-31.10)	32.10 (29.32-36.30)	-8.602	<0.001^c
A/G	1.56 (1.43-1.69)	1.22 (1.03-1.38)	-13.014	<0.001^c
ALP (U/L)	66.00 (57.00-83.25)	82.00 (66.00-107.00)	-5.405	<0.001^c
ALT (U/L)	18.40 (14.12-25.57)	16.20 (11.05-24.90)	-2.630	0.009^c
AST (U/L)	19.05 (16.13-23.30)	21.10 (16.65-27.40)	-3.014	0.003^c
LDH (U/L)	160.50 (149.75-185.00)	216.50 (177.50-267.75)	-9.784	<0.001^c
BUN (mmol/L)	5.10 (4.30-6.30)	5.10 (3.90-6.30)	-0.736	0.462^b
Cr (μmol/L)	81.00 (69.00-90.00)	71.00 (59.00-85.00)	-4.344	<0.001^c
UA (μmol/L)	356.50 (306.50-438.50)	322.00 (255.54-388.00)	-4.137	<0.001^c
TCO2 (mmol/L)	25.10 (23.90-26.30)	24.60 (22.75-26.30)	-2.844	0.004^c
GFR (mL/min)	83.02±19.36	70.33±18.80	6.794	0.470^b
Cys-C (mg/L)	0.94 (0.84-1.09)	1.09 (0.94-1.25)	-6.237	<0.001^c

Data with normal distribution was indicated by mean ± standard deviation (SD), otherwise, it was presented by median (inter-quartile range, P25-P75). The P values were calculated by ^a Pearson chi-square test, ^b Independent-sample T test and ^c Mann-Whitney U test, separately.

MTHFR Genetic Polymorphisms and Lung Cancer Susceptibility

Genotyping of MTHFR variants (rs1801131, rs1801133) was performed using multiplex fluorescence PCR (Supplemental Figure S1). All polymorphisms conformed to Hardy-Weinberg equilibrium in controls, patients, and overall cohorts (P > 0.05), indicating no significant deviation from the expected genotype distribution..

The study examined the association between MTHFR rs1801131 polymorphism and lung cancer susceptibility, suggesting that genotypic variations, particularly in the recessive model, might play a role in susceptibility to lung cancer (Table 2). Chi-square tests revealed a significant difference in the distribution of MTHFR rs1801131 GG between lung cancer patients and healthy controls (χ² = 7.706, P = 0.021). Specifically, the recessive model (TT+TG vs. GG) showed a significant association (χ² = 5.731, P = 0.017). However, allele frequencies did not differ significantly (P = 0.440), suggesting that the genotypic rather than the allelic variation is more relevant for lung cancer susceptibility.

Table 2.

Association Between Gene Polymorphisms and Healthy Individuals Versus LC Patients

Genotype and allele	HC	LC	χ2	P value
MTHFR rs1801131
TT	89 (57.1)	168 (54.9)
TG	45 (28.8)	116 (37.9)
GG	22 (14.1)	22 (7.2)	7.706	0.021
T	223 (71.5)	452 (73.9)
G	89 (28.5)	160 (26.1)	0.596	0.44
TT+TG vs GG	134 (85.9)	284 (92.8)	5.731	0.017
TT+GG vs TG	111 (71.2)	190 (62.1)	3.737	0.053
TG+GG vs TT	67 (42.9)	138 (45.1)	0.193	0.66
MTHFR rs1801133
AA	13 (8.3)	24 (7.8)
GA	62 (39.7)	126 (41.2)
GG	81 (51.9)	156 (51.0)	0.101	0.951
A	88 (28.2)	174 (28.4)
G	224 (71.8)	438 (71.6)	0.005	0.942
AA+GA vs GG	75 (48.1)	150 (49.0)	0.037	0.848
AA+GG vs GA	94 (60.3)	180 (58.8)	0.088	0.767
GA+GG vs AA	143 (91.7)	282 (92.2)	0.034	0.854

HC, health control; LC, lung cancer. Data are presented as n (%). P values were calculated using the Pearson chi-square test.

