Expression of m6A Reader Protein YTHDC2 in Non-small Cell Lung Cancer in a Chinese Clinical Cohort

Summary

This study evaluates YTHDC2 expression in non-small cell lung cancer using bioinformatics and qRT-PCR. While database analyses suggest downregulation and prognostic relevance, clinical validation shows no significant difference. Findings highlight inconsistencies, indicating limited diagnostic accuracy and the need for further validation before clinical application.

Abstract

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide, with limited biomarkers available for early diagnosis and prognosis. N6-methyladenosine (m6A) RNA modification and its reader proteins, such as YTHDC2, play critical roles in gene regulation and tumorigenesis. This study aims to evaluate the expression profile of YTHDC2 in NSCLC tissues using bioinformatics and quantitative real-time PCR (qRT-PCR), assess the relationship between YTHDC2 and clinicopathological characteristics, and explore its potential clinical and biological relevance for future research. Gene expression and survival data from databases were analyzed for prognostic accuracy. YTHDC2 expression was quantified in NSCLC tissues in a Chinese clinical cohort, and clinicopathological characteristics were analyzed. Public database analyses showed that YTHDC2 was downregulated in NSCLC tissues (p < 0.05) and was associated with patient survival, although its prognostic performance was poor (AUC ≈ 0.5). qRT-PCR analysis of 19 paired tumor and adjacent normal tissues showed no statistically significant difference (p = 0.537) in YTHDC2 expression between cancer and adjacent tissues. Collectively, public database analyses suggest that YTHDC2 may be downregulated and have potential prognostic relevance in NSCLC, but independent clinical validation in our cohort did not confirm significant differential expression. These findings highlight substantial heterogeneity between large-scale datasets and real-world cohorts, indicating that YTHDC2 is unlikely to serve as a reliable standalone diagnostic or prognostic biomarker and may require integration with additional molecular markers for clinical applicability.

Introduction

Non-small cell lung cancer (NSCLC), encompassing squamous cell carcinoma and adenocarcinoma, is the most frequently diagnosed lung cancer and a leading cause of global cancer mortality¹. Global cancer statistics show that lung cancer is one of the main causes of cancer deaths². Recent developments in diagnosis, surgery, radiotherapy, and molecular targeted therapy have not improved the prognosis of NSCLC significantly because the primary diagnosis occurs in the late stage, and the disease is often asymptomatic in the early stages³. This underscores the need for new biomarkers to identify, prognosticate, and assess treatment effectiveness in NSCLC patients.

m6A is the first identified and most abundant RNA modification in eukaryotes and has been implicated in RNA stability, translation, and transcription⁴. m6A modifications are dynamic and reversible, facilitated by methyltransferases (writers), demethylases (erasers), and specific binding proteins (readers)⁵. Of these, YTHDC2, a major m6A reader protein, modulates RNA stability and translation rate⁶. Recent studies indicate that YTHDC2 is implicated in the regulation of multiple cancer types, either by suppressing cell growth and invasion or by inducing cell death⁷. Studies have also highlighted the clinical relevance of other m6A reader proteins, including YTHDF1, YTHDF2, and YTHDC1, in lung cancer progression, immune regulation, and prognosis, underscoring the importance of systematically evaluating individual m6A readers in independent patient cohorts^8,9,10. In NSCLC, there are significantly low levels of YTHDC2, which has been associated with high tumor stages, lymph node metastasis, and poor prognosis for patients, indicating that it could be an anti-tumor factor¹¹. In vitro and in vivo experiments also reveal that high expression of YTHDC2 inhibits cell proliferation and metastasis in lung cancer⁷.

In this study, YTHDC2 regulation in NSCLC was investigated through integrated computational analyses and experimental validation via qRT-PCR. The relationship between YTHDC2 expression and clinicopathological characteristics and patient prognosis was also investigated. Despite increasing evidence from public databases suggesting dysregulation of YTHDC2 in NSCLC, published studies have reported inconsistent findings regarding the magnitude and clinical significance of YTHDC2 dysregulation, and independent validation in well-characterized real-world clinical cohorts, particularly in Asian populations, remains limited. Moreover, most previous studies have relied predominantly on public transcriptomic datasets or experimental models, with relatively few integrating bioinformatics findings with independent clinical validation. By combining large-scale public transcriptomic analyses with independent qRT-PCR validation in a Chinese clinical cohort, this study aimed to assess the reproducibility of prior findings and to address the translational gap between public database analyses and real-world clinical samples. Therefore, this study aimed to systematically evaluate YTHDC2 expression using integrated bioinformatics and clinical validation approaches and to assess its potential biological and clinical relevance in NSCLC. It was hypothesized that YTHDC2 expression is dysregulated in NSCLC, and that integrating bioinformatics analyses with independent clinical validation would provide a more reliable assessment of its diagnostic and prognostic significance than either approach alone.

