Identification of Mitotic Catastrophe-related Prognostic Genes in Lung Adenocarcinoma via Transcriptome Analysis

Summary

This study identifies eight mitotic catastrophe-related prognostic genes (PDGFB, LDHA, ZEB2, H2AX, FKBP4, DMD, ANXA2, S100B) in lung adenocarcinoma, establishes a risk model with acceptable predictive performance, and reveals distinct immune microenvironment characteristics between risk groups.

Abstract

Lung adenocarcinoma (LUAD), the most common lung cancer subtype, exhibits a poor prognosis. Although mitotic catastrophe can be induced in LUAD, leading to cell death, the prognostic and biological significance of mitotic catastrophe-related genes (MCRGs) in this disease remains unclear. Differentially expressed genes (DEGs) associated with LUAD were screened from the TCGA-LUAD cohort, and overlapping genes were defined by intersecting DEGs with MCRGs. Candidate prognostic genes were selected via machine learning, after which a multigene prognostic model was constructed. An eight-candidate prognostic signature comprising PDGFB, LDHA, ZEB2, H2AX, FKBP4, DMD, ANXA2, and S100B was established, demonstrating acceptable predictive reliability in training with 2-year, 3-year and 5-year area under the curves (AUCs) of 0.723, 0.715, and 0.625, respectively, in the training set (bootstrap-corrected C-index: 0.699, 95% CI: 0.657-0.742) and consistent validation in GSE31210 (AUCs: 0.812, 0.766, and 0.814). Low-risk patients exhibited significantly better survival outcomes, enhanced immune infiltration, higher immune and stromal scores, increased immunophenoscores, and more active cancer immunity cycles. Significant differences in tumor mutation burden and drug sensitivity were observed between the high- and low-risk groups. Quantitative polymerase chain reaction(qPCR) validated the transcriptomic profiles of six candidate outcome-related genes in clinical samples. A nomogram incorporating the risk score and clinical stage showed good predictive performance. These findings demonstrate that the eight MCRG-related candidate genes have strong potential to predict patient outcomes and stratify risk in LUAD, with distinct immune microenvironment characteristics between risk groups.

Introduction

Lung cancer remains among the most prevalent and deadly cancers worldwide^1,2. Non-small cell lung cancer (NSCLC) is dominated by lung adenocarcinoma (LUAD), which accounts for about 40% of all lung cancer cases^3,4. Although significant strides have been made in operative procedures, molecularly targeted therapies, immune checkpoint inhibitors, and combination regimens, the clinical outlook for LUAD patients remains unfavorable^5,6. Although pathological and imaging-based diagnostic approaches ar....

Protocol

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Anhui Chest Hospital (K2025-007) on 22 April 2025. Informed consent was obtained from all subjects involved in the study.

Data extraction and normalization 

Transcriptomic profiles and corresponding clinical datasets for LUAD were sourced from the TCGA and GEO cohorts. The TCGA-LUAD dataset was designated as the training set, with GSE72094, GSE31210, and GSE26939 serving as cohorts for external validation (Table 1). In addition, 900 MCRGs were collected f....

Results

Candidate gene identification and functional analysis

A total of 4,915 DEGs were detected, including 2,485 down-DEGs and 2,430 up-DEGs (Figure 1A). Functional module clustering analysis classified these DEGs into six expression modules (C1-C6), among which the C1, C2, C3, and C5 modules showed relatively higher expression in tumor samples (Figure 1B). Functional annotation indicated that these modules were linked to b.......

Discussion

LUAD is among the leading forms of lung cancer and continues to pose a considerable public health burden²¹. MC is a cell death-related process that may contribute to clearing cells with severe mitotic abnormalities and the suppression of tumor progression²². Therefore, exploring the potential role of MCRGs in LUAD may help identify prognostic biomarkers and improve understanding of LUAD biology. The study analyzed the prognostic relevance and potential functional significan.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
ClusterGVis	CRAN	version 0.1.2	Gene Expression Clustering and Visualization
Cytoscape	Cytoscape Consortium	version 3.9.1	Visualization of Protein Interaction Networks RRID:SCR_003032
CFX384 Real-Time Quantitative Fluorescent PCR System	Bio-Rad	CFX384 Touch	qPCR reagent
DMD-primer	Tsingke	N/A	Forward: 5’-GCTCAACCATCGATTTGCAGCC-3’ Reverse: 5’-TTCAGCCTCCAGTGGTTCAAGC-3’
FKBP4-primer	Tsingke	N/A	Forward: 5’-TGACTCCAGTCTGGATCGCAAG-3’ Reverse: 5’-CTGGTTTGCAGGTGATGTGGCA-3’
GEO	NCBI	Online Tool	Obtaining the LUAD External Validation Dataset RRID:SCR_005012
GAPDH-primer	Tsingke	N/A	Forward: 5’-GTCTCCTCTGACTTCAACAGCG-3’ Reverse: 5’-ACCACCCTGTTGCTGTAGCCAA-3’
glmnet	CRAN	version 4.1-8	Feature Selection in LASSO Regression RRID:SCR_015505
LDHA-primer	Tsingke	N/A	Forward: 5’-GGATCTCCAACATGGCAGCCTT-3’ Reverse: 5’-AGACGGCTTTCTCCCTCTTGCT-3’
limma	Bioconductor	version 3.60.6	DEG screening RRID:SCR_010943
maftools	Bioconductor	version 2.20.0	Visualization of Somatic Mutation Data RRID:SCR_024519
pRRophetic	CRAN	0.5	Prediction of Drug IC50 Values RRID:SCR_024417
PDGFB-primer	Tsingke	N/A	Forward: 5’-GAGATGCTGAGTGACCACTCGA-3’ Reverse: 5’-GTCATGTTCAGGTCCAACTCGG-3’
qPCR SYBR Green Master Mix	Vazyme	Q111-02	qPCR reagent
R software	R Foundation	version 4.2.2	Bioinformatics Analysis
regplot	CRAN	version 1.1	Creating Line Charts
survival	CRAN	version 3.8-3	Survival Analysis and Cox Regression RRID:SCR_021137
STRING	STRING Consortium	Online Tool	Protein Interaction Network Analysis RRID:SCR_005223
S100B-primer	Tsingke	N/A	Forward: 5’-GAAGAAATCCGAACTGAAGGAGC-3’ Reverse: 5’-TCCTGGAAGTCACATTCGCCGT-3’
survminer	CRAN	version 0.5.0	Survival Curve Plotting and Optimization RRID:SCR_021094
TRIzol	Tiangen	DP424	qPCR reagent
TCGA	NCI	Online Tool	LUAD Transcriptome and Clinical Data Acquisition RRID:SCR_003193
ZEB2-primer	Tsingke	N/A	Forward: 5’-AATGCACAGAGTGTGGCAAGGC-3’ Reverse: 5’-CTGCTGATGTGCGAACTGTAGG-3’