Abstract Objective A low incidence and mortality rate of cancer has been observed in high-altitude regions, suggesting a potential positive effect of low press and hypoxia (LPH) environment on cancer. Based on this finding, our study aimed to construct a pan-cancer prognosis risk model using a series of ADME genes intervened by low oxygen, to explore the impact of LPH environment on the overall survival (OS) of various kinds of cancers, and to provide new ideas and approaches for cancer prevention and treatment. Datasets and Measures The study used multiple sources of data to construct the pan-cancer prognosis risk model, including gene expression and survival data of 8,628 samples from the cancer genome atlas, and three gene expression omnibus databases were employed to validate the prediction efficiency of the prognostic model. The AltitudeOmics dataset was specifically used to validate the significant changes in model gene expression in LPH. To further identify the biomarkers and refine the model, various analytical approaches were employed such as single-gene prognostic analysis, weighted gene co-expression network analysis, and stepwise cox regression. And LINCS L1000, AutoDockTools, and STITCH were utilized to explore effective interacting drugs for model genes. Main Outcomes and Conclusions The study identified eight ADME genes with significant changes in the LPH environment to describe the prognostic features of pan-cancer. Lower risk scores calculated by the model were associated with better prognosis in 25 types of tumors, with a p-value of less than 0.05. The LPH environment was found to reduce the overall expression value of model genes, which could decrease the death risk of tumor prognosis. Additionally, it is found that the low-risk group had a higher degree of T cell infiltration based on immune infiltration analysis. Finally, drug exploration led to the identification of three potential model-regulating drugs. Overall, the study provided a new approach to construct a pan-cancer survival prognosis model based on ADME genes from the perspective of LPH and offered new ideas for future tumor prognosis research.