Figure 1 summarizes statistically significant regression outcomes. In LC patients, (GG vs. TT) showed a significant association (adjusted OR = 2.614, 95% CI: 1.316–5.191, P = 0.006) (Supplemental Table S3). In the LUAD subgroup analysis, GG genotype carriers exhibited a trend toward increased LUAD risk relative to the TT genotype (unadjusted OR = 2.086, 95% CI: 0.976–4.456, P = 0.058) (Figure 1A). This association strengthened marginally after age and sex adjustment (adjusted OR = 2.143, 95% CI: 0.988–4.648, P = 0.054) (Figure 1B) (Supplemental Table S4). These findings suggest that the GG genotype may be associated with an increased risk of LUAD and warrant further investigation in larger cohorts. For LUSC, the GG genotype was infrequent, and the differences between patients and healthy controls were not statistically significant (P = 0.210) (Supplemental Table S2), although trends towards an association were observed (OR = 2.548, 95% CI: 0.830–7.824, P = 0.102) (Supplemental Table S5). For SCLC, the GG genotype was also associated with susceptibility in the subgroup analysis (TT vs GG, P = 0.022), with an OR of 6.596 (95% CI: 1.396–31.172, P = 0.017) (Figure 1B; Supplemental Table S6). However, given the extremely small number of GG genotype carriers in this subgroup, this finding should be interpreted with caution.

Figure 1.

The forest plots of positive results in the logistic regression analysis. * adjusted for age and gender

MTHFR Haplotype Associations in Lung Cancer Susceptibility

Analysis of haplotypes combining the rs1801131 and rs1801133 polymorphisms (order: rs1801131→ rs1801133) revealed a significant association with susceptibility specifically to LUAD (global P = 0.038). The GG haplotype (rs1801131-G/rs1801133-G) was significantly associated with an increased risk of developing LUAD (OR = 1.527, 95% CI: 1.062–2.195, P = 0.022). Conversely, the TG haplotype (rs1801131-T/rs1801133-G) was significantly associated with a decreased risk of LUAD (OR = 0.708, 95% CI: 0.518–0.967, P = 0.029). No significant global or individual haplotype associations were observed for overall LC (global P = 0.154), LUSC (global P = 0.842), or SCLC susceptibility (global P = 0.652) (Supplemental Table S7).

MTHFR rs1801131 GG Genotype Predicts Poor Survival in LUAD

The median follow-up duration, estimated using the reverse Kaplan–Meier method, was 1,163 days. Multivariate Cox regression analysis showed that the rs1801131 GG genotype was independently associated with shorter overall survival (OS) in the entire LC cohort (HR = 1.926, 95% CI: 1.071–3.465, P = 0.029) (Table 3). Additionally, within the LUAD subgroup, this genotype was significantly associated with worse OS and PFI after multivariate adjustment (HR=2.210, 95% CI: 1.048–4.661, P = 0.037; HR = 1.979, 95% CI: 1.103–3.552, P = 0.022) ( Tables 4 and 5). Importantly, Kaplan-Meier survival curve analyses corroborated and extended these findings, demonstrating the rs1801131 GG genotype was associated with the worst OS in the overall LC cohort (P = 0.033), and with both the worst OS (P = 0.043) and worst PFI (P = 0.023) specifically within the LUAD subgroup (Figure 2A-C). In contrast, multivariate Cox models did not reveal significant associations for the rs1801133 GG genotype with OS or PFI in the presented analyses (Tables 3-5 and Supplemental Table S8).

Table 3.

