Disulfidptosis classification of hepatocellular carcinoma reveals correlation with clinical prognosis and immune profile

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Abstract

A new mode of cell death, disulfidptosis, has been discovered. Clinical prognostic significance of disulfidptosis related pattern in hepatocellular carcinoma(HCC). In this study, a risk score model was established based on disulfidptosis model to analyze the role of risk score in clinical prognosis, immune cell infiltration, drug sensitivity and immunotherapy response. Disulfidptosis subtype were constructed based on the transcriptional profiles of 15 disulfidptosis-related genes(DRGs). All 601 samples were defined as high risk group(HRG) and low risk group(LRG) based on the disulfidptosis risk score. Drug sensitivity and response to immunotherapy were calculated by immunophenotypic score(IPS), tumor prediction, tumor immune dysfunction and rejection(TIDE). RT-qPCR was used to determine the mRNA level of disulfidptosis prognostic gene. Risk groups was identified as potential predictors of immune cell infiltration, drug sensitivity, and immunotherapy responsiveness. HRG may benefit from immunotherapy. Classification is very effective in predicting the prognosis and therapeutic effect of patients, and provides a reference for accurate individualized treatment. This study suggests that new biomarkers related to Disulfidptosis can be used in clinical diagnosis of liver cancer to predict prognosis and treatment targets.

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Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)

This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.

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Record: found
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Article: not found

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.