Machine learning-based on cytotoxic T lymphocyte evasion gene develops a novel signature to predict prognosis and immunotherapy responses for kidney renal clear cell carcinoma patients

Abstract

BackgroundImmunotherapy resistance has become a difficult point in treating kidney renal clear cell carcinoma (KIRC) patients, mainly because of immune evasion. Currently, there is no effective signature to predict immunotherapy. Therefore, we use machine learning algorithms to construct a signature based on cytotoxic T lymphocyte evasion genes (CTLEGs) to predict the immunotherapy responses of patients, so as to screen patients effective for immunotherapy.MethodsIn public data sets and our in-house cohort, we used 10 machine learning algorithms to screen the optimal model with 89 combinations under the cross-validation framework, and 101 published signatures were collected. The relationship between the CTLEG signature (CTLEGS) and clinical variables was analyzed. We analyzed the role of CTLES in other types of cancer by pan-cancer analysis. The immune cell infiltration and biological characteristics were evaluated. Moreover, the response to immunotherapy and drug sensitivity of different risk groups were investigated. The key gene closely related to the signature was identified by WGCNA. We also conducted cell functional experiments and clinical tissue validation of key gene.ResultsIn public data sets and our in-house cohort, the CTLEGS shows good prediction performance. The CTLEGS can be regard as an independent risk factor for KIRC. Compared with 101 published models, our signature shows considerable superiority. The high-risk group has abundant infiltration of immunosuppressive cells and high expression of T cell depletion markers, which are characterized by immunosuppressive phenotype, minimal benefit from immunotherapy, and resistance to sunitinib and sorafenib. The CTLEGS was also strongly correlated with immunity in pan-cancer. Immunohistochemistry verified that T cell depletion marker LAG3 is highly expressed in high-risk groups in the clinical in-house cohort. The key CTLEG STAT2 can promote the proliferation, migration and invasion of KIRC cell.ConclusionsCTLEGS can accurately predict the prognosis of patients and their response to immunotherapy. It can provide guidance for the precise treatment of KIRC and help clinicians identify patients who may benefit from immunotherapy.

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning

“…Out of more than 130 models tested by us, the combination of Ridge and plsRcox resulted in highest accuracy and were used for building the final model. While both these algorithms have been used for cancer research, particularly for predicting therapy response, prognosis and identifying novel gene signatures, the usage of Ridge has been more common for AML ( White et al, 2021 ; Tang et al, 2022 ; Wei et al, 2022 ; Chen et al, 2023 ; Liu et al, 2022 ). When using ML for predicting the important prognostic genes for AML, TREML2 was one of the top 6 with high AUC of 0.9, further confirming its importance in hematological malignancies.…”