Univariate and Multivariate Analyses of OS in LC Patients

Characteristics	OS
	Univariate analysis		Multivariate analysis
	HR (95%CI)	P value	HR (95%CI)	P value
Age	1.011 (0.993-1.029)	0.223
Male/Female	0.525 (0.377-0.731)	< 0.001	0.851 (0.534-1.354)	0.495
Smoking status: Yes/No	0.577 (0.427-0.779)	< 0.001	0.783 (0.474-1.293)	0.339
Drinking status: Yes/No	0.696 (0.512-0.947)	0.021	0.815 (0.533-1.247)	0.346
T Stage: IV	—	—
I	0.591 (0.218-1.601)	0.301	0.343 (0.971-1.174)	0.056
II	0.312 (0.115-0.848)	0.022	0.854 (0.296-2.464)	0.770
III	0.713 (0.496-1.026)	0.069	1.312 (0.846-2.033)	0.225
N Stage: N3	—	—
N2	0.629 (0.457-0.866)	0.005	0.692 (0.485-0.988)	0.043
N1	0.460 (0.223-0.945)	0.035	1.005 (0.446-2.266)	0.990
N0	0.266 (0.127-0.554)	< 0.001	0.438 (0.188-1.020)	0.056
M Stage: M1A	—	—
M1B	1.095 (0.715-1.677)	0.675	0.824 (0.519-1.307)	0.410
M1C	1.227 (0.846-1.779)	0.281	0.928 (0.607-1.420)	0.731
M0	0.435 (0.276-0.686)	< 0.001	0.456 (0.256-0.810)	0.007
Treatment: chemotherapy	—	—
Targeted therapy	0.646 (0.391-1.066)	0.087
Biologically targeted therapy	0.831 (0.578-1.196)	0.319
Histology: LUSC	—	—
LUAD	0.823 (0.575-1.178)	0.287	0.716 (0.470-1.092)	0.121
SCLC	1.884 (1.231-2.882)	0.004	1.229 (0.759-1.988)	0.402
Primary therapy outcome: PR	—	—
PD	3.884 (2.556-5.900)	< 0.001	3.331 (2.157-5.144)	< 0.001
SD	1.120 (0.659-1.904)	0.674	1.044 (0.607-1.796)	0.876
Cancerous site: Right	—	—
Left	0.952 (0.695-1.306)	0.761
Both	0.753 (0.185-3.058)	0.691
rs1801131: TG	—	—
TT	0.850 (0.621-1.164)	0.311	0.854 (0.614-1.188)	0.349
GG	1.886 (1.068-3.331)	0.029	1.926 (1.071-3.465)	0.029
rs1801133: GA	—	—
AA	0.915 (0.495-1.689)	0.776
GG	1.147 (0.842-1.563)	0.385

CI, confidence interval; LUAD, lung adenocarcinoma; LUSC, lung squamous carcinoma; SCLC, small cell lung cancer; HR, hazard ratio; OS, overall survival; PR, partial response; SD, stable disease; PD, progressive disease.

Table 4.

Univariate and Multivariate Analyses of OS in LUAD Patients

Characteristics	OS
	Univariate analysis		Multivariate analysis
	HR (95%CI)	P value	HR (95%CI)	P value
Age	0.984 (0.961-1.008)	0.196
Male/Female	0.518 (0.337-0.797)	0.003	0.563 (0.356-0.891)	0.014
Smoking status: Yes/No	0.694 (0.439-1.097)	0.118
Drinking status: Yes/No	0.830 (0.506-1.362)	0.461
T Stage: IV	—	—
I	0.434 (0.106-1.779)	0.246
II	1.156 (0.419-3.194)	0.779
III	0.739 (0.436-1.251)	0.260
N Stage: N3	—	—
N2	0.662 (0.416-1.053)	0.081	0.891 (0.533-1.488)	0.659
N1	0.432 (0.135-1.385)	0.158	1.197 (0.329-4.362)	0.785
N0	0.235 (0.085-0.651)	0.005	0.384 (0.124-1.187)	0.096
M Stage: M1A	—	—
M1B	1.009 (0.566-1.800)	0.975	0.621 (0.334-1.156)	0.133
M1C	1.166 (0.689-1.971)	0.568	0.935 (0.534-1.637)	0.814
M0	0.347 (0.169-0.714)	0.004	0.463 (0.214-1.003)	0.051
Treatment: chemotherapy	—	—
Targeted therapy	0.912 (0.449-1.850)	0.798
Biologically targeted therapy	1.048 (0.557-1.937)	0.885
Primary therapy outcome: PR	—	—
PD	5.012 (2.632-9.545)	< 0.001	3.860 (1.937-7.694)	< 0.001
SD	1.851 (0.867-3.955)	0.112	1.985 (0.905-4.353)	0.087
Cancerous site: Right	—	—
Left	0.823 (0.511-1.325)	0.424
Both	1.149 (0.158-8.368)	0.891
rs1801131: TG	—	—
TT	0.947 (0.595-1.506)	0.818	0.860 (0.532-1.390)	0.537
GG	2.573 (1.236-5.355)	0.011	2.210 (1.048-4.661)	0.037
rs1801133: GA	—	—
AA	0.868 (0.409-1.839)	0.711
GG	1.045 (0.659-1.657)	0.852

Table 5.

