Identification of methylation-driven genes related to prognosis in clear-cell renal cell carcinoma

Bioinformatics screening of prognostic immune-related genes in renal clear cell carcinoma

This study aims to harness bioinformatics to identify prognostic immune-related genes in clear cell renal cell carcinoma (ccRCC), focusing particularly on LILRB3. It evaluates LILRB3's expression in ccRCC, its association with patient prognosis, and its potential as a biomarker for predicting survival, thereby providing a preliminary basis for the diagnosis of ccRCC. Utilizing The Cancer Genome Atlas (TCGA) datasets and an immune gene set, we sought immune-related genes with elevated expression in ccRCC. Seventy-two normal tissue samples and 531 ccRCC samples were analyzed, and differential genes were identified with a screening criterion of fold change (FC) > 2 and P value < 0.01. Survival analysis and receiver operating characteristic (ROC) curve analysis were employed to discover genes of prognostic and diagnostic relevance to ccRCC. Pearson correlation analysis with a cutoff of |r|≥ 0.5, facilitated by cBioPortal, assessed genes co-expressed with LILRB3. The DAVID online tool conducted functional and pathway enrichment analyses for LILRB3-coexpressed genes. The TIMER and TCIA databases were utilized to explore LILRB3's influence on immune infiltration in the tumor microenvironment and its relation to key immunological checkpoints. Screening the TCGA database revealed 3719 up-regulated differential genes in ccRCC, with 355 overlapping immune-related genes. Survival analysis of these 355 genes revealed 100 with significant survival impact. ROC curve analysis pinpointed the top 10 genes, including LILRB3, with the highest diagnostic efficiency. LILRB3 emerged as an independent risk factor from the Cox risk regression model. GO and KEGG analyses linked LILRB3 to various biological processes, including chemokine signaling pathways, immunological response, antigen processing and presentation, inflammatory response, T cell co-stimulation, and signal transduction. LILRB3 significantly affected ccRCC immune infiltration and correlated positively with several immunological checkpoints, such as PD-1, LAG3, IDO1, PD-L1, CTLA4, TIM3, TIGIT, and VISTA. LILRB3 shows higher expression levels in ccRCC than in normal tissues and correlates with poor patient prognosis. Its impactful role in the immune infiltration of the RCC microenvironment suggests that LILRB3 could serve as a novel target for ccRCC treatment and prognosis, underlining its diagnostic and prognostic significance.

Identification of Differentially Methylated Genes Associated with Clear Cell Renal Cell Carcinoma and Their Prognostic Values

Objective

Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes among which clear cell RCC (ccRCC) accounts for 70% of all RCC cases. DNA methylation constitutes a main part of the molecular mechanism of cancer evolution and prognosis. In this study, we aim to identify differentially methylated genes related to ccRCC and their prognostic values.

Methods

The GSE168845 dataset was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between ccRCC tissues and paired tumor-free kidney tissues. DEGs were submitted to public databases for functional and pathway enrichment analysis, protein-protein interaction (PPI) analysis, promoter methylation analysis, and survival correlation analysis.

Results

In the setting of |log2FC| ≥ 2 and adjusted p value <0.05 during differential expression analysis of the GSE168845 dataset, 1659 DEGs between ccRCC tissues and paired tumor-free kidney tissues were sorted out. The most enriched pathways were "T cell activation" and "cytokine-cytokine receptor interaction." After PPI analysis, 22 hub genes related to ccRCC stood out, among which CD4, PTPRC, ITGB2, TYROBP, BIRC5, and ITGAM exhibited higher methylation levels, and BUB1B, CENPF, KIF2C, and MELK exhibited lower methylation levels in ccRCC tissues compared with paired tumor-free kidney tissues. Among these differentially methylated genes, TYROBP, BIRC5, BUB1B, CENPF, and MELK were significantly correlated with the survival of ccRCC patients (p < 0.001).

Conclusion

Our study indicates the DNA methylation of TYROBP, BIRC5, BUB1B, CENPF, and MELK may be promising results for the prognosis of ccRCC.

SPI1 is a prognostic biomarker of immune infiltration and immunotherapy efficacy in clear cell renal cell carcinoma

BACKGROUND

Spi-1 proto-oncogene (SPI1), which encodes an ETS-domain transcription factor, can activate gene expression in myeloid and lymphoid lineages. The role of SPI1 in the tumor immune microenvironment in clear cell renal cell carcinoma (ccRCC) remains unknown. In this study, we investigated the possible role of SPI1 in ccRCC using an independent cohort and a comprehensive bioinformatics analysis.

