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Background <p>Over the past decade targeted therapies in cancer plays a significant role in transforming the treatment outcomes of cancer. The success of this endeavour can be attributed to the advancements made in sequencing technology, which have significantly enhanced our understanding of the genetic drivers and mutational landscape of human cancers. One of the major challenges is the precise identification of somatic mutations in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cohorts. Several techniques have been developed for identifying different mutations, including MuTect2, MuSE, Varscan2, and SomaticSniper.</p> Aim <p>This study aimed to compare four somatic mutation–calling techniques (MuSE, Mutect2, Varscan2, and SomaticSniper) on TCGA-LUAD and TCGA-LUSC datasets to identify key mutated genes and prognostic signatures.</p> Methods <p>To identify somatic mutations in the LUAD and LUSC cohorts, we extracted mutation annotation format (MAF) files from the cancer genome atlas (TCGA) for our investigation. The top 30 highly mutated genes corresponding to each mutation calling technique across both cohorts were compiled, followed by overall survival (OS) analysis to separate patients into high- and low-risk groups according to their prognostic signature. Lastly, the top 10 significant pathway and gene ontology (GO) terms were identified for our prognostic signature. Expression and stage-wise analyses of the prognostic signature were performed using the GEPIA2 web-based tool.</p> Results <p>A multiple-gene-based univariate OS analysis revealed a prognostic signature (i.e., TP53, TTN, COL11A1, CSMD3, RYR2) across the LUSC cohort. The most significant pathways and GO terms corresponding to our prognostic signature were the activation of NOXA and its translocation to mitochondria, as well as striated muscle hypertrophy, protease binding, and junctional sarcoplasmic reticulum membrane.</p> Conclusions <p>This comparative study highlights the variability in somatic mutation detection across different mutation-calling tools and identifies a robust prognostic signature (TP53, TTN, COL11A1, CSMD3, RYR2) in LUSC. These findings provide a foundation for refining mutation-based prognostic models and may facilitate early detection, risk stratification, and targeted therapy development in lung cancer. Further experimental validation is warranted to confirm the biological role of these genes in disease progression.</p>

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Molecular subtyping based on RYR2 correlates prognosis and tumor mutation burden in lung squamous cell carcinoma patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月27日

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  • Prithvi Singh,
  • Asifa Khan,
  • Mohammad Masood,
  • Shivani Tyagi,
  • Nitesh Shriwash,
  • Bhupender Kumar,
  • Pramod Katara,
  • Mohammad Mahfuzul Haque,
  • Ravins Dohare

摘要

Background

Over the past decade targeted therapies in cancer plays a significant role in transforming the treatment outcomes of cancer. The success of this endeavour can be attributed to the advancements made in sequencing technology, which have significantly enhanced our understanding of the genetic drivers and mutational landscape of human cancers. One of the major challenges is the precise identification of somatic mutations in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cohorts. Several techniques have been developed for identifying different mutations, including MuTect2, MuSE, Varscan2, and SomaticSniper.

Aim

This study aimed to compare four somatic mutation–calling techniques (MuSE, Mutect2, Varscan2, and SomaticSniper) on TCGA-LUAD and TCGA-LUSC datasets to identify key mutated genes and prognostic signatures.

Methods

To identify somatic mutations in the LUAD and LUSC cohorts, we extracted mutation annotation format (MAF) files from the cancer genome atlas (TCGA) for our investigation. The top 30 highly mutated genes corresponding to each mutation calling technique across both cohorts were compiled, followed by overall survival (OS) analysis to separate patients into high- and low-risk groups according to their prognostic signature. Lastly, the top 10 significant pathway and gene ontology (GO) terms were identified for our prognostic signature. Expression and stage-wise analyses of the prognostic signature were performed using the GEPIA2 web-based tool.

Results

A multiple-gene-based univariate OS analysis revealed a prognostic signature (i.e., TP53, TTN, COL11A1, CSMD3, RYR2) across the LUSC cohort. The most significant pathways and GO terms corresponding to our prognostic signature were the activation of NOXA and its translocation to mitochondria, as well as striated muscle hypertrophy, protease binding, and junctional sarcoplasmic reticulum membrane.

Conclusions

This comparative study highlights the variability in somatic mutation detection across different mutation-calling tools and identifies a robust prognostic signature (TP53, TTN, COL11A1, CSMD3, RYR2) in LUSC. These findings provide a foundation for refining mutation-based prognostic models and may facilitate early detection, risk stratification, and targeted therapy development in lung cancer. Further experimental validation is warranted to confirm the biological role of these genes in disease progression.