Cellular information about \"life instruction\" is stored, transferred, and modified using different types of RNA molecules. During the last decades, a growing number of RNA data has been generated thanks to the development of microarray chips and next-generation sequencing (NGS) methods. Improvement of bioinformatics contributed to the discovery of many types of new non-coding RNAs (ncRNAs), mostly with regulatory functions that supplemented the knowledge about the world of RNA. All of it, as well as the Human Genome Project (HGP) and the Cancer Genome Atlas (TCGA) project, has resulted in the formation of data storage and analysis portals which are widely used in cancer research and moved science from in vitro to in silico research. In this review we presented and discussed the data storage and analysis portals used by us, such as cBioPortal, UALCAN, ENCORI, and others. During the revision of these sites, we paid attention to data integration, simplicity of analysis, and results visualization, which are important for users without bioinformatic or statistical skills. In our opinion, the RNA analysis online tools will rapidly develop during the next decade and it seems to be a way for personalization of cancer treatment.

Aim: Head and Neck squamous cell carcinoma (HNSCC) is the second most prevalent cancer in Pakistan. Methods: Gene expression data from TCGA and GETx for normal genes to analyze Differentially Expressed Genes (DEGs). Data was further investigated using the Enrichr tool to perform Gene Ontology (GO). Results: Our analysis identified most significantly differentially expressed genes and explored their established cellular functions as well as their potential involvement in tumor development. We found that the highly expressed Keratin family and S100A9 genes. The under-expressed genes KRT4 and KRT13 provide instructions for the production of keratin proteins. Conclusion: Our study suggests that factors such as poor oral hygiene and smokeless tobacco can result in oral stress and cellular damage and cause cancer.
The Cancer Genome Atlas (TCGA) holds vast cancer data processed with powerful computers and cloud tech. This sparks new bioinformatics for better cancer diagnosis, treatment, and prevention. In Southeast Asia, Head and Neck Squamous Cell Carcinoma (HNSCC) is prevalent. We used TCGA and GETx data to study gene expression. High-expression Keratin and S100A9 genes fight cellular damage under stress, while under-expressed KRT4 and KRT13 genes shape cell structure. Poor oral care and smokeless tobacco could induce cell damage, sparking cancer mutations. Unveiling HNSCC mechanisms may guide targeted treatments and preventive strategies.

Ovarian cancer (OC) ranks among the prevalent tumors affecting the female reproductive system. The aim of this study was to evaluate mitochondria-associated platinum resistance genes using organoid models. Univariate Cox regression, LASSO and multivariate Cox regression analyses were performed on The Cancer Genome Atlas (TCGA) database to construct 2-gene prognostic signature (MUL1 and SSBP1), and GSE26712 dataset was used for external validation. In addition, the relationship between MUL1 and platinum resistance was examined by organoid culture, lentiviral transduction, CCK8 assay, and Western blot. The results showed that patients in the high-risk group exhibited significantly worse OS (P = 0.002, P = 0.017). Drug sensitivity analysis revealed that platinum resistance increased with the upregulation of MUL1 expression (Cor = 0.5154, P = 0.02). Our experimental findings demonstrated that knockout of the MUL1 gene significantly increased apoptosis and enhanced the sensitivity of the OC cell line A2780 to cisplatin. Through this study, we have provided strong evidence for further research on prognostic risk factors and individualized treatment in OC patients, and provided new insights into addressing platinum resistance in OC.

BACKGROUND: The identification of reliable prognostic markers is crucial for optimizing patient management and improving clinical outcomes in clear cell renal cell carcinoma (ccRCC).
METHODS: We used the GSE89563 dataset from the GEO database and the Kidney Clear Cell Carcinoma (KIRC) dataset from the TCGA database to develop a prognostic model based on weighted gene co-expression network analysis (WGCNA) and non-negative matrix factorization (NMF) to predict disease progression and prognosis in ccRCC.
RESULTS: We utilized WGCNA to identify risk genes and applied NMF to stratify high-risk populations in ccRCC. We characterized the immune gene features of these high-risk groups and ultimately developed a risk prediction model for ccRCC patients using a Lasso regression approach. The risk score was calculated as follows: Risk score = SUM (-0.136394797 ANK3 + 0.004238138 BIVM_ERCC5 - 0.046248451 C4orf19 - 0.036013206 F2RL3 - 0.125531316 GNG7 - 0.012698109 METTL7A + 0.078462369 MSTO1 - 0.050450656 PINK1 - 0.059446590 SLC16A12 - 0.039883686 SLC2A9 + 0.083310722 TLCD1 - 0.059801739 WDR72 + 0.071430088 ZNF117).
CONCLUSIONS: We develop a prognostic model for clear cell renal cell carcinoma and analyzed immune response in subgroups and confirmed protein-level expression concordance.

