Multi-Omics Integrative Analysis of Lung Adenocarcinoma: An in silico Profiling for Precise Medicine

Abstract

Lung adenocarcinoma (LUAD) is one of the most common histological subtypes of lung cancer. The aim of this study was to construct consensus clusters based on multi-omics data and multiple algorithms. In order to identify specific molecular characteristics and facilitate the use of precision medicine on patients we used gene expression, DNA methylation, gene mutations, copy number variation data, and clinical data of LUAD patients for clustering. Consensus clusters were obtained using a consensus ensemble of five multi-omics integrative algorithms. Four molecular subtypes were identified. The CS1 and CS2 subtypes had better prognosis. Based on the immune and drug sensitivity predictions, we inferred that CS1 may be less responsive to immunotherapy and less sensitive to chemotherapeutic drugs. The high immune infiltration of CS2 cells may respond well to immunotherapy. Additionally, the CS2 subtype may also respond to EGFR molecular targeted therapy. The CS3 and CS4 subtypes were associated with poor prognosis. These two subtypes had more mutations, especially TP53 ones, as well as higher sensitivity to chemotherapeutics for lung cancer. However, CS3 was enriched in immune-related pathways and may respond to anti-PD1 immunotherapy. In addition, CS1 and CS4 were less sensitive to ferroptosis inhibitors. We performed a comprehensive analysis of the five types of omics data using five clustering algorithms to reveal the molecular characteristics of LUAD patients. These findings provide new insights into LUAD subtypes and potential clinical treatment strategies to guide personalized management and treatment.

Multi‐omics analysis reveals epigenetically regulated processes and patient classification in lung adenocarcinoma

“…In recent years, several studies in LUAD research have employed omics integration to achieve more refined patient stratification. For instance, in a comprehensive study by Ruan et al 51 five different omics data were integrated, including DNA methylation, to identify patient consensus clusters, followed by linking these subtypes to treatment strategies. In another study, 12 researchers explored the utility of DNA methylation data for LUAD patient classification, employing unsupervised bootstrap clustering.…”

Transcriptomic data analysis coupled with copy number aberrations reveals a blood-based 17-gene signature for diagnosis and prognosis of patients with colorectal cancer

“…We then validated its diagnostic and prognostic potential on an independent large cohort of CRC patients. Previous studies have demonstrated that multi-omics Frontiers in Genetics frontiersin.org analysis (using whole-genome gene expression profiling, copy number variations (CNVs), proteomics, metabolomics, and others) may lead to reliable biomarkers that are robust in disease classification and may also help identify cancer driver genes that are involved in tumor initiation and progression (Colak et al, 2010;Colak et al, 2013;Ohshima et al, 2017;Liu et al, 2021;Kaya et al, 2022;Ruan et al, 2022). Moreover, integrating omics data with the gene interaction networks has been shown to be a robust methodology that may lead to more reliable and accurate predictive biomarkers for human diseases (Al-Harazi et al, 2016;Ma et al, 2019;Khan et al, 2020;Seifert et al, 2020;Sinkala et al, 2020).…”

“…Since tumorigenesis genes show associations with copy number variations (CNVs) and expression levels, it is possible to increase the diagnostic reliability as well as the predictive potential of prognosis by integrating CNV and gene expression data (Sheng et al, 2011;Miao et al, 2014;Shao et al, 2019;Kaya et al, 2022). Indeed, the previous studies, including our own, reported that the multiomics approach may increase the robustness and reliability of biomarkers associated with complex diseases, including cancer (Miao et al, 2014;Aldosary et al, 2020;Das et al, 2020;Al-Harazi et al, 2021a;Baloni et al, 2021;Kaya et al, 2022;Ruan et al, 2022). Additionally, it has been reported that networkbased approaches have high efficacy in identifying biomarkers for many complex diseases, including several different types of cancer (Wang et al, 2017;Chen et al, 2019;Liu et al, 2019;Uddin et al, 2019;Khan et al, 2020;Al-Harazi et al, 2021b).…”

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

“…The conclusion is CHIAP2 is a potential target for lung adenocarcinoma. 132 Lu's published work in the Journal of Oncology discovered a prognostic biomarker for lung adenocarcinoma, characterized by a ferroptosisrelated long non-coding RNA (lncRNA) signature. Shen's published work in Europe PMC found that WWC2-AS2 is one of the four lncRNAs with reduced expression linked to the prognosis of lung adenocarcinoma in humans.…”