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Background: Forkhead box (FOX) proteins belong to one of the largest transcription factor families and play crucial roles in the initiation and progression of cancer. Prior research has linked several FOX genes, such as FOXA1 and FOXM1, to the crucial process of carcinogenesis. However, the overall picture of FOX gene family across human cancers is far from clear. Methods: To investigate the broad molecular signatures of the FOX gene family, we conducted study on multi‐omics data (including genomics, epigenomics and transcriptomics) from over 11,000 patients with 33 different types of human cancers. Results: Pan‐cancer analysis reveals that FOX gene mutations were found in 17.4% of tumor patients with a substantial cancer type‐dependent pattern. Additionally, high expression heterogeneity of FOX genes across cancer types was discovered, which can be partially attributed to the genomic or epigenomic alteration. Co‐expression network analysis reveals that FOX genes may exert functions by regulating the expression of both their own and target genes. For a clinical standpoint, we provided 103 FOX gene‐drug target‐drug predictions and found FOX gene expression have potential survival predictive value. All of the results have been included in the FOX2Cancer database, which is freely accessible at http://hainmu‐biobigdata.com/FOX2Cancer. Conclusion: Our findings may provide a better understanding of roles FOX genes played in the development of tumors, and help to offer new avenues for uncovering tumorigenesis and unprecedented therapeutic targets. [ABSTRACT FROM AUTHOR]

Noncoding RNAs (ncRNAs), especially microRNA (miRNA) and long noncoding RNA (lncRNA), have an impact on a variety of important biological processes during colon adenocarcinoma (COAD) progression. This includes chromatin organization, transcriptional and posttranscriptional regulation, and cell-cell signaling. The aim of this study is to identify the ncRNA-regulated modules that accompany the progression of COAD and to analyze their mechanisms, in order to screen the potential prognostic biomarkers for COAD. An integrative molecular analysis was carried out to identify the crosstalks of gene modules between different COAD stages, as well as the essential ncRNAs in the posttranscriptional regulation of these modules. 31 ncRNA regulatory modules were found to be significantly associated with overall survival in COAD patients. 17 out of the 31 modules (in which ncRNAs played essential roles) had improved the predictive ability for COAD patient survival compared to only the mRNAs of those modules, which were enriched in the core cancer hallmark pathways with closer interactions. These suggest that the ncRNAs' regulatory modules not only exhibit close relation to COAD progression but also reflect the dynamic significant crosstalk of genes in the modules to the different malignant extent of COAD. [ABSTRACT FROM AUTHOR]

Recent evidence has suggested that competitive endogenous RNAs (ceRNAs) are important regulatory molecules in clear cell kidney carcinoma (KIRC) and their dysregulation may contribute to cancer pathogenesis. However, the critical roles of dysregulated ceRNAs in KIRC remain unknown. In the present study, a KIRC dysregulated ceRNA‑ceRNA network (KDCCNet) was constructed based on the ʻceRNA hypothesisʼ by integrating microRNA regulation and expression profiles in cancerous and normal tissues. Two dysregulated patterns of ceRNAs interaction (gain and loss) exist in KDCCNet. The two modules, which are 95% loss interactions and 97% gain interactions, were demonstrated to be able to distinguish normal samples from cancer samples. Two long non‑coding (lnc)‑RNAs (glucuronidase β pseudogene 11 and LIFR antisense RNA 1) demonstrated significant associations with KIRC prognosis. The present study of the KDCCNet revealed a novel biological mechanism for KIRC and provides novel lncRNAs as candidate prognostic biomarkers. [ABSTRACT FROM AUTHOR]

Gene expression profiles have drawn broad attention in deciphering the pathogenesis of human cancers. Cancer-related gene modules could be identified in co-expression networks and be applied to facilitate cancer research and clinical diagnosis. In this paper, a new method was proposed to identify lung cancer-risk modules and evaluate the module-based disease risks of samples. The results showed that thirty one cancer-risk modules were closely related to the lung cancer genes at the functional level and interactional level, indicating that these modules and genes might synergistically lead to the occurrence of lung cancer. Our method was proved to have good robustness by evaluating the disease risk of samples in eight cancer expression profiles (four for lung cancer and four for other cancers), and had better performance than the WGCNA method. This method could provide assistance to the diagnosis and treatment of cancers and a new clue for explaining cancer mechanisms. [ABSTRACT FROM AUTHOR]