Univariate and Multivariate Analyses of PFI in LUAD Patients

Characteristics	PFI
	Univariate analysis		Multivariate analysis
	HR (95%CI)	P value	HR (95%CI)	P value
Age	0.993 (0.976-1.009)	0.385
Male/Female	1.367 (1.000-1.870)	0.050	1.355 (0.960-1.911)	0.084
Smoking status:No/Yes	1.277 (0.915-1.782)	0.150
Drinking status:Yes/No	0.830 (0.506-1.362)	0.427
T Stage: I	—	—
II	1.536 (0.530-4.449)	0.429
III	1.302 (0.570-2.975)	0.531
IV	1.312 (0.552-3.121)	0.539
N Stage: N3	—	—
N2	1.192 (0.844-1.684)	0.318
N1	1.391 (0.741-2.610)	0.304
N0	1.064 (0.597-1.895)	0.834
M Stage: M1C	—	—
M1A	1.661 (1.099-2.513)	0.016	1.774 (1.146-2.746)	0.010
M1B	1.013 (0.631-1.627)	0.958	0.969 (0.589-1.595)	0.903
M0	1.033 (0.671-1.591)	0.884	1.231 (0.780-1.943)	0.372
Treatment: chemotherapy	—	—
Targeted therapy	0.746 (0.442-1.259)	0.272
Biologically targeted therapy	0.934 (0.582-1.500)	0.778
Primary therapy outcome: PR	—	—
PD	5.012 (2.632-9.545)	< 0.001	3.860 (1.937-7.694)	< 0.001
SD	1.851 (0.867-3.955)	0.112	1.985 (0.905-4.353)	0.087
Cancerous site: Right	—	—
Left	0.823 (0.511-1.325)	0.424
Both	1.149 (0.158-8.368)	0.891
rs1801131: TG	—	—
TT	0.764 (0.548-1.065)	0.113	0.869 (0.608-1.242)	0.440
GG	1.745 (1.000-3.044)	0.050	1.979 (1.103-3.552)	0.022
rs1801133: GA	—	—
AA	1.120 (0.680-1.844)	0.656
GG	1.139 (0.676-1.919)	0.626

CI, confidence interval; LUAD, lung adenocarcinoma; LUSC, lung squamous carcinoma; SCLC,small cell lung cancer; HR, hazard ratio; OS, overall survival; PR, partial response; SD, stable disease; PD, progressive disease.

Figure 2.

Kaplan–meier survival curves of overall survival (OS) and progression-free interval (PFI) for LC patients. (A) OS for all LC patients with rs1801131 polymorphism; (B) OS for LUAD patients with rs1801131 polymorphism; (C) PFI for LUAD patients with rs1801131 polymorphism; (D) OS for all LC patients with rs1801131 polymorphism and eLDH; (E) OS for LUADD patients with rs1801131 polymorphism and eLDH

Combined Analysis of MTHFR Genotype and LDH Expression Reveals Enhanced Prognostic Risk

To further elucidate the interplay between MTHFR genotype and tumor metabolism, patients were stratified by serum LDH expression levels (high: eLDH; normal: neLDH) and combined with rs1801131 genotypes. Within the entire lung cancer cohort, patients harboring the rs1801131 GG genotype and elevated LDH (GG-eLDH) exhibited significantly worse overall survival compared to those with TG/TT genotypes and high LDH expression (TG-eLDH/TT-eLDH) (P = 0.034) (Figure 2D). In LUAD, it was also found that patients with GG-eLDH had significantly lower overall survival rates compared to TG-eLDH and TT-eLDH (P = 0.042) (Figure 2E).

Association Between MTHFR Gene Polymorphism (rs1801131) and its Protein Levels in LUAD

Upon categorizing and examining the immunohistochemical outcomes by genotype in LUAD tissues, it was discovered that the rs1801131-GG genotype was associated with a lower protein expression level compared to both the TG and TT genotypes (Figure 3). In addition, no significant differences in protein levels were observed among the rs1801131 genotypes in other histological types of LC examined (LUSC and SCLC).

Figure 3.

