Sage Journals: Discover world-class research

Abstract

The relationship between PLIN2 expression and prognosis, and clinicopathological significance of various cancers has been extensively studied, but the results are not completely consistent. This review followed the guidelines for systematic reviews of prognostic factors studies and was reported under the Preferred Reporting Program for Systematic Reviews and Meta-Analysis (PRISMA). We searched PubMed, Embase, Cochrane Library, Web of Science, and Google Academia for relevant articles up to September 2, 2022, and calculated the pooled hazard ratios (HR) with 95% confidence intervals (CI) to determine the association between PLIN2 expression and the prognosis of various cancers. The meta-analysis ultimately included 17 studies. The quality of all included cohort studies was evaluated using the Quality in Prognosis Studies (QUIPS) tool, and an adaptation of Grading of Recommendations Assessment, Development and Evaluation (GRADE) method was used to assess the certainty of the results. High expression of PLIN2 was associated with poorer overall survival (HR = 1.65; 95% CI = 1.14, 2.38; P = 0.008), metastasis-free survival (HR = 1.48; 95% CI = 1.12, 1.94; P = 0.005), progression-free survival (HR = 2.11; 95% CI = 1.55, 2.87; P < 0.0005) and recurrence-free survival/relapse-free survival (HR = 2.21; 95% CI = 1.64, 2.98; P < 0.0005) in cancers. The clinicopathological parameters of digestive system malignancies suggested that high expression of PLIN2 was notably associated with distant metastasis ( + ) (odds ratio (OR) = 3.37; 95% CI = 1.31, 8.67; P = 0.012), lymph node metastasis ( + ) (OR = 1.61; 95% CI = 1.01, 2.54; P = 0.004), and tumor stage (III–IV) (OR = 1.96; 95% CI = 1.24, 3.09; P = 0.006). In summary, overexpression of PLIN2 is significantly associated with a poor prognosis in various human cancers, especially in respiratory and digestive malignancies. Thus, PLIN2 expression may be a potential prognostic biomarker in cancer patients.

Keywords

PLIN2 cancers prognosis clinicopathologic systematic review meta-analysis

Introduction

The latest global cancer statistics, GLOBOCAN-2020, show that there are about 19.3 million cancer cases and nearly 10 million cancer-related deaths each year.¹ Cancer could be the leading cause of premature death in most countries in this century,^2,3 in part because approximately 50% of cancers are detected in an advanced stage. No country can eliminate cancer by treatment alone. Large-scale investments to ensure long-term cancer prevention and strategies for early detection, diagnosis, and treatment are fundamental to minimize the cancer burden.⁴ The discovery and validation of a useful cancer biomarker can provide sufficient information for early diagnosis to make clinical decisions and achieve a better prognosis for patients.⁵

PLIN2,⁶ also known as Perilipin 2, Adipophilin,⁷ and Adipose Differentiation-Related Protein (ADRP/ADFP),^8,9 is a protein-coding gene with a molecular weight of 50 kDa, located on human chromosome 9p22.1, encoding 437 amino acids.⁶ PLIN2 exists in lipid droplets (LD) in cells and relate to substances present on the surface of LD and plays a key role in the regulation of a variety of cellular functions.¹⁰ The biophysical properties of cell biofilms determine the efficiency of signal transmission, leading to changes in lipid metabolism pathways, which are closely related to cancer and are the biochemical basis for the carcinogenicity and malignancy of tumor cells.¹¹ Cancer cells exhibit uncontrollable changes in cellular lipid metabolism, including endogenous lipid synthesis, storage, and degradation, and disruption of exogenous uptake of membrane-forming materials. PLIN2 regulates cell function and lipid metabolism, leading to the transformation of the membrane properties of cancer cells.¹²

The in-depth study of PLIN2 found that it is highly expressed in Burkitt lymphoma,¹³ colorectal cancer,¹⁴ liver cancer,¹⁵ skin malignant melanoma,¹⁶ gastric carcinoma,¹⁷ and more. Therefore, PLIN2 may affect the carcinogenic mechanism as an oncogene. Conversely, however, the higher expression of PLIN2 in the urine and kidney tissue of patients with clear-cell renal cell carcinoma (ccRCC) represents a better prognosis.¹⁸ Similarly, high expression of PLIN2 in hepatoblastoma (HB) has also been shown to have a better prognosis.¹⁹ Inconsistency in study outcomes has resulted in an indeterminate prognostic value of PLIN2 in various cancers. Therefore, we combined data from all relevant studies in a systematic review and meta-analysis to assess the association of PLIN2 expression with cancer prognosis and clinical pathological characteristics.

Materials and methods

Guidance protocol

This review followed the guidelines for systematic reviews of prognostic factors studies²⁰ and was reported under the Preferred Reporting Program for Systematic Reviews and Meta-Analysis (PRISMA)²¹ (Supplemental material Table S1). We registered our protocol on PROSPERO (National Institute for Health Research, International Prospective Register of Systematic Reviews https://www.crd.york.ac.uk/PROSPERO) with number CRD42022375842.

All the work of this systematic review was carried out by two investigators (ML-L and HY-L) independently, and differences that occurred during the period were resolved by discussion and consultation. If no consensus was reached, a third reviewer (LT-H) was involved in resolving them.

Literature search strategies

We clearly defined the question of systematic review using the PICOTS²⁰ system (Population, Index prognostic factor, Comparator prognostic factors, Outcome, Timing, Setting) system (Table S2) and comprehensively searched PubMed, Embase, Cochrane Library, Web of Science, and Google Academia for relevant prognostic articles from cohort studies investigating the relationship between PLIN2 expression and clinical outcomes in cancer patients. The endpoint of the search was September 2, 2022. Using a combination of three keywords (and their synonyms) as a search topic, we entered (“plin2” or “PLIN2” or “Perilipin 2” or “ADRP” or “Adipose Differentiation-Related Protein” or “Adipophilin” or “ADFP” or “Perilipin-2”) and (“cancer” or “tumor” or “carcinoma” or “malignancy”) and (“prognos*” or “outcome” or “survival”) in the advanced search engines of various databases to retrieve literature that met the requirements of our study. To avoid omissions caused by the manual retrieval process, we carefully read the relevant reference lists for the included articles.