MATERIALS AND METHODS

Quantitative real-time PCR, western blot and immunohistochemistry assays were used to compare the SPI1 expression levels between ccRCC tissues and normal tissues, analyze the relationships between SPI1 and CD68, CD8, CD4 expression levels, and explore the link between SPI1 and the efficacy of immunotherapy in our cohort. Tumor Immune Estimation Resource, UALCAN, cBioPortal, TISIDB database, and LinkedOmics database were used in our study.

RESULTS

SPI1 expression level was higher in ccRCC bulk tissues than in normal bulk tissues. SPI1 was an independent prognostic factor for poor overall survival and progression-free survival in patients with ccRCC. SPI1 expression was strongly related to the infiltration of immune cells and immune-related molecules. SPI1 was more highly expressed in tumor-infiltrating immune cells rather than in cancer cells. Non-responders to immunotherapy against ccRCC were more likely to express higher SPI1 levels than responders. Genes co-expressed with SPI1 primarily correlated with immune-related pathways.

CONCLUSIONS

SPI1 expression in tumor bulk tissues is associated with disease progression and poor prognosis, as well as high expression levels of immune markers and infiltration of immune cells. SPI1 can be used as a prognostic biomarker to monitor and evaluate immunotherapy efficacy.

SurviveAI: Long Term Survival Prediction of Cancer Patients Based on Somatic RNA-Seq Expression

Motivation

Prediction of cancer outcome is a major challenge in oncology and is essential for treatment planning. Repositories such as The Cancer Genome Atlas (TCGA) contain vast amounts of data for many types of cancers. Our goal was to create reliable prediction models using TCGA data and validate them using an external dataset.

Results

For 16 TCGA cancer type cohorts we have optimized a Random Forest prediction model using parameter grid search followed by a backward feature elimination loop for dimensions reduction. For each feature that was removed, the model was retrained and the area under the curve of the receiver operating characteristic (AUC-ROC) was calculated using test data. Five prediction models gave AUC-ROC bigger than 80%. We used Clinical Proteomic Tumor Analysis Consortium v3 (CPTAC3) data for validation. The most enriched pathways for the top models were those involved in basic functions related to tumorigenesis and organ development. Enrichment for 2 prediction models of the TCGA-KIRP cohort was explored, one with 42 genes (AUC-ROC = 0.86) the other is composed of 300 genes (AUC-ROC = 0.85). The most enriched networks for both models share only 5 network nodes: DMBT1, IL11, HOXB6, TRIB3, PIM1. These genes play a significant role in renal cancer and might be used for prognosis prediction and as candidate therapeutic targets.

Availability And Implementation

The prediction models were created and tested using Python SciKit-Learn package. They are freely accessible via a friendly web interface we called surviveAI at https://tinyurl.com/surviveai.

The Role of Epigenetics in the Progression of Clear Cell Renal Cell Carcinoma and the Basis for Future Epigenetic Treatments

Simple Summary

The accumulated evidence on the role of epigenetic markers of prognosis in clear cell renal cell carcinoma (ccRCC) is reviewed, as well as state of the art on epigenetic treatments for this malignancy. Several epigenetic markers are likely candidates for clinical use, but still have not passed the test of prospective validation. Development of epigenetic therapies, either alone or in combination with tyrosine-kinase inhibitors of immune-checkpoint inhibitors, are still in their infancy.

Abstract

Clear cell renal cell carcinoma (ccRCC) is curable when diagnosed at an early stage, but when disease is non-confined it is the urologic cancer with worst prognosis. Antiangiogenic treatment and immune checkpoint inhibition therapy constitute a very promising combined therapy for advanced and metastatic disease. Many exploratory studies have identified epigenetic markers based on DNA methylation, histone modification, and ncRNA expression that epigenetically regulate gene expression in ccRCC. Additionally, epigenetic modifiers genes have been proposed as promising biomarkers for ccRCC. We review and discuss the current understanding of how epigenetic changes determine the main molecular pathways of ccRCC initiation and progression, and also its clinical implications. Despite the extensive research performed, candidate epigenetic biomarkers are not used in clinical practice for several reasons. However, the accumulated body of evidence of developing epigenetically-based biomarkers will likely allow the identification of ccRCC at a higher risk of progression. That will facilitate the establishment of firmer therapeutic decisions in a changing landscape and also monitor active surveillance in the aging population. What is more, a better knowledge of the activities of chromatin modifiers may serve to develop new therapeutic opportunities. Interesting clinical trials on epigenetic treatments for ccRCC associated with well established antiangiogenic treatments and immune checkpoint inhibitors are revisited.

