Abstract Hepatocellular carcinoma (HCC) is known for its high invasiveness, high fatality rate. Both hypoxia and senescence play crucial roles in the initiation and progression of cancer, yet their prognostic implications in HCC are yet to be fully understood. The hypoxia-senescence co-related genes (HSCRGs) were screened from public databases. Transcriptome data and clinical information were obtained from patients with HCC using the Cancer Genome Atlas, GSE76427, and International Cancer Genome Consortium (ICGC). The random forest tree algorithm was used to identify the characteristic genes of the disease, and the genes were verified by related experiments. SVM algorithm was used to classify HCC patients based on HSCRGs. The prediction model based on HSCRGs was established by LASSO, univariate and multivariate COX regression analysis. We used the ICGC for outside validation. The risk score model was analyzed from subgroup analysis, immune infiltration, and functional strength. The expression patterns of key prognostic genes in tumor microenvironment were decoded by single cell analysis. A total of 184 HSCRGs were identified. The expression pattern and functional characteristics of MLH1 gene in HCC were verified. Two HCC subtypes were identified based on HSCRGs. Then, a prediction model based on HSCRGs was established, and risk score was identified as an independent prognostic indicator of HCC. A new nomogram is constructed and shows good prediction ability. We further determined that the level of infiltration of immune cells and the expression of immune checkpoints are significantly affected by the risk score. The immune microenvironment was different between the two risk groups. The high-risk group was dominated by immunosuppressed cells, and the prognosis was poor. Single-cell analysis revealed the expression of seven key prognostic genes in the tumor microenvironment. Finally, qPCR results further verified the expression levels of seven prognostic genes. HSCRGs are of great significance in the prognosis prediction, risk stratification and targeted therapy of patients with HCC.