Inclusion and exclusion criteria

The following inclusion criteria were performed for this systematic review: (a) published studies in English; (b) original articles describing prognostic studies of PLIN2 (or other synonym) expression levels in cancer; (c) articles describing prognostic indicators and/or clinical characteristics of cancer; and (d) hazard ratio (HR) value and 95% confidence interval (CI) can be directly extracted from the text or indirectly calculated through the Kaplan–Meier (K–M) survival curve; odds ratio (OR) value, and 95% CI can be directly extracted from the text or calculated by extracting the corresponding sample size of the experimental group and the control group in the text.

This study complied with the following exclusion criteria: (a) non-English language publications; (b) non-prognostic studies; (c) lack of prognostic data; (d) animal studies; (e) reviews, case reports, letters, editorials, meta-analyses, and conference reports; and (f) repeated data articles with smaller sample sizes.

Data extraction and quality assessment

We performed extraction using CHARMS-PF²⁰ (a modification of CHARMS²² (checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies)) checklist for prognostic factors. The main information extracted from eligible studies was: first author, year, country, data sources, genetic-testing methods, cutoff value for PLIN2 expression, tumor type, tumor stage, age, outcome measures, sample sizes (high expression/ low expression), HR (95% CI) with its P value, follow-up months, overall risk of bias of QUIPS (Quality in Prognosis Studies) tool, and clinicopathological parameters. If the HR (95% CI) value was not directly available in the included study text, the survival data were extracted from the K–M curve by Engauge Digitizer software (produced by Mark Mitchell et al., Webpage: http://markummitchell.github.io/engauge-digitizer/) and then the Tierney's method²³ was used to calculate the HR (95% CI) value.

The quality of all included cohort studies was evaluated using the QUIPS tool,²⁴ which includes six domains for bias and applicability: study participation, study attrition, prognostic factor measurement, outcome measurement, adjustment for other prognostic factors, and statistical analysis and reporting. The quality of individual study reporting was assessed using the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) checklist²⁵ and informed the QUIPS assessments. We defined the overall risk of bias as: (a) low: low risk of bias in all domains or moderate risk in only one domain; (b) moderate: two to four domains were considered to have moderate risk of bias; and (c) high: at least five domains are considered moderate risk or at least one domain is high risk.

Statistical analysis

Stata 15.0 software (STATA Corporation, College Station, TX, USA) was chosen for this review. The relationship between PLIN2 expression and each prognostic indicator was represented by the respective HR and 95% CI, respectively. Additionally, OR and 95% CI were appropriately used to assess clinicopathological parameters. Subgroup analyses were cleverly performed to explore sources of heterogeneity. The Higgins I² test and the Cochran Q test were used to assess heterogeneity.²⁶ Publication bias was assessed using funnel plots, and if significant asymmetry was revealed, the Begg test and Egger test were used for further validation. Sensitivity analyses were performed to verify the robustness of the results by omitting each study in turn. Statistical significance was set at P < 0.05.

Confidence in summary results

An adaptation of Grading of Recommendations Assessment, Development and Evaluation (GRADE) instrument, which is based on study design, risk of bias, inconsistency, indirectness, imprecision, and other considerations, was used to examine confidence in the main summary results of systematic reviews of prognostic factor research.²⁷ The quality of evidence was assessed through a guideline development tool (http://gradepro.org) and ultimately graded as high, moderate, low, or very low.

Results

Study identification and characteristics

Details of the search strategy for all database (Table S3) and screening processes are shown in Figure 1. Twenty-four articles were evaluated in full text, and we eliminated seven after consultation. Five studies^14,28–31 had no survival data, one³² included patients with non-cancer, and one³³ included duplicate data. Ultimately, 17 articles were included. Four studies were conducted in China, 10 in Japan and 1 each in the United States, Germany, and South Korea. There were 4848 patients with sample sizes ranging from 45 to 1402. The included studies used quantitative real-time-polymerase chain reaction or immunohistochemistry to detect the expression level of PLIN2, and all patients were divided into high or low PLIN2 expression groups by cutoff value. In the included studies, there were 10 different malignancies: breast cancer,^34–38 ccRCC,^18,39,40 cutaneous malignant melanoma,⁴¹ HB,¹⁹ hepatocellular carcinoma,⁴² high-grade serous ovarian carcinoma,⁴³ lung adenocarcinomas,^44,45 oral squamous cell carcinoma,⁴⁶ pancreatic ductal adenocarcinoma,⁴⁷ and salivary duct carcinoma.⁴⁸ The result of analysis of survival data from the included studies were obtained following patients for 40–300 months. Analysis of survival data results in the form of prognostic indicators contained 10 overall survival (OS), 4 disease-free survival (DFS), 3 disease-specific survival (DSS), 3 metastasis-free survival (MFS), 3 progression-free survival (PFS), and 5 recurrence-free survival/relapse-free survival (RFS). The main characteristics of all included articles are summarized in Table 1.

Figure 1.

Flow diagram of literature search.

Table 1.

The main characteristics of eligible studies.