BACKGROUND: Large-scale sequencing plays important roles in revealing the genomic map of ccRCC and predicting prognosis and therapeutic response to targeted drugs. However, the relevant clinical data is still sparse in Chinese population.
METHODS: Fresh tumor specimens were collected from 66 Chinese ccRCC patients, then the genomic RNAs were subjected to whole transcriptome sequencing (WTS). We comprehensively analyzed the frequently mutated genes from our hospital\'s cohort as well as TCGA-KIRC cohort.
RESULTS: VHL gene is the most frequently mutated gene in ccRCC. In our cohort, BAP1 and PTEN are significantly associated with a higher tumor grade and DNM2 is significantly associated with a lower tumor grade. The mutant type (MT) groups of BAP1 or PTEN, BAP1 or SETD2, BAP1 or TP53, BAP1 or MTOR, BAP1 or FAT1 and BAP1 or AR had a significantly correlation with higher tumor grade in our cohort. Moreover, we identified HMCN1 was a hub mutant gene which was closely related to worse prognosis and may enhance anti-tumor immune responses.
CONCLUSIONS: In this preliminary research, we comprehensively analyzed the frequently mutated genes in the Chinese population and TCGA database, which may bring new insights to the diagnosis and medical treatment of ccRCC.

BACKGROUND: Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively.
METHODS: Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data.
RESULTS: We found that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue and tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also discovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database.
CONCLUSIONS: These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.
Lung adenocarcinoma (LUAD) is a disease that affects males and females differently. Biological sex not only influences chances of developing the disease, but also how the disease progresses and how effective various therapies may be. We analyzed sex-specific gene regulatory networks consisting of transcription factors and the genes they regulate in both healthy lung tissue and in LUAD and identified sex-biased differences. We found that genes associated with cell proliferation, immune response, and drug metabolism are differentially targeted by transcription factors between males and females. We also found that several genes that are drug targets in LUAD, are also regulated differently between males and females. Importantly, these differences are also influenced by an individual’s smoking history. Extending our analysis using a drug repurposing tool, we found candidate drugs with evidence that they might work better for one sex or the other. These results demonstrate that considering the differences in gene regulation between males and females will be essential if we are to develop precision medicine strategies for preventing and treating LUAD.

BACKGROUND: Muscle-invasive bladder cancer (MIBC) is a prevalent and aggressive malignancy. Ferroptosis and cuproptosis are recently discovered forms of programmed cell death (PCD) that have attracted much attention. However, their interactions and impacts on MIBC overall survival (OS) and treatment outcomes remain unclear.
METHODS: Data from the TCGA-BLCA project (as the training set), cBioPortal database, and GEO datasets (GSE13507 and GSE32894, as the test sets) were utilized to identify hub ferroptosis/cuproptosis-related genes (FRGs and CRGs) and develop a prognostic signature. Differential expression analysis (DEA) was conducted, followed by univariate and multivariate Cox\'s regression analyses and multiple machine learning (ML) techniques to select genetic features. The performance of the ferroptosis/cuproptosis-related signature was evaluated using Kaplan-Meier (K-M) survival analysis and receiver-operating characteristics (ROC) curves. Mutational and tumour immune microenvironment landscapes were also explored. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) experiments confirmed the expression patterns of the hub genes, and functional assays assessed the effects of SCD knockdown on cell viability, proliferation, and migration.
RESULTS: DEA revealed dysregulated FRGs and CRGs in the TCGA MIBC cohort. SCD, DDR2, and MT1A were identified as hub genes. A prognostic signature based on the sum of the weighted expression of these genes demonstrated strong predictive efficacy in the training and test sets. Nomogram incorporating this signature accurately predicted 1-, 3-, and 5-year survival probabilities in the TCGA cohort and GSE13507 dataset. Copy number variation (CNV) and tumour immune microenvironment analysis revealed that high risk score level groups were associated with immunosuppression and lower tumour purity. The associations of risk scores with immunotherapy and chemical drugs were also explored, indicating their potential for guiding treatment for MIBC patients. The dysregulated expression patterns of three hub genes were validated by RT-qPCR experiments.
CONCLUSIONS: Targeting hub FRGs and CRGs could be a promising therapeutic approach for MIBC. Our prognostic model offers a new framework for MIBC subtyping and can inform personalized therapeutic strategies.