Novel prognostic prediction model constructed through machine learning on the basis of methylation-driven genes in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is a common tumor with poor prognosis and is closely related to many aberrant gene expressions. DNA methylation is an important epigenetic modification mechanism and a novel research target. Thus, exploring the relationship between methylation-driven genes and KIRC prognosis is important. The methylation profile, methylation-driven genes, and methylation characteristics in KIRC was revealed through the integration of KIRC methylation, RNA-seq, and clinical information data from The Cancer Genome Atlas. The Lasso regression was used to establish a prognosis model on the basis of methylation-driven genes. Then, a trans-omics prognostic nomogram was constructed and evaluated by combining clinical information and methylated prognosis model. A total of 242 methylation-driven genes were identified. The Gene Ontology terms of these methylation-driven genes mainly clustered in the activation, adhesion, and proliferation of immune cells. The methylation prognosis prediction model that was established using the Lasso regression included four genes in the methylation data, namely, FOXI2, USP44, EVI2A, and TRIP13. The areas under the receiver operating characteristic curve of 1-, 3-, and 5-year survival rates were 0.810, 0.824, and 0.799, respectively, in the training group and 0.794, 0.752, and 0.731, respectively, in the testing group. An easy trans-omics nomogram was successfully established. The C-indices of the nomogram in the training and the testing groups were 0.8015 and 0.8389, respectively. The present study revealed the overall perspective of methylation-driven genes in KIRC and can help in the evaluation of the prognosis of KIRC patients and provide new clues for further study.

USP44 hypermethylation promotes cell proliferation and metastasis in breast cancer

Aim: The methylation and expression levels of USP44 in breast cancer were investigated and their effects on tumor cells were researched. Materials & methods: Bioinformatics was employed to identify the target gene from TCGA database. Sodium bisulfite and decitabine were used for DNA modification and demethylation, and methylation-specific PCR and reverse transcriptase PCR were performed to assess USP44 methylation and expression levels. Tumor cell behaviors were assayed via several in vitro experiments. Results: USP44 was hypermethylated, which caused its poor expression in breast cancer, whereas its overexpression significantly suppressed cancer cell proliferation, migration and invasion and induced apoptosis. Conclusion: USP44 negatively functions in cancer progression upon overexpression, indicating its potential as a therapeutic target for clinical treatment of breast cancer.

Development and validation of an integrative methylation signature and nomogram for predicting survival in clear cell renal cell carcinoma

Background

Growing evidence has shown that genetic or epigenetic alterations are highly involved in the initiation and progression of renal cell carcinoma (RCC). This study aimed to find prognostic methylation markers in clear cell RCC (ccRCC).

Methods

In this study, we developed and confirmed an integrated and comprehensive methylation signature by integrating DNA methylation, gene expression, and The Cancer Genome Atlas (TCGA) survival data. First, the methylation signature was found and checked based on data analysis of published datasets. Then, independent predictive factors were selected using the Cox proportional model and incorporated into the nomogram. Finally, the predictive nomogram was derived and validated using a concordance index and calibration plots.

Results

A series of differentially expressed and methylated genes were identified. After intersection analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) analysis, and correlation analysis, FCGR1A, F2, and NOD2 were established as a predictive signature. According to the Kaplan-Meier survival analysis, the risk score system based on the predictive signature was able to stratify the patients into high- and low-risk groups with significantly different overall survival. The receiver operating characteristic (ROC) analysis further showed that the predictive signature yielded high sensitivity and specificity in predicting the prognosis outcome of ccRCC patients. Moreover, univariate and multivariate Cox regression analysis confirmed that the three-gene methylation signature was an independent prognostic factor in ccRCC. Finally, a nomogram comprising the predictive signature and several independent variables were constructed and proved to effectively predict ccRCC patient survival.

Conclusions

The three-gene methylation signature was revealed to be a potential novel and independent adverse predictor of prognosis for ccRCC patients and may serve as a promising marker for treatment management and survival outcome improvement. However, substantial validation experiments are required to characterize the molecular background of the predictive signature.

