Integrating Multi-omics and Machine Learning Survival Frameworks to Build a Prognostic Model Based on Immune Function and Cell Death Patterns in a Lung Adenocarcinoma Cohort

Overview

Journal Front Immunol

Specialty Allergy & Immunology

Date 2024 Sep 30

PMID 39346927

Authors

Yiluo Xie

Huili Chen

Mei Tian

Ziqang Wang

Luyao Wang

Jing Zhang

Xiaojing Wang

Chaoqun Lian

Affiliations

Soon will be listed here.

Abstract

Introduction: The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD.

Methods: In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD.

Results: We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma.

Discussion: Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

Citing Articles

Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.

Hua M, Li T Sci Rep. 2025; 15(1):3075.

PMID: 39856156 PMC: 11760357. DOI: 10.1038/s41598-025-87419-4.

References

Sui S, An X, Xu C, Li Z, Hua Y, Huang G . An immune cell infiltration-based immune score model predicts prognosis and chemotherapy effects in breast cancer. Theranostics. 2020; 10(26):11938-11949. PMC: 7667685. DOI: 10.7150/thno.49451. View

Little A, Greer Gay E, Gaspar L, Stewart A . National survey of non-small cell lung cancer in the United States: epidemiology, pathology and patterns of care. Lung Cancer. 2007; 57(3):253-60. DOI: 10.1016/j.lungcan.2007.03.012. View

Tang D, Kang R, Vanden Berghe T, Vandenabeele P, Kroemer G . The molecular machinery of regulated cell death. Cell Res. 2019; 29(5):347-364. PMC: 6796845. DOI: 10.1038/s41422-019-0164-5. View

Zou Y, Xie J, Zheng S, Liu W, Tang Y, Tian W . Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery. Int J Surg. 2022; 107:106936. DOI: 10.1016/j.ijsu.2022.106936. View

Galluzzi L, Vitale I, Aaronson S, Abrams J, Adam D, Agostinis P . Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018. Cell Death Differ. 2018; 25(3):486-541. PMC: 5864239. DOI: 10.1038/s41418-017-0012-4. View

Hu X, Wang Z, Wang Q, Chen K, Han Q, Bai S . Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: A multi-omics consensus ensemble clustering. Biomed Pharmacother. 2021; 144:112222. DOI: 10.1016/j.biopha.2021.112222. View

Hoshida Y . Nearest template prediction: a single-sample-based flexible class prediction with confidence assessment. PLoS One. 2010; 5(11):e15543. PMC: 2990751. DOI: 10.1371/journal.pone.0015543. View

Kruiswijk F, Labuschagne C, Vousden K . p53 in survival, death and metabolic health: a lifeguard with a licence to kill. Nat Rev Mol Cell Biol. 2015; 16(7):393-405. DOI: 10.1038/nrm4007. View

Riaz N, Havel J, Makarov V, Desrichard A, Urba W, Sims J . Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab. Cell. 2017; 171(4):934-949.e16. PMC: 5685550. DOI: 10.1016/j.cell.2017.09.028. View

10.

Wang Z, Jensen M, Zenklusen J . A Practical Guide to The Cancer Genome Atlas (TCGA). Methods Mol Biol. 2016; 1418:111-41. DOI: 10.1007/978-1-4939-3578-9_6. View

11.

Wei W, Su Y . Function of CD8, conventional CD4, and regulatory CD4 T cell identification in lung cancer. Comput Biol Med. 2023; 160:106933. DOI: 10.1016/j.compbiomed.2023.106933. View

12.

Chen Y, Tang L, Huang W, Zhang Y, Abisola F, Li L . Identification and validation of a novel cuproptosis-related signature as a prognostic model for lung adenocarcinoma. Front Endocrinol (Lausanne). 2022; 13:963220. PMC: 9637654. DOI: 10.3389/fendo.2022.963220. View

13.

Van Allen E, Miao D, Schilling B, Shukla S, Blank C, Zimmer L . Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015; 350(6257):207-211. PMC: 5054517. DOI: 10.1126/science.aad0095. View

14.

Gibney G, Weiner L, Atkins M . Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol. 2016; 17(12):e542-e551. PMC: 5702534. DOI: 10.1016/S1470-2045(16)30406-5. View

15.

Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z . clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb). 2021; 2(3):100141. PMC: 8454663. DOI: 10.1016/j.xinn.2021.100141. View

16.

Yamamoto S, Iwakuma T . Regulators of Oncogenic Mutant TP53 Gain of Function. Cancers (Basel). 2018; 11(1). PMC: 6356290. DOI: 10.3390/cancers11010004. View

17.

Sun N, Luo Y, Zheng B, Zhang Z, Zhang C, Zhang Z . A novel immune checkpoints-based signature to predict prognosis and response to immunotherapy in lung adenocarcinoma. J Transl Med. 2022; 20(1):332. PMC: 9310422. DOI: 10.1186/s12967-022-03520-6. View

18.

Huang J, Zhang J, Zhang F, Lu S, Guo S, Shi R . Identification of a disulfidptosis-related genes signature for prognostic implication in lung adenocarcinoma. Comput Biol Med. 2023; 165:107402. DOI: 10.1016/j.compbiomed.2023.107402. View

19.

Mroz E, Tward A, Tward A, Hammon R, Ren Y, Rocco J . Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas. PLoS Med. 2015; 12(2):e1001786. PMC: 4323109. DOI: 10.1371/journal.pmed.1001786. View

20.

Wang X, Yu Q, Yu H, Wang Y, Sun L, Yu L . The prognostic value and multiomic features of m6A-related risk signature in lung adenocarcinoma. Am J Transl Res. 2022; 14(8):5379-5393. PMC: 9452311. View