Protocol

The study was approved by the Ethical Committee of Hainan Medical University (approval number HMC1984.24) and was in accordance with the Declaration of Helsinki (as revised in 2013).

Study subjects

A study of 50 lung cancer patients diagnosed between 2017 and 2024 at Sanya Central Hospital in Hainan Province was conducted using the Chinese Medical Association Clinical Guidelines for the Diagnosis and Treatment of Lung Cancer (2024 Edition). CSCO staging (2024) aligned with the AJCC 8th Edition for cross-study comparability, as validated in Chinese cohorts¹², with two independent oncologist reviews to ensure consistency. The patient selection process, tissue availability, RNA quality assessment, and final sample inclusion for qRT-PCR analysis are summarized in Figure 1.

Study data

This study used bioinformatics to analyze YTHDC2 expression levels in NSCLC and their relationship with clinicopathological features, offering insights into potential mechanisms. Bioinformatics analyses were performed exclusively using publicly available RNA sequencing data from The Cancer Genome Atlas (TCGA), including lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and corresponding normal lung tissue samples downloaded through the GDC Data Portal (https://portal.gdc.cancer.gov/). The downloaded dataset included RNA sequencing expression data together with available clinical variables (patient identifier, sample identifier, age, sex, pathological stage, survival status, and overall survival time) for eligible TCGA-LUAD and TCGA-LUSC cases. Samples lacking gene expression or survival information were excluded from downstream survival and ROC analyses. The dataset used in this study is provided as Supplementary File 1. No clinical specimens collected at Sanya Central Hospital were used for the bioinformatics analyses. Detailed procedures for the GEPIA, Kaplan–Meier Plotter, and survivalROC analyses are provided in the Bioinformatics analysis section below. The complete bioinformatics workflow, including data acquisition, preprocessing, gene expression analysis, survival analysis, and ROC analysis, is summarized in Figure 2.

Inclusion and exclusion criteria

The inclusion and exclusion criteria of the patients are presented in Table 1. A power analysis using statistical power analysis software determined that at least 26 cases per group would provide 80% power to detect a moderate effect size (d = 0.8, α = 0.05, two-tailed). While initial enrollment aimed for 50 pairs in the lung cancer group, the final qRT-PCR analysis included 30 tumor samples and 19 normal adjacent tissue samples after exclusions for tissue or RNA quality. This reduction in sample size reflects real-world clinical constraints and highlights the importance of RNA integrity and tissue availability in translational studies. At n = 19 for comparisons, the minimum detectable effect size is d = 1.0 (80% power, α = 0.05). Thus, the experiment was sufficiently powered to detect large, but not small-to-moderate, differences in YTHDC2 expression.

Tissue specimens and quantitative real-time PCR (qRT-PCR)

The pathological diagnoses were determined in a double-blind manner by two different pathologists. Tumor purity was estimated by pathologists (>70% malignant cells) and confirmed via ESTIMATE (TCGA). Adjacent normal tissues were macrodissected to minimize stromal contamination. The histological types of lung cancer included lung adenocarcinoma and squamous cell carcinoma, with 26 cases of lung adenocarcinoma and 4 cases of squamous cell carcinoma. The clinical staging of the 50 lung cancer patients was conducted according to the staging criteria outlined in “Chinese Medical Association Clinical Guidelines for Lung Cancer (2024 Edition)”. Pathologically confirmed NSCLC specimens (n = 50) represented typical clinical distributions (see Table 2). Of the 50 patients initially enrolled, 30 tumor tissue samples and 19 matched adjacent normal tissue samples met the RNA quality criteria. Because paired statistical analysis requires matched specimens from the same patient, the comparison of tumor versus adjacent normal tissue expression was performed using the 19 available matched pairs. These clinical specimens were used exclusively for experimental validation by qRT-PCR and were analyzed independently of the public TCGA datasets used for bioinformatics analysis. These selected specimens were processed immediately after pathological confirmation and handled under RNase-free conditions before RNA extraction. This subset reflects the cases in which both adequate tissue quantity and high-quality total RNA (RNA integrity number, RIN >7.0) could be obtained. Total RNA concentration and purity were measured before reverse transcription, and only samples with adequate RNA quality (RIN >7.0) were included for downstream analysis. Equal amounts of total RNA were reverse-transcribed into complementary DNA (cDNA) according to the manufacturer's protocol prior to quantitative PCR. This process ensured the validity of the gene expression data analyzed. The qRT-PCR experiments were carried out on a real-time PCR thermocycler using a probe-based quantitative PCR assay. All reactions were performed in triplicate, together with no-template controls to ensure analytical reproducibility. PCR amplification was performed under the following cycling conditions: an initial enzyme activation/denaturation step at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 s and annealing/extension at 60°C for 60 s. Fluorescence signals were acquired at the end of each amplification cycle. All reagents and consumables were obtained from commercial suppliers (see Table of Materials). The primer and probe sequences used in qRT-PCR are provided in Table 3.