First author	Year/^Ref	Country	Data sources	Genetic testing methods	Cut-off value	Tumor type	Tumor stage I + II(n) vs. III + IV/III(n)	Age (%)	Outcome measures	Sample size (n)	Expression of PLIN2(n)		Follow-up months (median/maximum)	Overall risk of bias of QUIPS tool
First author	Year/^Ref	Country	Data sources	Genetic testing methods	Cut-off value	Tumor type	Tumor stage I + II(n) vs. III + IV/III(n)	Age (%)	Outcome measures	Sample size (n)	High	Low	Follow-up months (median/maximum)	Overall risk of bias of QUIPS tool
Masahiro Yao	2005³⁹	Japan	Tissue	qRT-PCR	NA	ccRCC	79 vs. 24	≥56 (74.8)	DSS	103	42	61	1.9–188.6 (71 ± 49.3)	Moderate
Masahiro Yao	2007¹⁸	Japan	Tissue	qRT-PCR	＞64%	ccRCC	300 vs. 130	≥60 (57.0)	DSS	432	293	139	0.4–236.5 (68.7)	Low
Masahiro Yao	2007¹⁸	Japan	Tissue	qRT-PCR	＞64%	ccRCC	300 vs. 130	≥60 (57.0)	DFS	339	251	88	0.4–236.5 (68.7)	Low
Kimberly S. Lucenay	2016³⁴	America	Tissue	IHC	NA	BC	I + II(203) vs. III(55)	NA	RFS	100	30	70	120 (maximum)	High
Masakazu Fujimoto	2016⁴⁴	Japan	Tissue	IHC	NA	ADC	I + II(293) vs. IIIA(35)	≥66 (53.0)	OS	328	51	277	0.3–126.8 (45)	Low
Masakazu Fujimoto	2016⁴⁴	Japan	Tissue	IHC	NA	ADC	I + II(293) vs. IIIA(35)	≥66 (53.0)	RFS	328	51	277	0.3–126.8 (45)	Low
Qi Cao	2018⁴⁰	China	TCGA database	IHC	NA	ccRCC	319 vs. 207	≥60 (53.8)	OS	526	263	263	166 (maximum)	Moderate
Qi Cao	2018⁴⁰	China	TCGA database	IHC	NA	ccRCC	319 vs. 207	≥60 (53.8)	DFS	526	263	263	150 (maximum)	Moderate
Sun Ah Shin	2018⁴⁵	Korea	Tissue	IHC	NA	ADC	158 vs. 40	＞60 (60)	OS	230	30	200	1.2–162.7 (49.55)	Low
Sun Ah Shin	2018⁴⁵	Korea	Tissue	IHC	NA	ADC	158 vs. 40	＞60 (60)	PFS	230	30	200	120 (maximum)	Low
Cristina Cadenas	2019³⁵	Germany	GSE11121, GSE2034, GSE5327, GSE6532, GSE7390	IHC	NA	BC	I + II(NA) vs. III(NA)	≥50 (71.3)	MFS	824	NA	NA	105.1–110.6 (NA)	Moderate
Yuki Hashimoto	2019⁴⁷	Japan	Tissue	IHC	NA	PDAC	116 vs. 65	NA	OS	181	51	130	25–115 (48)	Low
Yuki Hashimoto	2019⁴⁷	Japan	Tissue	IHC	NA	PDAC	116 vs. 65	NA	RFS	150	37	113	0–60 (19)	Low
Masakazu Fujimoto	2020⁴¹	Japan	Tissue	IHC	NA	CMM	63 vs. 27	≥70 (51.1)	OS	90	44	46	1.16–144 (60.7)	Low
									MFS	90	44	46
									DSS	90	44	46
Katsuhiro Yoshikawa	2020³⁶	Japan	Tissue	IHC	>30%	TNBC	I + II(53) vs III(8)	NA	RFS	61	14	47	40 (maximum)	Low
Hideaki Hirai	2020⁴⁸	Japan	Tissue	IHC	>5%	SDC	51 vs. 96	NA	OS	147	112	35	0.48–48 (NA)	Low
Hideaki Hirai	2020⁴⁸	Japan	Tissue	IHC	>5%	SDC	51 vs. 96	NA	PFS	147	112	35	0.48–48 (NA)	Low
Sadafumi Azukisawa	2021¹⁹	Japan	Tissue	IHC	>12.5%	HB	NA	≥8 (6.9)	DFS	85	33	52	120 (maximum)	Low
Xuede Zhang	2021³⁷	China	TCGA database	IHC	>50%	BC	NA	NA	OS	1402	700	702	300 (maximum)	Moderate
Naoyuki Iwahashi	2021⁴³	Japan	Tissue	IHC	>15%	HGSOC	11 vs. 85	NA	OS	96	35	61	0.5–168.5 (25.9)	Low
Naoyuki Iwahashi	2021⁴³	Japan	Tissue	IHC	>15%	HGSOC	11 vs. 85	NA	PFS	96	35	61	0.5–168.5 (13.8)	Low
Katsuhiro Yoshikawa	2022³⁸	Japan	Tissue	IHC	>30%	TNBC	I + II(94) vs. III(8)	NA	RFS	102	36	66	40 (maximum)	Low
Wuguang Liu	2022⁴²	China	Tissue	IHC	NA	HCC	25 vs. 20	＞58 (55.6)	OS	45	23	22	100 (maximum)	Low
Yijia He	2022⁴⁶	China	Tissue	IHC	NA	OSCC	62 vs. 34	≥60 (57.3)	OS	96	NA	NA	2–60 (39)	Low
									DFS	96	NA	NA
									MFS	96	NA	NA

ADC: lung adenocarcinomas; BC: breast cancer; ccRCC: clear-cell renal cell carcinoma; CMM: cutaneous malignant melanoma; DFS: disease-free survival; DSS: disease-specific survival; HB: hepatoblastoma; HCC: hepatocellular carcinoma; HGSOC: high-grade serous ovarian carcinoma; IHC: immunohistochemistry; MFS: metastasis-free survival; NA: not available; OS: overall survival; OSCC: oral squamous cell carcinoma; PDAC: pancreatic ductal adenocarcinoma; PFS: progression-free survival; qRT-PCR: quantitative real-time polymerase chain reaction; QUIPS: Quality in Prognosis Studies; Ref: reference number; RFS: recurrence-free survival/relapse-free survival; SDC: salivary duct carcinoma; TNBC: triple-negative breast cancer.

Quality assessment

The overall risk of bias of each study assessed by the QUIPS tool is presented in Table 1. Twelve studies were at low risk of bias, four studies were at moderate risk of bias, and one study was considered at high risk. Details of the risk of bias in each domain are provided in Table S4.

Relationship between high expression of PLIN2 and OS

Data from 3141 patients in 10 studies were used to calculate HR and 95% CI of OS. Pooled HR indicated that patients with high expression of PLIN2 had a significantly worse prognosis (pooled HR = 1.65; 95% CI = 1.14, 2.38; P = 0.008, random effects model) (Figure 2(a)).

Figure 2.

Forest plots of studies evaluating the association between PLIN2 expression with (a) overall survival (os); (b) disease-free survival (DFS); (c) disease-specific survival (DSS); (d) metastasis-free survival (MFS); (e) progression-free survival (PFS); and (f) recurrence-free survival/relapse-free survival (RFS).

Subgroup analysis for OS

To find out which studies caused heterogeneity, we performed subgroup analysis of cancer type (Figure 3(a)) and sample size (Figure 3(b)). Subgroup analysis showed that PLIN2 overexpression in the respiratory system cancer (pooled HR = 2.56; 95%CI = 1.76, 3.73; P < 0.0005, fixed effects model) and the digestive system cancer (pooled HR = 1.89; 95% CI = 1.40, 2.54; P = 0.000, fixed effects model) had a significantly worse prognosis, while in other systems cancer (pooled HR = 1.01; 95% CI = 0.87, 1.18; P = 0.877, random effects model) expression of PLIN2 was not associated with a significant difference in OS. After stratification by sample size, whether the sample size was <150 patients (pooled HR = 2.03; 95% CI = 1.38, 2.99; P < 0.0005, fixed effects model), or ≥150 patients (pooled HR = 1.19; 95% CI = 1.04, 1.36; P = 0.012, fixed effects model), high expression of PLIN2 and poor patient prognosis showed both a significant relationship.