The Landscape of Iron Metabolism-Related and Methylated Genes in the Prognosis Prediction of Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is characteristics of resistance to chemotherapy and radiotherapy. The prognosis of ccRCC was dismay with immense diversity. Iron metabolism disturbance is a common phenomenon in ccRCC. The purpose of our study is to identify and validate the candidate prognostic gene signature of iron metabolism and methylation closely related to the poor prognosis of ccRCC through comprehensive bioinformatics analysis in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases.

Methods: The prognostic iron metabolism-related genes were screened according to the overlapping differentially expressed genes (DEGs) from the TCGA database. We built a prognostic model using risk score method to predict OS, each ccRCC patient's risk score was calculated, and the resulting score can divide these patients into two categories according to the cut-point risk score. The prognostic significance of the hub genes was further evaluated with the Kaplan-Meier (KM) survival and Receiver Operating Characteristic (ROC) curve analysis. Univariate and multivariate Cox regression analysis was implemented to evaluate the impact of each variable on OS. Furthermore, the prediction power of the 25 gene signatures has been validated using an independent ccRCC cohort from the GEO database. The Gene Set Enrichment Analysis (GSEA) identified the characteristics of hub related oncogenes. Finally, we utilize Weighted Gene Co-expression Network Analysis (WGCNA) to investigate the co-expression network based on these DEGs.

Results: In this study, we identified and validated 25 iron metabolism-related and methylated genes as the prognostic signatures, which differentiated ccRCC patients into high and low risk subgroups. The KM analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 25 gene signatures could largely predict OS and DFS for 1, 3, and 5 years in patients with ccRCC.

Conclusions: Taken together, we identified the key iron metabolism-related and methylated genes for ccRCC through a comprehensive bioinformatics analysis. This study provides a reliable and robust gene signature for the prognostic predictor of ccRCC patients and maybe provides a promising treatment strategy for this lethal disease.

A FITM1-Related Methylation Signature Predicts the Prognosis of Patients With Non-Viral Hepatocellular Carcinoma

Although great progress has been made in treatment against hepatitis virus infection, the prognosis of hepatocellular carcinoma (HCC) remains unsatisfied. Therefore, there is an unmet need to explore biomarkers or prognostic models for monitoring non-viral hepatocellular carcinoma. Accumulating evidence indicates that DNA methylation participates in carcinogenesis of malignancies. In the present study, we analyzed 101 non-viral HCC patients from TCGA database to figure out methylation-driven genes (MDGs) that might get involved in non-viral HCC pathogenesis using MethyMix algorithm. Then we picked out 8 key genes out of 137 MDGs that could affect the overall survival (OS) of both methylation and expression level. Using PCA, Uni-variate, Multi-variate, and LASSO cox regression analyses, we confirmed the potential prognostic value of these eight epigenetic genes. Ultimately, combined with immunohistochemistry (IHC), ROC, OS, and GSEA analyses, fat storage-inducing transmembrane protein1 (FITM1) was identified as a novel tumor suppressor gene in non-viral HCC and an applicable FITM1-methylation-based signature was built in a training set and validated in a testing set. Briefly, our work provides several potential biomarkers, especially FITM1, as well as a new method for disease surveillance and treatment strategy development.

Identification of methylation-driven genes related to prognosis in clear-cell renal cell carcinoma

With the participation of the existing treatment methods, the prognosis of advanced clear‐cell renal cell carcinoma (ccRCC) is poor. More evidence indicates the presence of methylation in ccRCC cancer cells, but there is a lack of studies on methylation‐driven genes in ccRCC. We analyzed the open data of ccRCC in The Cancer Genome Atlas database to obtain ccRCC‐related methylation‐driven genes, and then carried out pathway enrichment, survival, and joint survival analyses. More important, we deeply explored the correlation between differential methylation sites and the expression of these driving genes. Finally, we screened 29 methylation‐driven genes via MethylMix, of which six were significantly associated with the survival of ccRCC patients. This study demonstrated that the effect of hypermethylation or hypomethylation on prognosis is different, and the level of methylation of key methylation sites is associated with gene expression. We identified methylation‐driven genes independently predicting prognosis in ccRCC, which offers theoretical support in bioinformatics for the study of methylation in ccRCC and a new perspective for the epigenetic study of ccRCC.