Integrative analysis and prognostication in gastric cancer: unveiling the role of mitochondrial genomics with the MLRScore model

Gastric cancer, a leading cause of cancer-related mortality globally, presents significant challenges in prognosis and treatment due to its heterogeneity. This study aimed to elucidate the role of mitochondrial-related genes (MRGs) in gastric cancer and develop a prognostic model. We analyzed RNA sequencing data and clinical information of 412 gastric cancer samples from The Cancer Genome Atlas (TCGA). A comprehensive list of 1136 MRGs was curated from the MitoCarta3.0 database, leading to the identification of 110 differentially expressed MRGs between gastric cancer and normal tissues. Using univariate and multivariate Cox regression analyses, we constructed the Mitochondrial-Related Risk Score (MLRScore), a prognostic model incorporating five key MRGs. The model was validated in training and testing cohorts and exhibited promising prognostic capability. Additionally, we investigated the relationship between MLRScore and immune cell infiltration, somatic mutations, tumor mutation burden (TMB), and response to chemotherapy. The MLRScore was found to correlate with distinct immune landscapes and chemotherapeutic sensitivities, suggesting its potential utility in guiding personalized treatment strategies. Our study not only provides a novel tool for prognostic assessment in gastric cancer but also underscores the importance of mitochondrial dynamics in tumor biology and patient stratification.

Authors

Institutions

Publication Details

Journal
Discover Oncology
Published
2025-04-05
DOI
https://doi.org/10.1007/s12672-025-02203-0
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrative analysis and prognostication in gastric cancer: unveiling the role of mitochondrial genomics with the MLRScore model

Jun Quan, Wen Yue, Lie Yang, Yulan Guo et al.
Discover Oncology
Cancer-related molecular mechanisms research
article

Integrative analysis and prognostication in gastric cancer: unveiling the role of mitochondrial genomics with the MLRScore model

Jun Quan, Wen Yue, Lie Yang, Yulan Guo, Peng Chen, Peng Liu, Yuming Wen, Yong Wang, Chun-Yan Lu, Xiaohong Cheng, Cao Li
article en

Abstract

Gastric cancer, a leading cause of cancer-related mortality globally, presents significant challenges in prognosis and treatment due to its heterogeneity. This study aimed to elucidate the role of mitochondrial-related genes (MRGs) in gastric cancer and develop a prognostic model. We analyzed RNA sequencing data and clinical information of 412 gastric cancer samples from The Cancer Genome Atlas (TCGA). A comprehensive list of 1136 MRGs was curated from the MitoCarta3.0 database, leading to the identification of 110 differentially expressed MRGs between gastric cancer and normal tissues. Using univariate and multivariate Cox regression analyses, we constructed the Mitochondrial-Related Risk Score (MLRScore), a prognostic model incorporating five key MRGs. The model was validated in training and testing cohorts and exhibited promising prognostic capability. Additionally, we investigated the relationship between MLRScore and immune cell infiltration, somatic mutations, tumor mutation burden (TMB), and response to chemotherapy. The MLRScore was found to correlate with distinct immune landscapes and chemotherapeutic sensitivities, suggesting its potential utility in guiding personalized treatment strategies. Our study not only provides a novel tool for prognostic assessment in gastric cancer but also underscores the importance of mitochondrial dynamics in tumor biology and patient stratification.

Discover OncologyVol. 16(1)
Sichuan University (CN), West China Hospital of Sichuan University (CN)
Good health and well-being
Openalex Percentile: Top 92%
Cancer-related molecular mechanisms research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.