Figure 3.

Forest plots of the subgroup analysis evaluating HRs of PLIN2 for overall survival (OS) by the factors of (a) cancer type; and (b)sample size. Forest plots of the subgroup analysis evaluating HRs of PLIN2 for (c) disease-free survival (DFS) by the factors of cancer type; and (d) recurrence-free survival/relapse-free survival (RFS) by the factors of sample size.

Relationship between high expression of PLIN2 and other outcome measures

Other outcome measures include DFS, DSS, MFS, PFS, and RFS. Four studies which included 1046 patients assessed the HR and 95% CI of DFS (Figure 2(b)), and the results (pooled HR = 0.88; 95% CI = 0.45, 1.73; P = 0.715, random effects model) showed that the expression of PLIN2 had no statistical significance on cancer patients. A total of 625 patients were included in three studies that evaluated DSS (Figure 2(c)), and the results were also not statistically significant (pooled HR = 0.56; 95% CI = 0.21, 1.50; P = 0.247, random effects model). In addition, three studies analyzed MFS (pooled HR = 1.48; 95% CI = 1.12, 1.94; P = 0.005, fixed effects model), including 1010 patients, the results of which suggested that high expression of PLIN2 was significantly associated with poor MFS (Figure 2(d)). Similarly, three studies were performed to assess PFS in 473 patients (Figure 2(e)), and the results (pooled HR = 2.11; 95% CI = 1.55, 2.87; P < 0.0005, fixed effects model) indicated a significant correlation between high expression of PLIN2 and poor PFS. Finally, five studies that included 741 patients assessed RFS (pooled HR = 2.21; 95% CI = 1.64, 2.98; P < 0.0005, fixed effects model), which also suggested a significant association between high expression of PLIN2 and poor RFS of patients (Figure 2(f)).

Subgroup analysis for DFS and RFS

Similarly, we further optimized the predictive power of PLIN2 expression on patient prognostic outcomes by subgroup analysis for DFS and RFS. First, the DFS group was divided into digestive system malignancy and urinary system malignancy according to tumor types. The results in the digestive system malignancy group (pooled HR = 1.40; 95% CI = 0.60, 3.24; P = 0.436, random effects model) showed that high expression of PLIN2 was not significantly associated with DFS. In the group of urologic malignancy, high expression of PLIN2 was associated with better DFS (pooled HR = 0.55; 95% CI = 0.41, 0.74; P < 0.0005, fixed effects model) (Figure 3(c)). Second, in the analysis of the RFS sample size subgroup, the results of the group of <150 patients showed that the expression level of PLIN2 was not significantly correlated with RFS (pooled HR = 2.91; 95% CI = 0.95, 8.95; P = 0.062, fixed effects model). The results for groups with sample sizes ≥150 indicate that high expression of PLIN2 was significantly associated with poor RFS (pooled HR = 2.16; 95% CI = 1.58, 2.95; P < 0.0005, fixed effects model) (Figure 3(d)).

Relationship between PLIN2 expression and clinicopathological characteristics

We extracted clinicopathological parameters from the included studies, including age, distant metastasis, gender, lymph node metastasis, and tumor differentiation (Table S5 and Figure S1). Due to the large heterogeneity of the data, random effects models were used in all cases. Our findings showed that high expression of PLIN2 was not significantly associated with age (older vs. younger) (pooled OR = 1.32; 95% CI = 0.95, 1.83; P = 0.095), distant metastasis ( + vs. −) (pooled OR = 1.84; 95% CI = 0.76, 4.44; P = 0.177), gender (male vs. female) (pooled OR = 0.90; 95% CI = 0.55, 1.48; P = 0.685), lymph node metastasis ( + vs. −) (pooled OR = 1.16; 95% CI = 0.73, 1.83; P = 0.528) and tumor stage (GIII–IV vs. GI–II) (pooled OR = 1.52; 95% CI = 0.89, 2.57; P = 0.123).

Considering the heterogeneity of the pan-cancer data sources, we separately analyzed the related data of digestive system malignancies to further explore the correlation of clinicopathological parameters with a high expression of PLIN2. Due to the low heterogeneity, we used fixed effects models. The results (Table S5 and Figure S2) showed that high expression of PLIN2 was not significantly associated with age (older vs. younger) (pooled OR = 1.09; 95% CI = 0.56, 2.12; P = 0.808), and gender (male vs. female) (pooled OR = 0.93; 95% CI = 0.59, 1.45; P = 0.735). The results suggested that high PLIN2 was notably associated with distant metastasis ( + ) (pooled OR = 3.37; 95% CI = 1.31, 8.67; P = 0.012), and lymph node metastasis ( + ) (pooled OR = 1.61; 95% CI = 1.01, 2.54; P = 0.004) and tumor stage (GIII–IV) (pooled OR = 1.96; 95% CI = 1.24, 3.09; P = 0.006).

Publication bias and sensitivity analysis

Publication bias in the included studies was detected by drawing a funnel plot or by further performing Begg and Egger tests. For OS, the shape of the funnel plot showed asymmetry (Figure 4(a)), but the P-values of Begg test (Figure 4(b)) and Egger test (Figure 4(c)) were 0.371 and 0.120, respectively, indicating that PLIN2 expression does not have a significant publication bias for patients’ OS. Likewise, for DFS (Figure S3(a)), DSS (Figure S3(b)), MFS (Figure S3(c)), PFS (Figure S3(d)), and RFS (Figure S3(e)), the points on both sides of the funnel plot were evenly distributed, suggesting that there is no apparent publication bias between PLIN2 expression and these metrics.

Figure 4.

The funnel plot of publication bias of OS. (a) Funnel plot. (b) Begg's funnel plot. (c) Egger's publication bias plot. (d) Sensitivity analysis of the included studies concerning PLIN2 and OS.