Bioinformatics analysis

GEPIA database analysis of YTHDC2 gene expression

The GEPIA database was used to analyze YTHDC2 expression in NSCLC. The GEPIA web server (http://gepia.cancer-pku.cn/) was accessed through a web browser. The Expression DIY module was selected, the gene symbol "YTHDC2" was entered, the LUAD and LUSC datasets were selected, the default normalization parameters were retained, and differential expression box plots were generated directly through the GEPIA interface. Statistical significance was defined as p < 0.05.

Kaplan-Meier plotter database for survival analysis of lung cancer patients

The study analyzed the relationship between YTHDC2 expression and prognosis in lung cancer patients using the Kaplan-Meier Plotter database. The lung cancer dataset was selected, the gene symbol "YTHDC2" was entered, the auto-selected best cutoff option was applied, and Kaplan–Meier curves for overall survival and post-progression survival were generated using the default analysis settings. Patients were automatically stratified into high- and low-expression groups using the optimal cutoff determined by the Kaplan–Meier Plotter platform, and hazard ratios with corresponding 95% confidence intervals were generated using the platform's default settings.

Running R packages in R software for ROC curve plotting

RNA sequencing expression data and the corresponding clinical metadata for eligible TCGA-LUAD and TCGA-LUSC cases were downloaded from the GDC Data Portal. The downloaded datasets were merged by patient identifier and imported into R for downstream analyses. The survivalROC package was used to generate time-dependent ROC curves at 1-, 3-, and 5-year prediction time points, and the corresponding area under the curve (AUC) values were calculated to evaluate the prognostic performance of YTHDC2 expression. Only TCGA-LUAD and TCGA-LUSC patients with available RNA-seq expression and survival information were included in the survival ROC analysis. The local clinical cohort was not used for survival prediction because long-term follow-up data were unavailable.

Tissue YTHDC2 expression by qRT-PCR

To analyze gene expression levels, qRT-PCR was performed. Equal volumes of cDNA were added to each reaction according to the manufacturer's recommended reaction conditions. Amplification was performed using a probe-based quantitative PCR assay, and fluorescence data were collected automatically at the end of each amplification cycle. Briefly, the RNA extraction process involved several steps, including sample preparation, deparaffinization, removal of residual liquid, proteinase K digestion, incubation, centrifugation, DNase treatment, DNase I addition, and ethanol precipitation. The sample was then bound to a silica-based RNA purification column and centrifuged at 8,000 × g for 30 s. The column was then washed with wash buffer 1, wash buffer 2, and wash buffer 2 diluted with ethanol, and dried at 13,000 × g for 2 min. The RNA was then eluted by adding 70 µL of RNase-Free Water to the center of the column membrane, followed by centrifugation at 13,000 × g for 1 min. Each primer pair demonstrated a single amplification product, which was confirmed by melting-curve analysis before relative gene expression was calculated. The primer efficiency (90–110%) was validated using standard curves prior to sample analysis. Melting curve analyses confirmed the presence of single amplicons and the absence of primer dimers. Relative YTHDC2 expression was calculated using the 2^-ΔCt method, in which Ct values were normalized to the endogenous reference gene GAPDH. Because expression values were presented as normalized expression levels rather than fold changes relative to a calibrator sample, the results are reported as 2^−ΔCt values. GAPDH was selected as the housekeeping gene because its expression showed minimal variability (CV < 5%) compared with the tested alternatives (ACTB, CV = 12%; 18S rRNA, CV = 18%), consistent with reference gene selection criteria in m6A studies.

