Comprehensive Analysis of Molecular Characteristics, Clinical Signifificance, and Cancer Immune Interactions of Patients by Anoikis-Related Genes in LUAD Combined with Single-cell Data
Yu, W.; Miao, Z.; Sheyhidin, I.; Qiao, B.; Jumai, K.
Show abstract
BackgroundLung adenocarcinoma(LUAD) is the most prevalent subtype of lung cancer today. There is a close relationship between Anoikis related genes(ARGs) and tumor prognosis, drug susceptibility, and tumor microenvironment(TME). MethodWe calculated differential expression genes using downloaded Anoikis genes and selected genes of prognostic value. Consensus clustering analysis was used and characterized between different clusters. Differences between the different groups were also explored. Risk scores and Nomogram with predictive prognostic functions were established. Immune status and drug sensitivity were also assessed between different risk groups. Single-cell data were downloaded to compare the expression profiles of selected genes, and immunohistochemical results of selected genes were also downloaded to corroborate the reliability of the manuscript. ResultTwo clusters were identified on the basis of related gene expression. We analyzed the survival time, functional enrichment between the two groups and found significant differences between the two clusters. Significant relationships were found between the different clusters and clinical variables. group B had a significantly lower KM curve than group A, as well as a significant enrichment in multiple tumor functions. A risk score with prognostic value was established. The risk score was found to have a high predictive value for prognosis and was an independent prognostic factor. Combined with clinical variables, a Nomogram was established and found to be an accurate predictor of patient prognosis. There were significant differences in immune status between the different risk groups. Patients in the low-risk group were significantly better treated than those in the high-risk group. Finally single cell data confirmed the expression of the selected genes. Also, the immunohistochemical results helped us to confirm the selected genes have increased expression in tumor tissue. ConclusionIn conclusion, this paper reveals the role of ARGs and immune status, drug susceptibility, and prediction of prognosis in LUAD. Also, an accurate prognostic prediction model was established based on genetic.
Matching journals
The top 12 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Mapping of single-cell landscape of acral melanoma and analysis of molecular regulatory network of tumor microenvironment 97%
- Tumor Purity-Related Genes for Predicting the Prognosis and Drug Sensitivity of DLBCL Patients 97%
- Molecular Feature-Based Classification of Retroperitoneal Liposarcoma: A Prospective Cohort Study 96%
Similar papers in this journal
- Risk assessment of cancer patients based on HLA-I alleles, neobinders and expression of cytokines 93%
- Systematic identification of A-to-I editing associated regulators from multiple human cancers 92%
- A Comprehensive Targeted Panel of 295 Genes: Unveiling Key Disease Initiating and Transformative Biomarkers in MultipleMyeloma 92%
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 99%
- Pyroptosis-related gene signatures can robustly diagnose skin cutaneous melanoma and predict the prognosis 98%
- Patient stratification of clear cell renal cell carcinoma using the global transcription factor activity landscape derived from RNA-seq data 96%
"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.