Cisplatin resistance-related multi-omics differences and the establishment of machine learning models

Abstract

Objectives Platinum-based chemotherapies are currently the first-line treatment of non-small cell lung cancer. This study will improve our understanding of the causes of resistance to cisplatin, especially in lung adenocarcinoma (LUAD) and provide a reference for therapeutic decisions in clinical practice. Methods Cancer Cell Line Encyclopedia (CCLE), The Cancer Genome Atlas (TCGA) and Zhongshan hospital affiliated to Fudan University (zs-cohort) were used to identify the multi-omics differences related to platinum chemotherapy. Cisplatin-resistant mRNA and miRNA models were constructed by Logistic regression, classification and regression tree and C4.5 decision tree classification algorithm with previous feature selection performed via least absolute shrinkage and selection operator (LASSO). qRT-PCR and western-blotting of A549 and H358 cells, as well as single-cell Seq data of tumor samples were applied to verify the tendency of certain genes. Results 661 cell lines were divided into three groups according to the IC50 value of cisplatin, and the top 1/3 (220) with a small IC50 value were defined as the sensitive group while the last 1/3 (220) were enrolled in the insensitive group. TP53 was the most common mutation in the insensitive group, in contrast to TTN in the sensitive group. 1348 mRNA, 80 miRNA, and 15 metabolites were differentially expressed between 2 groups (P < 0.05). According to the LASSO penalized logistic modeling, 6 of the 1348 mRNAs, FOXA2, BATF3, SIX1, HOXA1, ZBTB38, IRF5, were selected as the associated features with cisplatin resistance and for the contribution of predictive mRNA model (all of adjusted P-values < 0.001). Three of 6 (BATF3, IRF5, ZBTB38) genes were finally verified in cell level and patients in zs-cohort. Conclusions Somatic mutations, mRNA expressions, miRNA expressions, metabolites and methylation were related to the resistance of cisplatin. The models we created could help in the prediction of the reaction and prognosis of patients given platinum-based chemotherapies.

HOXA1 is a radioresistance marker in multiple cancer types

“…Few studies have reported that HOXA1 regulates the radiosensitivity of cancer cells. However, it has been reported that high HOXA1 expression is correlated with cisplatin resistance of lung adenocarcinoma (34). In addition, many studies have reported that HOXA1 promoted proliferation and invasion of different cancer types (13,30,35).…”

A glycolysis-related gene signatures in diffuse large B-Cell lymphoma predicts prognosis and tumor immune microenvironment

“…The correlation analysis between gene expression and metabolites were performed. Metabolites with p -value < 0.05 and | cor | > 0.5 were selected in this study and shown in the heat map using “ggplot2” package in R software ( Sui et al, 2022 ).…”

Genomic characteristics of two breast malignant phyllodes tumors during pregnancy and lactation identified through whole-exome sequencing

“…TP53 is a classic tumor suppressor gene involved in many malignant tumors, often accompanied by advanced tumor grade and poor prognosis, with high proliferative and invasive ability and genomic instability[ 35 ]. TP53 mutation may cause resistance to platinum, fluorouracil and other chemotherapeutic drugs[ 36 , 37 ], and TP53 mutations may reduce radiotherapy sensitivity by promoting cell proliferation and metastasis[ 38 , 39 ]. This may be one of the reasons why Patient 1 progressed after 25 radiation treatments.…”