Statistical analysis

Statistical analyses were performed using statistical software, including the creation of graphics and charts. To analyze quantitative and categorical data on the YTHDC2 gene expression in NSCLC patients, R software was used for bioinformatics analyses and ROC curve generation. ROC curves and AUC were used to evaluate the diagnostic performance of YTHDC2 expression in predicting survival. For regression-based analyses, effect estimates (odds ratios) with corresponding 95% confidence intervals were reported where applicable. The Wilcoxon signed-rank test was used to compare YTHDC2 expression between paired tumor and adjacent normal tissue samples, while Pearson correlation analysis was used to assess the association between YTHDC2 expression and clinicopathological features. Correlation coefficients (r) and corresponding p-values were reported. Because the qRT-PCR data consisted of paired tumor and adjacent normal tissue samples from the same patients and the gene expression data were not normally distributed, the Wilcoxon signed-rank test was used to compare YTHDC2 expression levels between paired tissues. This test does not assume normality of the data and is commonly used for skewed biological data. All statistical tests were two-sided, and p <0.05 was considered statistically significant. Continuous variables were assessed for normality before analysis. Continuous variables are presented as mean ± standard deviation or median (interquartile range), as appropriate.

Results

Expression of the YTHDC2 gene in tumors based on the TCGA database

Figure 3 illustrates the association between NSCLC and YTHDC2 by examining YTHDC2 expression levels across different cancers using the GDC tool in the TCGA database.

Expression of the YTHDC2 gene in NSCLC in the GEPIA database

483 lung adenocarcinoma tissue samples, 347 normal lung tissue samples, 486 lung squ...

Discussion

YTHDC2, the largest N6-methyladenosine (m6A) binding protein in the YTH family, is unique for its ATP-dependent RNA helicase activity, distinguishing it as a key regulator of RNA metabolism¹³. Unlike other YTH family members, YTHDC2 is found in both the nucleus and the cytoplasm, where it regulates mRNA translation and RNA stability¹⁴. Previous experimental studies have shown that YTHDC2 suppresses lung adenocarcinoma (LUAD) progression by inhibiting ADIRF and MRPL12 mRNA e...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Custom Primers and Probes	Sangon Biotech Co., Ltd., Shanghai, China	Custom synthesis	Primer and probe sequences listed in Table 6
DNase I	Magen Biotechnology Co., Ltd., Guangzhou, China	DNase I (HiPure FFPE RNA Kit)	Removal of genomic DNA contamination
ESTIMATE algorithm	Yoshihara et al.	R package (Version 1.0.13)	Tumor purity estimation
FFPE RNA Extraction Kit	Magen Biotechnology Co., Ltd., Guangzhou, China	HiPure FFPE RNA Kit (R4130-02)	RNA extraction from FFPE tissue
GDC Data Portal	National Cancer Institute (NCI), NIH, USA	https://portal.gdc.cancer.gov	Download of TCGA datasets
GEPIA web server	Peking University, China	http://gepia.cancer-pku.cn	Gene expression analysis
Kaplan–Meier Plotter	Semmelweis University, Hungary	https://kmplot.com	Survival analysis
Microcentrifuge	Eppendorf AG, Hamburg, Germany	5424 R	RNA purification procedures
NanoDrop Spectrophotometer	Thermo Fisher Scientific, Waltham, MA, USA	NanoDrop 2000/2000c	RNA quantification
Probe-based qPCR Master Mix	Thermo Fisher Scientific, Waltham, MA, USA	TaqMan Universal PCR Master Mix II (4440040)	Quantitative PCR amplification
R Statistical Software	R Foundation for Statistical Computing, Vienna, Austria	Version 4.4.1	Statistical analyses and ROC analysis
Real-time PCR System	Bio-Rad Laboratories, Hercules, CA, USA	CFX Opus 96	Quantitative real-time PCR
Reverse Transcription Kit	Thermo Fisher Scientific, Waltham, MA, USA	RevertAid First Strand cDNA Synthesis Kit (K1622)	cDNA synthesis
RNA Purification Column	Magen Biotechnology Co., Ltd., Guangzhou, China	HiPure Mini Column I	Silica membrane RNA purification column
RNase-Free Water	Magen Biotechnology Co., Ltd., Guangzhou, China	RNase-Free Water	RNA elution
SPSS Statistics	IBM Corp., Armonk, NY, USA	IBM SPSS Statistics Version 27.0	Statistical analyses
survivalROC package	CRAN (R Foundation for Statistical Computing)	Version 1.0.3.1	Time-dependent ROC analysis
TCGA database	National Cancer Institute (NCI), NIH, USA	https://portal.gdc.cancer.gov	Public cancer genomics database
Wash Buffer RW1	Magen Biotechnology Co., Ltd., Guangzhou, China	Buffer RW1	RNA purification wash buffer
Wash Buffer RW2	Magen Biotechnology Co., Ltd., Guangzhou, China	Buffer RW2	RNA purification wash buffer