AUTHOR=Wu Xianqiao , Chen Hang , Ge Zhen , Luo Binyu , Pan Hanbo , Shen Yiming , Xie Zuorun , Zhou Chengwei TITLE=A novel mitochondria-related algorithm for predicting the survival outcomes and drug sensitivity of patients with lung adenocarcinoma JOURNAL=Frontiers in Molecular Biosciences VOLUME=11 YEAR=2024 URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2024.1397281 DOI=10.3389/fmolb.2024.1397281 ISSN=2296-889X ABSTRACT=Background

Mitochondria have always been considered too be closely related to the occurrence and development of malignant tumors. However, the bioinformatic analysis of mitochondria in lung adenocarcinoma (LUAD) has not been reported yet.

Methods

In the present study, we constructed a novel and reliable algorithm, comprising a consensus cluster analysis and risk assessment model, to predict the survival outcomes and tumor immunity for patients with terminal LUAD.

Results

Patients with LUAD were classified into three clusters, and patients in cluster 1 exhibited the best survival outcomes. The patients in cluster 3 had the highest expression of PDL1 (encoding programmed cell death 1 ligand 11) and HAVCR2 (encoding Hepatitis A virus cellular receptor 2), and the highest tumor mutation burden (TMB). In the risk assessment model, patients in the low-risk group tended to have a significantly better survival outcome. Furthermore, the risk score combined with stage could act as a reliable independent prognostic indicator for patients with LUAD. The prognostic signature is a novel and effective biomarker to select anti-tumor drugs. Low-risk patients tended to have a higher expression of CTLA4 (encoding cytotoxic T-lymphocyte associated protein 4) and HAVCR2. Moreover, patients in the high-risk group were more sensitive to Cisplatin, Docetaxel, Erlotinib, Gemcitabine, and Paclitaxel, while low-risk patients would probably benefit more from Gefitinib.

Conclusion

We constructed a novel and reliable algorithm comprising a consensus cluster analysis and risk assessment model to predict survival outcomes, which functions as a reliable guideline for anti-tumor drug treatment for patients with terminal LUAD.