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Establishing a prognostic model of chromatin modulators and identifying potential drug candidates in renal clear cell patients

TLDR
In this paper , the functional and prognostic value of chromatin regulators in renal carcinoma patients was investigated using univariate Cox regression analysis and LASSO regression analysis to construct a risk model for predicting the prognosis of renal cancer.
Abstract
Renal carcinoma is a common malignant tumor of the urinary system. Advanced renal carcinoma has a low 5-year survival rate and a poor prognosis. More and more studies have confirmed that chromatin regulators (CRs) can regulate the occurrence and development of cancer. This article investigates the functional and prognostic value of CRs in renal carcinoma patients.mRNA expression and clinical information were obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and LASSO regression analysis were used to select prognostic chromatin-regulated genes and use them to construct a risk model for predicting the prognosis of renal cancer. Differences in prognosis between high-risk and low-risk groups were compared using Kaplan-Meier analysis. In addition, we analyzed the relationship between chromatin regulators and tumor immune infiltration, and explored differences in drug sensitivity between risk groups.We constructed a model consisting of 11 CRs to predict the prognosis of renal cancer patients. We not only successfully validated its feasibility, but also found that the 11 CR-based model was an independent prognostic factor. Functional analysis showed that CRs were mainly enriched in cancer development-related signalling pathways. We also found through the TIMER database that CR-based models were also associated with immune cell infiltration and immune checkpoints. At the same time, the genomics of drug sensitivity in cancer database was used to analyze the commonly used drugs of renal clear cell carcinoma patients. It was found that patients in the low-risk group were sensitive to medicines such as axitinib, pazopanib, sorafenib, and gemcitabine. In contrast, those in the high-risk group may be sensitive to sunitinib.The chromatin regulator-related prognostic model we constructed can be used to assess the prognostic risk of patients with clear cell renal cell carcinoma. The results of this study can bring new ideas for targeted therapy of clear cell renal carcinoma, helping doctors to take corresponding measures in advance for patients with different risks.

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Journal ArticleDOI

Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.

TL;DR: The GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer (IARC) as mentioned in this paper show that 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 cancer, colorectal (11 4.4%), liver (8.3%), stomach (7.7%) and female breast (6.9%), and cervical cancer (5.6%) cancers.
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TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells

TL;DR: Tumor Immune Estimation Resource (TIMER) is presented to comprehensively investigate molecular characterization of tumor-immune interactions and provides a user-friendly web interface for dynamic analysis and visualization of these associations, which will be of broad utilities to cancer researchers.
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A Landscape of Pharmacogenomic Interactions in Cancer

TL;DR: It is reported how cancer-driven alterations identified in 11,289 tumors from 29 tissues can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs.
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pRRophetic: An R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels

TL;DR: An R package called pRRophetic is created, allowing prediction of clinical drug response for many cancer drugs in a user-friendly R environment and showing that prediction of bortezomib sensitivity in multiple myeloma may be improved by training models on a large set of neoplastic hematological cell lines.
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