To examine the effect of individual studies on the overall pooled results, a sensitivity analysis was performed to test the robustness of the results by sequentially excluding individual studies. In OS (Figure 4(d)) and DFS (Figure 4S(a)), when the Cao et al.⁴⁰ study was removed, a more significant effect was found in the pooled HR, suggesting that this study may make the aggregated HR less robust However, in DSS (Figure S4(b)), MFS (Figure S4(c)), PFS (Figure S4(d)), and RFS (Figure S4(e)), when all studies were sequentially deleted, no significant effect was found, suggesting that pooled HR is reliable.

Quality of evidence

The results of the GRADE instrument showed that the confidence of the main results of this systematic review was moderate to high, indicating that the estimated effect was close to the true effect, but there is a possibility that it is substantially different (Table S6).

Discussion

Analysis of results

In this review, the combined results showed that the high expression of PLIN2 in cancer patients was negatively correlated with OS, MFS, PFS, and RFS. Further subgroup analysis showed that, for OS, high expression of PLIN2 was associated with a worse prognosis in both the subgroup of the respiratory and digestive system malignant tumor and the subgroup of the large or small sample size. For RFS, high expression of PLIN2 in the subgroup with large sample size showed worse RFS. However, there was no significant correlation between the expression of PLIN2 and DFS and DSS. Notably, however, a subgroup of urologic malignancies showed that high expression of PLIN2 was inversely associated with bad DFS. Besides, the expression of PLIN2 in pan-cancer was not significantly associated with clinicopathological features (age, distant metastasis, gender, lymph node metastases, and tumor grade). However, in gastrointestinal malignancies, high PLIN2 expression was associated with distant metastases ( + ), lymph node metastasis ( + ), and stage III–IV tumors.

A key feature of lipogenic metabolism in tumor biology is the maintenance of sustained cell proliferation, and the metabolic mechanisms may differ across tumor types. PLIN2 is found at the start of LD formation and is upregulated concurrently with lipid storage.⁴⁹ It exerts tumorigenic or tumor suppressive effects in a tumor-dependent manner.⁴⁶ As mentioned above, PLIN2 is associated with a poor prognosis in most cancers. However, other studies, as well as our systematic review and meta-analysis, suggest that inhibition of PLIN2 promotes tumor cell proliferation, tissue entry, and distant metastasis in ccRCC, uterine leiomyoma,²⁹ and HB. First, the LD content in cancer cells is much higher than that in normal cells⁵⁰ because the proliferative metabolism of cancer cells requires an ever-expanding lipid substrate and a continuous energy source to increase the LD content. LDs as energy reservoirs can trigger the development of metastatic clones in aggressive cancers.⁵¹ PLIN2 is the only constitutive LD protein involved in LD synthesis.⁵² This reveals the mechanism by which PLIN2 promotes the development of advanced cancer and promotes the metastasis of cancer cells. Second, autophagy triggers intracellular degradation, a process that inhibits tumor cell proliferation,⁵³ and decreased autophagy increases cancer cell proliferation.⁵⁴ It has been shown that the activity of the intracellular AMPK/ULK1 pathway can be decreased by PLIN2 to inhibit autophagy or prevent autophagy by PLIN2 through other mechanisms that have not yet been elucidated.^42,55 Additionally, advanced solid tumors are more likely to suffer from hypoxia than normal tissues because of their low blood supply. Tumor cells can adapt to hypoxic conditions by adjusting metabolism and growth mechanisms, which are largely influenced by the hypoxia-inducible factor (HIF) 1 pathway.⁵⁶ The glycolysis and glucose uptake processes are activated by HIF1α protein, causing cancer cell proliferation. Some studies have observed that PLIN2 promotes the HIF1 signaling pathway by accelerating the production of the HIF1α protein.⁴² It has also been shown that choline kinases (CHKs) phosphorylate PLIN2 to dissociate it from LDs and to promote lipolysis of LD, β-oxidation and tumor growth.^31,57

Conversely, studies have shown that the expression of PLIN2 is higher in low-stage, low-grade, or von Hippel–Lindau (VHL) alteration-positive ccRCC.¹⁸ A study found that the prognosis of patients with ccRCC was better when the VHL mutation was detected in tumor cells, and VHL mutation may induce the overexpression of PLIN2, which may explain the low invasiveness of tumors overexpressing PLIN2.^18,33 Alterations in the VHL pathway in ccRCC are based on hypoxia, which further induces expression of PLIN2.^58,59 Furthermore, overexpression of PLIN2 has been shown to lead to lipid overload.⁴⁹ It has been shown that the protective overexpression of PLIN2 is a cellular defense mechanism against lipid overload, which can cause lipid toxicity and even cell apoptosis.⁶⁰ Therefore, PLIN2 may attenuate renal cell carcinoma (RCC) cell migration and invasion, acting as an anti-cancer gene.⁴⁰ Similarly, in HB, in hyperproliferative or chemotherapy-induced stressful environments, increased PLIN2-induced LDs leads to dysregulation of autophagy, enhanced tumor cell apoptosis, and ultimately a better prognosis.¹⁹ Other studies have shown that in uterine leiomyomas, PLIN2 gene expression is significantly reduced. When PLIN2 was knocked down, cell oxygen consumption and extracellular environment acidification were significantly increased. This results in a more vigorous metabolic basal activity and stress response of the cells, with a significant increase in proliferation.²⁹ In conclusion, the role of PLIN2 in tumor lipid biology should continue to be actively explored to explain the above paradoxical pathogenesis. Exploring the signaling pathways regulated by PLIN2 in various cancer models is an important verification direction in the future.

Important reasons for the heterogeneity

First, in order to explore the source of heterogeneity between studies, we performed subgroup analysis and sensitivity analysis. In the subgroup analysis, we found that the HR (95% CI) value of the Cao et al.⁴⁰ study in tumor subgroups of other systems showed better OS correlation with RCC. However, the HR values of the other three studies in this subgroup are all >1. Although the results are not statistically significant, it also indicates to some extent that the high expression of PLIN2 has a greater tendency to negatively correlate with OS. At the same time, we also performed a subgroup analysis in DFS, and the pooled results of two studies (both ccRCC) in the subgroup of urological malignancies suggested that the high expression of PILN2 was associated with better prognostic outcomes. In addition, in the sensitivity analysis for OS, our pooled results were statistically significant regardless of which study was deleted. However, we observed a significant increase in the pooled HR (95% CI) value when the study of Cao et al.⁴⁰ was deleted. In summary, we can reasonably suspect that studies of ccRCC are an important source of data heterogeneity. Second, subgroup analysis also found that studies with small sample sizes may also lead to heterogeneity in studies. Third, the analysis of PLIN2 and clinicopathological features indicated that in the pan-cancer analysis, various cancers may have different mechanisms for the occurrence and development of various clinicopathological features, and may also lead to heterogeneity. Our analysis of the association of digestive malignancies with clinicopathological features can counterevidence this point. Likewise, inconsistencies in study inclusion criteria and data collection processes across cancers, among other things, make it impossible to unify all confounding factors, which may be an unavoidable source of heterogeneity.