BACKGROUND: Glioblastoma (GBM) severely disrupts the quality of life of patients. Anoikis represents a significant mechanism in cancer invasion and metastasis. Our study focused on the prognostic relationship between the anoikis-associated gene and GBM and its effect on GBM cell progression.
METHODS: We downloaded 656 and 979 GBM sample data from TCGA and CGGA cohort datasets, respectively. Fifteen anoikis-associated genes were obtained from the GeneCards database and were subsequently clustered to identify differential genes associated with them. After LAASO screening, the expression values of the 5 differential genes were the sum of LASSO regression coefficients. Survival analysis and ROC curve analysis of anoikis scores were performed using the TCGA training and CGGA validation sets. The prognostic factors were analyzed using Cox regression analysis in GBM. Moreover, CCK-8, colony formation, wound healing, and transwell assay were used to evaluate GBM cell proliferation and migration.
RESULTS: Significant differences were observed in the 5-year survival of GBM patients between the two subgroups. Then, our analysis demonstrated that high OCIAD2, FTLP3, IGFBP2, and H19 levels were associated with lower 5-year GBM survival rates, whereas high SFRP2 levels were associated with higher survival rates. Univariate Cox analysis indicated that GBM risk was linked to both anoikis score and grade, while multivariate Cox analysis indicated that GBM risk was associated with both anoikis score and age. Additionally, OCIAD2 was highly expressed in U251MG and T98G cells. Moreover, OCIAD2 silencing inhibited GBM cell proliferation and migration.
CONCLUSIONS: This study demonstrated the potential of the anoikis-associated gene OCIAD2 as a prognostic biomarker for GBM. Furthermore, we validated in vitro that OCIAD2 promoted GBM cell progression.

Aim: This study investigates the altered expression and CpG methylation patterns of histone demethylase KDM8 in hepatocellular carcinoma (HCC), aiming to uncover insights and promising diagnostics biomarkers. Materials & methods: Leveraging TCGA-LIHC multi-omics data, we employed R/Bioconductor libraries and Cytoscape to analyze and construct a gene correlation network, and LASSO regression to develop an HCC-predictive model. Results: In HCC, KDM8 downregulation is correlated with CpGs hypermethylation. Differential gene correlation analysis unveiled a liver carcinoma-associated network marked by increased cell division and compromised liver-specific functions. The LASSO regression identified a highly accurate HCC prediction signature, prominently featuring CpG methylation at cg02871891. Conclusion: Our study uncovers CpG hypermethylation at cg02871891, possibly influencing KDM8 downregulation in HCC, suggesting these as promising biomarkers and targets.
Changes in gene function can play a role in causing cancer. In this study, we looked at how a specific gene called KDM8 behaves in liver cancer. By analyzing a large set of liver cancer samples, we investigated how gene interactions are different in this disease and if they can help predict liver cancer risk. Our results show that the KDM8 gene is less active, and its DNA gets chemically modified more often in liver cancer. We also found a group of genes and DNA changes, which are linked to the disease. Using this information, we identified 16 important markers and built a computer model that can accurately predict liver cancer. We found that DNA methylation at a specific spot called cg02871891 is especially important for predicting liver cancer. Overall, our study suggests that high levels of DNA methylation may lead to reduced KDM8 activity in liver cancer, which could be important for future research and better diagnostic tools.

Dysregulation of histone deacetylases (HDACs) is closely associated with cancer development and progression. Here, we comprehensively analyzed the association between all HDAC family members and several clinicopathological and molecular traits of solid tumors across 22 distinct tumor types, focusing primarily on cancer stemness and immunity. To this end, we used publicly available TCGA data and several bioinformatic tools (i.e., GEPIA2, TISIDB, GSCA, Enrichr, GSEA). Our analyses revealed that class I and class II HDAC proteins are associated with distinct cancer phenotypes. The transcriptomic profiling indicated that class I HDAC members, including HDAC2, are positively associated with cancer stemness, while class IIA HDAC proteins, represented by HDAC7, show a negative correlation to cancer stem cell-like phenotypes in solid tumors. In contrast to tumors with high amounts of HDAC7 proteins, the transcriptome signatures of HDAC2-overexpressing cancers are significantly enriched with biological terms previously determined as stemness-associated genes. Moreover, high HDAC2-expressing tumors are depleted with immune-related processes, and HDAC2 expression correlates with tumor immunosuppressive microenvironments. On the contrary, HDAC7 upregulation is significantly associated with enhanced immune responses, followed by enriched infiltration of CD4+ and CD8+ T cells. This is the first comprehensive report demonstrating robust and versatile associations between specific HDAC family members, cancer dedifferentiation, and anti-tumor immune statuses in solid tumors.