Relationship between protein expression and rs1801131 genotype of MTHFR in LUAD patients. (A) rs1801131-TT; (B) rs1801131-TG; (C) rs1801131-GG; (D) differential statistics of MTHFR genotypes and protein levels in LUAD patients

Discussion

In the present study, focusing on the Guangxi Zhuang population, we found that the MTHFR rs1801131 GG genotype was associated with poorer survival in patients with LUAD, as supported by multivariate Cox regression and Kaplan–Meier analyses. Patients carrying the GG genotype combined with elevated LDH also showed worse overall survival in both the overall lung cancer cohort and the LUAD subgroup. Given the limited data available for this ethnic group, our findings provide population-specific evidence and suggest that rs1801131, particularly in combination with LDH, may have prognostic value, although further validation in larger and multi-ethnic cohorts is needed.

Molecular genetic research has increasingly linked various genetic and epigenetic alterations to the pathogenesis of LC. Among these, polymorphisms in the MTHFR gene are significant, as they reduce enzymatic activity and expression, thereby disrupting folate metabolism and initiating a cascade of downstream events.²¹ Currently, only two common MTHFR polymorphisms, rs1801133 (C677T) and rs1801131 (A1298C), have been conclusively shown to impact enzymatic function.²² The rs1801133 variant involves a C>T substitution resulting in an alanine-to-valine replacement at position 222 (A222V) within the catalytic domain, which reduces enzymatic efficiency by approximately 35%.²³ In contrast to rs1801133 homozygosity, which is strongly associated with hyperhomocysteinemia,²⁴ the rs1801131 (A1298C) risk allele (C variant) does not typically elevate homocysteine levels. Nevertheless, this polymorphism similarly reduces MTHFR activity, potentially impairing folate cycling and associated metabolic processes.²⁵

Our findings suggest that the MTHFR rs1801131 GG genotype significantly elevates LUAD susceptibility and independently predicts adverse prognosis in the Guangxi Zhuang population—a contrast to studies reporting null associations in non-Asian cohorts.^26,27 This discrepancy likely stems from ethnic-specific genetic architectures and histological stratification. Recent East Asian evidence further supports the possibility that ancestry-specific molecular contexts contribute to heterogeneity in LUAD susceptibility and may partly explain why certain loci show stronger effects in specific populations than in European cohort.²⁸ While rs1801131 showed no overall LC risk in European²⁹ or non-Hispanic White populations,²⁷ its effect was pronounced in LC or LUAD subgroups in Asians^14-16 and Turkey.³⁰ Notably, the GG haplotype (rs1801131-G/rs1801133-G) conferred 1.53-fold increased LUAD risk (P = 0.022), aligning with reports of elevated DNA adducts in high-risk smokers carrying this variant.³¹

The synergistic interaction between the rs1801131-GG genotype and eLDH underscores the established role of LDH as a biomarker of tumor metabolic reprogramming. Elevated serum LDH reflects enhanced glycolysis (Warburg effect) and correlates with aggressive phenotypes, metastasis, and poor survival in NSCLC.^19,32 We showed that rs1801131-GG carriers exhibit both reduced tumor MTHFR protein and LDH-associated risk amplification, suggesting a model of metabolic dysregulation. The rs1801131 chronically impairs folate cycling.^25,33 This disruption may alter NAD⁺/NADH homeostasis (33)—a key regulator of LDH activity—explaining the allele’s association with elevated serum LDH even in non-oncogenic contexts (e.g., high-intensity athletes³⁴). This study revealed that the combined effect of rs1801131 and LDH on the prognosis of LC and LUAD may account for the inconsistent findings regarding rs1801131’s association with chemotherapy toxicity, complications, and poor prognosis in LC patients reported in the current literature,^35-38,39likely due to the lack of stratification for this factor. Taken together, these observations suggest that reduced MTHFR expression combined with elevated LDH may be associated with a more aggressive phenotype in LUAD; however, the underlying functional consequences and mechanistic basis still require further validation in dedicated mechanistic studies.

Several limitations of this study should be acknowledged. First, this was a single-center study conducted in a Zhuang cohort, which may limit the generalizability of the findings to other populations. Second, the sample sizes of certain subgroup analyses were relatively small, particularly in the SCLC subgroup, where the number of patients carrying the rs1801131 GG genotype was extremely limited. This restricted the statistical power of the subgroup analyses and may affect the robustness of the subgroup-specific observations. Therefore, these findings, especially those derived from the SCLC subgroup, should be interpreted cautiously and considered preliminary. Future studies should include larger multicenter and multi-ethnic cohorts, together with functional investigations and external validation, to further clarify the biological and clinical relevance of this association. Despite these limitations, our findings suggest that the combination of rs1801131 genotype and LDH may offer supplementary information for risk stratification in LUAD within the Guangxi Zhuang population.