New discoveries and trends

Research on PLIN2 has never been interrupted in recent years, and current studies not only demonstrate that PLIN2 expression correlates with the prognosis of cancer patients, but can also be used as a diagnostic marker for early cancer detection.¹⁴ Recent research by Rios Garcia et al.⁶¹ demonstrated that PLIN2 levels can validate the responsiveness of tumor-targeting drugs. Additionally, studies have shown that PILN2 can serve as a biomarker for multiple age-related metabolic syndromes⁶² such as fatty liver, diabetes, cardiovascular disease, and sarcopenia, and more. Therefore, whether PLIN2 can be regarded as a potential pharmacological target against one or more of the above diseases could be considered. In addition, Jung et al.³² demonstrated that PLIN2 positivity was an independent factor for shorter OS and DFS in patients with a breast phyllodes tumor. The study by Tang et al.²⁸ demonstrated that PLIN2 is significantly associated with poor survival in patients with non-small cell lung cancer. Studies by Hoffman et al.³⁰ suggest that elevated PLIN2 can be detected in liposarcoma, etc. This gives us full confidence that PLIN2 can be used as an effective biomarker to indicate the prognosis of most cancer patients. However, our study also found that patients with high expression of PLIN2 in ccRCC had a better prognosis. In addition to the three articles on ccRCC included in this paper, the studies by Tolkach et al.³³ and Okeigwe et al.²⁹ came to the same conclusion. Because the current research data on the relationship between PLIN2 and RCC is insufficient, we were unable to conduct a further integrated analysis. More large-scale cohort studies are needed to explore the role of PLIN2 in different cancers. Other noteworthy studies have shown that urine PLIN2 rapid dipstick test, which is an economical, rapid, and promising test, can be used as a primary screening test for RCC, but the diagnostic efficacy and clinical practicability of the biomarker need to be further verified.^63,64 Efforts to develop truly non-invasive biomarkers for the diagnosis of early-stage renal cancer may be a key future direction.

Strengths and weaknesses

We conclude this first systematic review and meta-analysis of the association between PLIN2 and pan-cancer by strictly adhering to the recommendations of the guidelines for the meta-analysis of prognostic studies and by using the GRADE tool to assess the credibility of the overall results. To ensure that relevant studies were not omitted, we conducted multiple literature searches. In order to ensure the authenticity and accuracy of the data, we carefully read the full texts and the data in a number of proof-reading. Through reasonable subgroup analysis, we have come to more comprehensive and detailed conclusions. We have also achieved a satisfactory result. However, the study has some limitations. First, the adoption of different cutoff values between studies resulted in different positive correlation rates for PLIN2, the presence of confounding factors between various variables that could not be controlled uniformly, and so on—all of which may have introduced heterogeneity. Second, we excluded studies that were not published in English, which may have contributed to selection bias. Third, the concentration of research in Asia limits the global applicability of our findings.

Conclusion

Overexpression of PLIN2 is significantly associated with a poor prognosis in various human cancers, especially in respiratory and digestive malignancies. In addition, in malignant tumors of the digestive system, attention should be paid to monitoring the expression of PLIN2 in patients with advanced tumors, lymph node metastases, and distant metastasis in order to pursue early intervention. Thus, PLIN2 expression may be a potential prognostic biomarker in cancer patients.

Supplemental Material

sj-docx-1-jbm-10.1177_03936155221147536 - Supplemental material for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis

Supplemental material, sj-docx-1-jbm-10.1177_03936155221147536 for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis by Ming-Lin Li, Han-Yong Luo, Zi-Wei Quan, Le-Tian Huang and Jia-He Wang in The International Journal of Biological Markers

Supplemental Material

sj-docx-2-jbm-10.1177_03936155221147536 - Supplemental material for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis

Supplemental material, sj-docx-2-jbm-10.1177_03936155221147536 for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis by Ming-Lin Li, Han-Yong Luo, Zi-Wei Quan, Le-Tian Huang and Jia-He Wang in The International Journal of Biological Markers

Supplemental Material

sj-docx-3-jbm-10.1177_03936155221147536 - Supplemental material for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis

Supplemental material, sj-docx-3-jbm-10.1177_03936155221147536 for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis by Ming-Lin Li, Han-Yong Luo, Zi-Wei Quan, Le-Tian Huang and Jia-He Wang in The International Journal of Biological Markers

Supplemental Material

sj-docx-4-jbm-10.1177_03936155221147536 - Supplemental material for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis

Supplemental material, sj-docx-4-jbm-10.1177_03936155221147536 for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis by Ming-Lin Li, Han-Yong Luo, Zi-Wei Quan, Le-Tian Huang and Jia-He Wang in The International Journal of Biological Markers

Supplemental Material

sj-docx-5-jbm-10.1177_03936155221147536 - Supplemental material for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis

Supplemental material, sj-docx-5-jbm-10.1177_03936155221147536 for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis by Ming-Lin Li, Han-Yong Luo, Zi-Wei Quan, Le-Tian Huang and Jia-He Wang in The International Journal of Biological Markers

Supplemental Material

sj-docx-6-jbm-10.1177_03936155221147536 - Supplemental material for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis

Supplemental material, sj-docx-6-jbm-10.1177_03936155221147536 for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis by Ming-Lin Li, Han-Yong Luo, Zi-Wei Quan, Le-Tian Huang and Jia-He Wang in The International Journal of Biological Markers

Supplemental Material

sj-docx-7-jbm-10.1177_03936155221147536 - Supplemental material for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis

Supplemental material, sj-docx-7-jbm-10.1177_03936155221147536 for Prognostic and clinicopathologic significance of PLIN2 in cancers: A systematic review with meta-analysis by Ming-Lin Li, Han-Yong Luo, Zi-Wei Quan, Le-Tian Huang and Jia-He Wang in The International Journal of Biological Markers

Footnotes

Acknowledgments

Not applicable.

