Machine learning models assess the roles of RAS subfamily genes in immunotherapy based on bladder cancer

doi:10.3877/cma.j.issn.1674-3253.2024.02.003

Abstract

Abstract:

Objective

To analyze the expression of RAS subfamily genes in bladder cancer, and explore the predictive effect of RAS subfamily genes on immunotherapy response.

Methods

This study was mainly based on the mRNA sequencing data of 407 cases of bladder cancer and 19 cases of adjacent normal tissues from the cancer genome atlas (TCGA) database. First, the expression differences of 35 RAS subfamily genes in tumor and normal tissues were compared at the transcriptional level, and the immunohistochemical staining images of bladder cancer and adjacent normal tissues were used in the human protein map database for protein level verification. Use univariate and multivariate Cox regression analysis to evaluate the prognostic role of RNA subfamily genes. GSE32894 data set 308 cases of bladder cancer were used as the validation set for prognostic analysis. Calculate the abundance of tumor immune infiltrating cells in TCGA tumor samples using the CIBERSORT algorithm. Based on the immunotherapeutic response results of 298 bladder cancer patients in IMvigor210 trial, random forest (RF) model was used to screen key genes, and logical regression (LR), support vector machine (SVM), and artificial neural network (ANN) models were used to verify. The single cell RNA sequencing data of one case of bladder cancer in GSE145140 dataset were used to evaluate the expression of key genes in the single cell of bladder cancer.

Results

The expression of lots of RAS subfamily genes were altered in bladder cancer and could be used as predictors of prognosis. RAS subfamily genes were strongly associated with M1 macrophage infiltration and co-expressed with some immune checkpoint molecules. RHEBL1, RRAD, GEM, RAP2B, HRAS, RERG, and NRAS were considered by the RF model to be the key genes that can best predict the response to immunotherapy.

Conclusion

This study preliminarily confirms that the expression of RAS subfamily genes can be used as prognostic factors for bladder cancer, and play a role in tumor immune cells infiltration and immunotherapy response.

Key words: Bladder cancer, TCGA, RAS subfamily genes, The Human Protein Atlas, Machine learning, Immunotherapy, PD-1/PD-L1, Immune checkpoint

Yicheng Huang, Haiqi Liang, Qihuan He, Faye Wei, Shubo Yang, Shuting Tan, Gaoqiang Zhai, Jiwen Cheng. Machine learning models assess the roles of RAS subfamily genes in immunotherapy based on bladder cancer[J]. Chinese Journal of Endourology(Electronic Edition), 2024, 18(02): 131-140.

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References 33

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基因及病理分型分期	单因素Cox回归分析		多因素Cox回归分析
基因及病理分型分期	HR(95%CI)	P值	HR(95%CI)	P值
RAP2A	1.3(1.0～1.6)	0.046	0.79(0.46～1.36)	0.394
DIRAS1	1.1(1.0～1.1)	0.022	1.12(0.97～1.30)	0.122
RASL10B	1.1(1.0～1.2)	0.007	1.22(1.01～1.48)	0.041
RASL10A	0.9(0.8～1.0)	0.017	0.81(0.66～1.00)	0.051
NKIRAS1	1.3(1.0～1.7)	0.018	1.01(0.63～1.61)	0.981
NKIRAS2	1.6(1.1～2.3)	0.013	2.12(1.02～4.40)	0.044
RASL12	1.1(1.0～1.2)	0.020	0.95(0.71～1.28)	0.756
GEM	1.1(1.0～1.2)	0.013	1.11(0.88～1.41)	0.386
DIRAS3	1.1(1.0～1.2)	0.012	0.94(0.74～1.20)	0.643
病理分型	0.7(0.5～1.0)	0.023	1.09(0.56～2.12)	0.811
病理M分期	3.3(1.6～6.9)	0.002	0.73(0.22～2.46)	0.608
病理N分期	1.6(1.3～1.9)	<0.001	1.53(0.90～2.58)	0.112
病理T分期	1.7(1.4～2.1)	<0.001	1.37(0.77～2.44)	0.292
TNM总体分期	1.7(1.4～2.1)	<0.001	0.94(0.45～1.95)	0.859

基因及病理分型分期	单因素Cox回归分析		多因素Cox回归分析
基因及病理分型分期	HR(95%CI)	P值	HR(95%CI)	P值
RAP2A	1.3(1.0～1.6)	0.046	0.79(0.46～1.36)	0.394
DIRAS1	1.1(1.0～1.1)	0.022	1.12(0.97～1.30)	0.122
RASL10B	1.1(1.0～1.2)	0.007	1.22(1.01～1.48)	0.041
RASL10A	0.9(0.8～1.0)	0.017	0.81(0.66～1.00)	0.051
NKIRAS1	1.3(1.0～1.7)	0.018	1.01(0.63～1.61)	0.981
NKIRAS2	1.6(1.1～2.3)	0.013	2.12(1.02～4.40)	0.044
RASL12	1.1(1.0～1.2)	0.020	0.95(0.71～1.28)	0.756
GEM	1.1(1.0～1.2)	0.013	1.11(0.88～1.41)	0.386
DIRAS3	1.1(1.0～1.2)	0.012	0.94(0.74～1.20)	0.643
病理分型	0.7(0.5～1.0)	0.023	1.09(0.56～2.12)	0.811
病理M分期	3.3(1.6～6.9)	0.002	0.73(0.22～2.46)	0.608
病理N分期	1.6(1.3～1.9)	<0.001	1.53(0.90～2.58)	0.112
病理T分期	1.7(1.4～2.1)	<0.001	1.37(0.77～2.44)	0.292
TNM总体分期	1.7(1.4～2.1)	<0.001	0.94(0.45～1.95)	0.859

基因	单因素Cox回归分析		多因素Cox回归分析
基因	HR(95%CI)	P值	HR(95%CI)	P值
RAP2A	1.8(0.9～3.6)	0.089	1.65(0.75～3.65)	0.214
RASL10A	1.5(1.0～2.3)	0.079	1.35(0.81～2.26)	0.243
NKIRAS1	1.3(0.6～2.8)	0.581	1.08(0.45～2.59)	0.871
NKIRAS2	3.5(1.3～10.0)	0.016	3.66(1.29～10.36)	0.015
RASL12	1.9(1.1～3.2)	0.021	1.31(0.62～2.77)	0.481
GEM	3.8(1.5～9.5)	0.005	2.15(0.64～7.24)	0.218
DIRAS3	1.4(0.1～19.0)	0.812	0.56(0.05～6.55)	0.645

基因	单因素Cox回归分析		多因素Cox回归分析
基因	HR(95%CI)	P值	HR(95%CI)	P值
RAP2A	1.8(0.9～3.6)	0.089	1.65(0.75～3.65)	0.214
RASL10A	1.5(1.0～2.3)	0.079	1.35(0.81～2.26)	0.243
NKIRAS1	1.3(0.6～2.8)	0.581	1.08(0.45～2.59)	0.871
NKIRAS2	3.5(1.3～10.0)	0.016	3.66(1.29～10.36)	0.015
RASL12	1.9(1.1～3.2)	0.021	1.31(0.62～2.77)	0.481
GEM	3.8(1.5～9.5)	0.005	2.15(0.64～7.24)	0.218
DIRAS3	1.4(0.1～19.0)	0.812	0.56(0.05～6.55)	0.645

染色强度	RRAS(例)		MRAS(例)
染色强度	肿瘤(n=12)	癌旁(n=2)	肿瘤(n=10)	癌旁(n=2)
阴性	6	0	0	0
弱	5	2	3	0
中性	1	0	5	2
强	0	0	2	0

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