Conclusion

Supplemental Material

Supplemental Material - MTHFR rs1801131 GG Genotype Combined With Serum LDH Level Predicts Lung Adenocarcinoma Susceptibility and Poor Prognosis in Guangxi Zhuang Population

Supplemental Material for MTHFR rs1801131 GG Genotype Combined With Serum LDH Level Predicts Lung Adenocarcinoma Susceptibility and Poor Prognosis in Guangxi Zhuang Population by Chao Zuo, Yuanyuan Wang, Dongli Yang, Jing Cheng, Mengna Guo, Zhuo Yang, Yu Wang, Feng Wang and Yongchao Qiao in Cancer Control.

Supplemental Material

Supplemental Material - MTHFR rs1801131 GG Genotype Combined With Serum LDH Level Predicts Lung Adenocarcinoma Susceptibility and Poor Prognosis in Guangxi Zhuang Population

Footnotes

Acknowledgments

The authors of this article express gratitude for the collaborative efforts and contributions made by all members involved.

ORCID iD

Yongchao Qiao

Ethical Considerations

This study was approved by the Institutional Review Board of the First Affiliated Hospital of Guilin Medical University (approval No. 2023QTLL-36; approved on December 15, 2023).

Consent to Participate

Written informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki of 1975, as revised in 2024. All patient data were de-identified before analysis. All patients provided written informed consent prior to their inclusion in the study.

Author contributions

Conceptualization: Y.Q., F.W.; Methodology: C.Z., Y.W., D.Y., J.C.; Formal analysis: M.G., Z.Y., Y.W.; Investigation: C.Z., Y.W., D.Y., J.C.; Writing - original draft: C.Z.; Writing - review & editing: Y.Q., F.W.; Supervision: Y.Q., F.W.; Funding acquisition: Y.Q., F.W.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Natural Science Foundation of Guangxi Zhuang Autonomous Region (2025GXNSFHA069198) and the Guangxi medical and health appropriate technology development and application project (S2024041).

Declaration of Conflicting Interests

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

Data Availability Statement

All data associated with this study are available in the main text or the supplementary materials. The original data analyzed in this study are available from the corresponding author upon reasonable request.*

Use of Artificial Intelligence

Artificial intelligence was not used in the preparation of this manuscript.

Patient and Public Involvement

It was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research.

Supplemental Material

Supplemental material for this article is available online.

Appendix

References

Smolarz

Łukasiewicz

Samulak

Piekarska

Kołaciński

Romanowicz

. Lung Cancer-Epidemiology, Pathogenesis, Treatment and Molecular Aspect (Review of Literature). Int J Mol Sci. 2025;26(5):2049. doi:10.3390/ijms26052049.

Cao

, et al. Comparative analysis of cancer statistics in China and the United States in 2024. Chin Med J (Engl). 2024;137(24):3093-3100. doi:10.1097/CM9.0000000000003442.

Kratzer

Bandi

Freedman

, et al. Lung cancer statistics, 2023. Cancer. 2024;130(8):1330-1348. doi:10.1002/cncr.35128.

Malhotra

Malvezzi

Negri

La Vecchia

Boffetta

. Risk factors for lung cancer worldwide. Eur Respir J. 2016;48(3):889-902. doi:10.1183/13993003.00359-2016.

Mihray

Chen

. Research Progress on Heterogeneity of Tumor Mutation Burden in Patients with Non-small Cell Lung Cancer. Zhongguo Fei Ai Za Zhi. 2021;24(4):293-298.

Hudson

Chan

Woolf

, et al.

Is heterogeneity in stage 3 non-small cell lung cancer obscuring the potential benefits of dose-escalated concurrent chemo-radiotherapy in clinical trials?

Lung Cancer. 2018;118:139-147. doi:10.1016/j.lungcan.2018.02.006.

Long

Patel

Golden

, et al. High-throughput characterization of functional variants highlights heterogeneity and polygenicity underlying lung cancer susceptibility. Am J Hum Genet. 2024;111(7):1405-1419. doi:10.1016/j.ajhg.2024.05.021.

de Souza

Marinho

Marques

. The Fundamental Role of Nutrients for Metabolic Balance and Epigenome Integrity Maintenance. Epigenomes. 2025;9(3):3. doi:10.3390/epigenomes9030023.