Author contributions

The final manuscript has been read and approved by all authors. JH-W, LT-H, and ML-L proposed this study after considerable deliberation. ML-L and HY-L gathered and integrated data for meta-analysis. ML-L, HY-L, ZW-Q, LT-H, and JH-W collaborated to compile the manuscript. ML-L and HY-L have contributed equally to this work and share first authorship

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 funded by grant number 2018YFC2002100, by National Key Research and Development Program of China, grant number 2018YFC2002104 and by Key Research and Development Program of Liaoning Province, grant number 2019JH8/10300021.

Ethical approval and informed consent

Not applicable.

Consent to publish

Not applicable.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request

ORCID iDs

Ming-Lin Li

Han-Yong Luo

Supplemental material

Supplemental material for this article is available online.

References

Kocarnik

Compton

Dean

, et al. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the global burden of disease study 2019. JAMA Oncol 2022; 8: 420–444.

Bray

Laversanne

Weiderpass

, et al. The ever-increasing importance of cancer as a leading cause of premature death worldwide. Cancer 2021; 127: 3029–3030.

Sung

Ferlay

Siegel

, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71: 209–249.

Wild

. The global cancer burden: necessity is the mother of prevention. Nat Rev Cancer 2019; 19: 123–124.

Crosby

Bhatia

Brindle

, et al. Early detection of cancer. Science 2022; 375: eaay9040.

Kimmel

Brasaemle

McAndrews-Hill

, et al. Adoption of PERILIPIN as a unifying nomenclature for the mammalian PAT-family of intracellular lipid storage droplet proteins. J Lipid Res 2010; 51: 468–471.

Orlicky

Degala

Greenwood

, et al. Multiple functions encoded by the N-terminal PAT domain of adipophilin. J Cell Sci 2008; 121: 2921–2929.

Brasaemle

Barber

Wolins

, et al. Adipose differentiation-related protein is an ubiquitously expressed lipid storage droplet-associated protein. J Lipid Res 1997; 38: 2249–2263.

Heid

Schnölzer

Keenan

. Adipocyte differentiation-related protein is secreted into milk as a constituent of milk lipid globule membrane. Biochem J 1996; 320: 1025–1030.

10.

Greenberg

Egan

Wek

, et al. Perilipin, a major hormonally regulated adipocyte-specific phosphoprotein associated with the periphery of lipid storage droplets. J Biol Chem 1991; 266: 11341–11346.

11.

Agarwala

Aneja

Kapoor

. Lipidomic landscape in cancer: actionable insights for membrane-based therapy and diagnoses. Med Res Rev 2022; 42: 983–1018.

12.

Liu

Luo

Halim

, et al. Targeting lipid metabolism of cancer cells: a promising therapeutic strategy for cancer. Cancer Lett 2017; 401: 39–45.

13.

Ambrosio

Piccaluga

Ponzoni

, et al. The alteration of lipid metabolism in Burkitt lymphoma identifies a novel marker: adipophilin. PLoS One 2012; 7: e44315.

14.

Matsubara

Honda

Ono

, et al. Identification of adipophilin as a potential plasma biomarker for colorectal cancer using label-free quantitative mass spectrometry and protein microarray. Cancer Epidemiol Biomarkers Prev 2011; 20: 2195–2203.

15.

Kurokawa

Matoba

Nakamori

, et al. PCR-array gene expression profiling of hepatocellular carcinoma. J Exp Clin Cancer Res 2004; 23: 135–141.

16.

Ostler

Prieto

Reed

, et al. Adipophilin expression in sebaceous tumors and other cutaneous lesions with clear cell histology: an immunohistochemical study of 117 cases. Mod Pathol 2010; 23: 567–573.

17.

Sun

Yang

Feng

, et al. The modification of ferroptosis and abnormal lipometabolism through overexpression and knockdown of potential prognostic biomarker perilipin2 in gastric carcinoma. Gastric Cancer 2020; 23: 241–259.

18.

Yao

Huang

Shioi

, et al. Expression of adipose differentiation-related protein: a predictor of cancer-specific survival in clear cell renal carcinoma. Clin Cancer Res 2007; 13: 152–160.

19.

Azukisawa

Zheng

Guo

, et al. The differential expression of perilipin-2 in hepatoblastoma and its association with prognosis. Histol Histopathol 2021; 36: 1169–1178.

20.

Riley

Moons

KGM

Snell

KIE

, et al. A guide to systematic review and meta-analysis of prognostic factor studies. Br Med J 2019; 364: k4597.

21.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. J Clin Epidemiol 2021; 134: 178–189.

22.

Moons

de Groot

Bouwmeester

, et al.

Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist

PLoS Med 2014; 11: e1001744.

23.

Tierney

Stewart

Ghersi

, et al. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials 2007; 8: 16.

24.

Hayden

van der Windt

Cartwright

, et al. Assessing bias in studies of prognostic factors. Ann Intern Med 2013; 158: 280–286.

25.

McShane

Altman

Sauerbrei

, et al. Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 2005; 97: 1180–1184.

26.

Higgins

Thompson

Deeks

, et al. Measuring inconsistency in meta-analyses. Br Med J 2003; 327: 557–560.

27.

Huguet

Hayden

Stinson

, et al. Judging the quality of evidence in reviews of prognostic factor research: adapting the GRADE framework. Syst Rev 2013; 2: 71.

28.

Tang

Zhao

Liu

, et al. Potentially functional genetic variants in PLIN2, SULT2A1 and UGT1A9 genes of the ketone pathway and survival of nonsmall cell lung cancer. Int J Cancer 2020; 147: 1559–1570.

29.

Okeigwe

Bulun

Liu

, et al. PLIN2 Functions as a novel link between progesterone signaling and metabolism in uterine leiomyoma cells. J Clin Endocrinol Metab 2019; 104: 6256–6264.

30.

Hoffman

Ghadimi

Demicco

, et al. Localized and metastatic myxoid/round cell liposarcoma: clinical and molecular observations. Cancer 2013; 119: 1868–1877.

31.

Liu

Lee

, et al. Choline kinase alpha 2 acts as a protein kinase to promote lipolysis of lipid droplets. Mol Cell 2021; 81: 2722–2735.e2729.

32.

Jung

Lee

Koo

. Expression of lipid metabolism-related proteins in breast phyllodes tumors. Neoplasma 2016; 63: 254–262.

33.

