The Potential of Low Press and Hypoxia Environment in Assisting Pan-cancer Treatment
Hu, X.; Chen, X.; Sun, M.; Wang, X.; Hu, Z.; Zhang, S.
Show abstract
ObjectiveA 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 MeasuresThe 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 ConclusionsThe 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.
Matching journals
The top 13 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Mapping of single-cell landscape of acral melanoma and analysis of molecular regulatory network of tumor microenvironment 95%
- A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer 95%
- Multi-gradient Permutation Survival Analysis Identifies Mitosis and Immune Signatures Steadily Associated with Cancer Patient Prognosis 95%
Similar papers in this journal
- Identification of cuproptosis and ferroptosis-related subtypes and development of a prognostic signature in colon cancer 97%
- Research of the mechanism on miRNA193 in exosomes promotes cisplatin resistance in esophageal cancer cells 96%
- Bioinformatics analysis of immune-related prognostic genes and immunotherapy in renal clear cell carcinoma 95%
Similar papers in this journal
- Establishment of a prognosis prediction model based on pyroptosis-related signatures associated with the immune microenvironment and molecular heterogeneity in clear cell renal carcinoma 97%
- Pyroptosis-related gene signatures can robustly diagnose skin cutaneous melanoma and predict the prognosis 95%
- BNIP3 upregulation characterizes cancer cell subpopulation with increased fitness and proliferation 94%
Similar papers in this journal
- Classification of clear cell renal cell carcinoma based on PKM alternative splicing 95%
- Atlas of ACE2 gene expression in mammals reveals novel insights in transmission of SARS-Cov-2 92%
- BESFA: Bioinformatics based Evolutionary, Structural & Functional Analysis of Prostrate, Placenta, Ovary, Testis, and Embryo (POTE) Paralogs 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.