Vidmar

Šmid

Karas Kuželički

Trontelj

Geršak

Mlinarič-Raščan

. Folate Insufficiency Due to MTHFR Deficiency Is Bypassed by 5-Methyltetrahydrofolate. Journal of Clinical Medicine. 2020;9(9):9. doi:10.3390/jcm9092836.

10.

Araszkiewicz

Jańczak

Wójcik

, et al. MTHFR Gene Polymorphisms: A Single Gene with Wide-Ranging Clinical Implications—A Review. Genes. 2025;16(4):4. doi:10.3390/genes16040441.

11.

Kim

Choi

Jung

. Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer. Clin Epigenetics. 2020;12(1):119. doi:10.1186/s13148-020-00907-4.

12.

Teng

Balch

Liu

, et al. The influence of cis-regulatory elements on DNA methylation fidelity. Published online 2012. https://hdl.handle.net/1805/49295. Accessed 12 July 2025.

13.

Gnimpieba

Eveillard

Guéant

Chango

. Using logic programming for modeling the one-carbon metabolism network to study the impact of folate deficiency on methylation processes. Mol Biosyst. 2011;7(8):2508-2521. doi:10.1039/c1mb05102d.

14.

Al-Motassem

Shomaf

Said

, et al. Allele and Genotype Frequencies of the Polymorphic Methylenetetrahydrofolate Reductase and Lung Cancer in ther Jordanian Population: a Case Control Study. Asian Pac J Cancer Prev. 2015;16(8):3101-3109. doi:10.7314/apjcp.2015.16.8.3101.

15.

Zhang

Yang

. Meta-analysis on MTHFR polymorphism and lung cancer susceptibility in East Asian populations. Biomed Rep. 2013;1(3):440-446. doi:10.3892/br.2013.68.

16.

Tong

Jin

. MTHFR C677T and A1298C polymorphisms and lung cancer risk in a female Chinese population. Cancer Manag Res. 2018;10:4155-4161. doi:10.2147/CMAR.S176263.

17.

Zuo

Zhu

Zhang

, et al. The response prediction and prognostic values of systemic inflammation response index in patients with advanced lung adenocarcinoma. Thorac Cancer. 2023;14(16):1500-1511. doi:10.1111/1759-7714.14893.

18.

Chan

SWS

Smith

Aggarwal

, et al. Systemic Inflammatory Markers of Survival in Epidermal Growth Factor-Mutated Non-Small-Cell Lung Cancer: Single-Institution Analysis, Systematic Review, and Meta-analysis. Clin Lung Cancer. 2021;22(5):390-407. doi:10.1016/j.cllc.2021.01.002.

19.

von

Altman

Egger

, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med. 2007;147(8):573-577. doi:10.7326/0003-4819-147-8-200710160-00010.

20.

Wang

, et al. Prognostic value of lactate dehydrogenase in non-small cell lung cancer patients with brain metastases: a retrospective cohort study. J Thorac Dis. 2022;14(11):4468-4481. doi:10.21037/jtd-22-1502.

21.

Zhang

, et al. A partition-ligation-combination-subdivision EM algorithm for haplotype inference with multiallelic markers: update of the SHEsis. Cell Res. 2009;19(4):519-523. doi:10.1038/cr.2009.33.

22.

Barretta

Uomo

Fecarotta

, et al. Contribution of Genetic Test to Early Diagnosis of Methylenetetrahydrofolate Reductase (MTHFR) Deficiency: The Experience of a Reference Center in Southern Italy. Genes (Basel). 2023;14(5):980. doi:10.3390/genes14050980.

23.

Leclerc

Sibani

Rozen

. Molecular Biology of Methylenetetrahydrofolate Reductase (MTHFR) and Overview of Mutations/Polymorphisms. In: Madame Curie Bioscience Database. Landes Bioscience; 2013. https://www.ncbi.nlm.nih.gov/books/NBK6561/. Accessed 14 July 2025.

24.

Zarembska

Ślusarczyk

Wrzosek

. The Implication of a Polymorphism in the Methylenetetrahydrofolate Reductase Gene in Homocysteine Metabolism and Related Civilisation Diseases. Int J Mol Sci. 2023;25(1):193. doi:10.3390/ijms25010193.