Tolkach

Lüders

Meller

, et al. Adipophilin as prognostic biomarker in clear cell renal cell carcinoma. Oncotarget 2017; 8: 28672–28682.

34.

Lucenay

Doostan

Karakas

, et al. Cyclin E associates with the lipogenic enzyme ATP-citrate lyase to enable malignant growth of breast cancer cells. Cancer Res 2016; 76: 2406–2418.

35.

Cadenas

Vosbeck

Edlund

, et al. LIPG-promoted lipid storage mediates adaptation to oxidative stress in breast cancer. Int J Cancer 2019; 145: 901–915.

36.

Yoshikawa

Ishida

Yanai

, et al. Adipophilin expression is an independent marker for poor prognosis of patients with triple-negative breast cancer: an immunohistochemical study. PLoS One 2020; 15: e0242563.

37.

Zhang

Sun

. Expression status and prognostic value of the perilipin family of genes in breast cancer. Am J Transl Res 2021; 13: 4450–4463.

38.

Yoshikawa

Ishida

Yanai

, et al. Prognostic significance of adipophilin expression in biopsy specimens of patients with triple-negative breast cancer. Oncol Lett 2022; 23: 127.

39.

Yao

Tabuchi

Nagashima

, et al. Gene expression analysis of renal carcinoma: adipose differentiation-related protein as a potential diagnostic and prognostic biomarker for clear-cell renal carcinoma. J Pathol 2005; 205: 377–387.

40.

Cao

Ruan

Wang

, et al. Overexpression of PLIN2 is a prognostic marker and attenuates tumor progression in clear cell renal cell carcinoma. Int J Oncol 2018; 53: 137–147.

41.

Fujimoto

Matsuzaki

Nishitsuji

, et al. Adipophilin expression in cutaneous malignant melanoma is associated with high proliferation and poor clinical prognosis. Lab Invest 2020; 100: 727–737.

42.

Liu

, et al. PLIN2 Promotes HCC cells proliferation by inhibiting the degradation of HIF1α. Exp Cell Res 2022; 418: 113244.

43.

Iwahashi

Ikezaki

Fujimoto

, et al. Lipid droplet accumulation independently predicts poor clinical prognosis in high-grade serous ovarian carcinoma. Cancers (Basel) 2021; 13: 5251–5265.

44.

Fujimoto

Yoshizawa

Sumiyoshi

, et al. Adipophilin expression in lung adenocarcinoma is associated with apocrine-like features and poor clinical prognosis: an immunohistochemical study of 328 cases. Histopathology 2016; 70: 232–241.

45.

Shin

Choe

, et al. The expression of adipophilin is frequently found in solid subtype adenocarcinoma and is associated with adverse outcomes in lung adenocarcinoma. J Pathol Transl Med 2018; 52: 357–362.

46.

Dong

Zhang

, et al. Lipid droplet-related PLIN2 in CD68( + ) tumor-associated macrophage of oral squamous cell carcinoma: implications for cancer prognosis and immunotherapy. Front Oncol 2022; 12: 824235.

47.

Hashimoto

Ishida

Ryota

, et al. Adipophilin expression is an indicator of poor prognosis in patients with pancreatic ductal adenocarcinoma: an immunohistochemical analysis. Pancreatology 2019; 19: 443–448.

48.

Hirai

Tada

Nakaguro

, et al. The clinicopathological significance of the adipophilin and fatty acid synthase expression in salivary duct carcinoma. Virchows Arch 2020; 477: 291–299.

49.

Listenberger

Ostermeyer-Fay

Goldberg

, et al. Adipocyte differentiation-related protein reduces the lipid droplet association of adipose triglyceride lipase and slows triacylglycerol turnover. J Lipid Res 2007; 48: 2751–2761.

50.

Zhang

Meng

Song

, et al. Roles of perilipins in diseases and cancers. Curr Genomics 2018; 19: 247–257.

51.

Liu

Luo

. Lipid droplet and its implication in cancer progression. Am J Cancer Res 2020; 10: 4112–4122.

52.

Bildirici

Schaiff

Chen

, et al. PLIN2 Is essential for trophoblastic lipid droplet accumulation and cell survival during hypoxia. Endocrinology 2018; 159: 3937–3949.

53.

Yazdani

Huang

Tsung

. Autophagy: Dual Response in the Development of Hepatocellular Carcinoma. Cells 2019; 8: 91–106.

54.

White

. The role for autophagy in cancer. J Clin Invest 2015; 125: 42–46.

55.

Tsai

Chen

, et al. The constitutive lipid droplet protein PLIN2 regulates autophagy in liver. Autophagy 2017; 13: 1130–1144.

56.

Singleton

Rouhi

Zois

, et al. Hypoxic regulation of RIOK3 is a major mechanism for cancer cell invasion and metastasis. Oncogene 2015; 34: 4713–4722.

57.

Arlauckas

Popov

Delikatny

. Choline kinase alpha-putting the ChoK-hold on tumor metabolism. Prog Lipid Res 2016; 63: 28–40.

58.

Saarikoski

Rivera

Hankinson

. Mitogen-inducible gene 6 (MIG-6), adipophilin and tuftelin are inducible by hypoxia. FEBS Lett 2002; 530: 186–190.

59.

Sztalryd

Kimmel

. Perilipins: lipid droplet coat proteins adapted for tissue-specific energy storage and utilization, and lipid cytoprotection. Biochimie 2014; 96: 96–101.

60.

Fitchev

Cornwell

, et al. FOXO3 Growth inhibition of colonic cells is dependent on intraepithelial lipid droplet density. J Biol Chem 2013; 288: 16274–16281.

61.

Rios Garcia

Meissburger

Chan

, et al. Trip13 depletion in liver cancer induces a lipogenic response contributing to Plin2-dependent mitotic cell death. Adv Sci (Weinh) 2022; 9: e2104291.

62.

Conte

Franceschi

Sandri

, et al. Perilipin 2 and age-related metabolic diseases: a new perspective. Trends Endocrinol Metab 2016; 27: 893–903.

63.

Gupta

Wang

, et al. Bioplasmonic paper-based assay for perilipin-2 non-invasively detects renal cancer. Kidney Int 2019; 96: 1417–1421.

64.

Flitcroft

Verheyen

Vemulkar

, et al. Early detection of kidney cancer using urinary proteins: a truly non-invasive strategy. BJU Int 2022; 129: 290–303.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB

0.02 MB

0.05 MB

0.03 MB

2.75 MB