25.

Frosst

Blom

Milos

, et al. A candidate genetic risk factor for vascular disease: a common mutation in methylenetetrahydrofolate reductase. Nat Genet. 1995;10(1):111-113. doi:10.1038/ng0595-111.

26.

Yamada

Chen

Rozen

Matthews

. Effects of common polymorphisms on the properties of recombinant human methylenetetrahydrofolate reductase. Proc Natl Acad Sci U S A. 2001;98(26):14853-14858. doi:10.1073/pnas.261469998.

27.

Mao

Fan

Jin

Bai

. Methylenetetrahydrofolate reductase gene polymorphisms and lung cancer: a meta-analysis. J Hum Genet. 2008;53(4):340-348. doi:10.1007/s10038-008-0262-6.

28.

Shen

Spitz

Wang

Hong

Wei

. Polymorphisms of methylene-tetrahydrofolate reductase and risk of lung cancer: a case-control study. Cancer Epidemiol Biomarkers Prev. 2001;10(4):397-401.

29.

Chen

Chien

Jiang

, et al. Identifying expression and DNA methylation biomarkers for lung adenocarcinoma risk in East Asia. J Natl Cancer Inst. 2026; djag021. doi:10.1093/jnci/djag021. Published online January 24.

30.

Siemianowicz

Gminski

Garczorz

, et al. Methylenetetrahydrofolate reductase gene C677T and A1298C polymorphisms in patients with small cell and non-small cell lung cancer. Oncol Rep. 2003;10(5):1341-1344.

31.

Arslan

Karadayi

Yildirim

Ozdemir

Akkurt

. The association between methylene-tetrahydrofolate reductase gene polymorphism and lung cancer risk. Mol Biol Rep. 2011;38(2):991-996. doi:10.1007/s11033-010-0194-z.

32.

Lee

Asomaning

Wain

Mark

Christiani

. MTHFR polymorphisms, folate intake and carcinogen DNA adducts in the lung. Int J Cancer. 2012;131(5):1203-1209. doi:10.1002/ijc.27338.

33.

Thompson

McGovern

Roxburgh

CSD

Edwards

Dolan

McMillan

. The relationship between LDH and GLIM criteria for cancer cachexia: Systematic review and meta-analysis. Crit Rev Oncol Hematol. 2024;199:104378. doi:10.1016/j.critrevonc.2024.104378.

34.

Wang

, et al. MTHFR Knockdown Assists Cell Defense against Folate Depletion Induced Chromosome Segregation and Uracil Misincorporation in DNA. Int J Mol Sci. 2021;22(17):9392. doi:10.3390/ijms22179392.

35.

Di Mauro

Currò

Trimarchi

, et al. Role of Genetic Background in Cardiovascular Risk Markers Changes in Water Polo Players. Int J Sports Med. 2018;39(5):390-396. doi:10.1055/s-0044-101459.

36.

Shao

Wang

, et al. Heterozygote advantage of methylenetetrahydrofolate reductase polymorphisms on clinical outcomes in advanced non-small cell lung cancer (NSCLC) patients treated with platinum-based chemotherapy. Tumour Biol. 2014;35(11):11159-11170. doi:10.1007/s13277-014-2427-6.

37.

Liu

Wang

Luo

Yuan

Luo

. Genetic Polymorphisms and Platinum-Based Chemotherapy-Induced Toxicities in Patients With Lung Cancer: A Systematic Review and Meta-Analysis. Front Oncol. 2019;9:1573. doi:10.3389/fonc.2019.01573.

38.

Mak

Alexander

Asomaning

, et al. A single-nucleotide polymorphism in the methylene tetrahydrofolate reductase (MTHFR) gene is associated with risk of radiation pneumonitis in lung cancer patients treated with thoracic radiation therapy. Cancer. 2012;118(14):3654-3665. doi:10.1002/cncr.26667.

39.

Pérez-Ramírez

Cañadas-Garre

Alnatsha

, et al. Pharmacogenetics of platinum-based chemotherapy: impact of DNA repair and folate metabolism gene polymorphisms on prognosis of non-small cell lung cancer patients. Pharmacogenomics J. 2019;19(2):164-177. doi:10.1038/s41397-018-0014-8.