Using bioinformatics approaches to investigate driver genes and identify BCL7A as a prognostic gene in colorectal cancer

DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods

Although computational methods for driver gene identification have progressed rapidly, it is far from the goal of obtaining widely recognized driver genes for all cancer types. The driver gene lists predicted by these methods often lack consistency and stability across different studies or datasets. In addition to analytical performance, some tools may require further improvement regarding operability and system compatibility. Here, we developed a user-friendly R package (DriverGenePathway) integrating MutSigCV and statistical methods to identify cancer driver genes and pathways. The theoretical basis of the MutSigCV program is elaborated and integrated into DriverGenePathway, such as mutation categories discovery based on information entropy. Five methods of hypothesis testing, including the beta-binomial test, Fisher combined p-value test, likelihood ratio test, convolution test, and projection test, are used to identify the minimal core driver genes. Moreover, de novo methods, which can effectively overcome mutational heterogeneity, are introduced to identify driver pathways. Herein, we describe the computational structure and statistical fundamentals of the DriverGenePathway pipeline and demonstrate its performance using eight types of cancer from TCGA. DriverGenePathway correctly confirms many expected driver genes with high overlap with the Cancer Gene Census list and driver pathways associated with cancer development. The DriverGenePathway R package is freely available on GitHub: https://github.com/bioinformatics-xu/DriverGenePathway.

Aspirin sensitivity of PIK3CA-mutated Colorectal Cancer: potential mechanisms revisited

PIK3CA mutations are amongst the most prevalent somatic mutations in cancer and are associated with resistance to first-line treatment along with low survival rates in a variety of malignancies. There is evidence that patients carrying PIK3CA mutations may benefit from treatment with acetylsalicylic acid, commonly known as aspirin, particularly in the setting of colorectal cancer. In this regard, it has been clarified that Class IA Phosphatidylinositol 3-kinases (PI3K), whose catalytic subunit p110α is encoded by the PIK3CA gene, are involved in signal transduction that regulates cell cycle, cell growth, and metabolism and, if disturbed, induces carcinogenic effects. Although PI3K is associated with pro-inflammatory cyclooxygenase-2 (COX-2) expression and signaling, and COX-2 is among the best-studied targets of aspirin, the mechanisms behind this clinically relevant phenomenon are still unclear. Indeed, there is further evidence that the protective, anti-carcinogenic effect of aspirin in this setting may be mediated in a COX-independent manner. However, until now the understanding of aspirin's prostaglandin-independent mode of action is poor. This review will provide an overview of the current literature on this topic and aims to analyze possible mechanisms and targets behind the aspirin sensitivity of PIK3CA-mutated cancers.

Upregulated Linc01836 in Serum Promisingly Serving as a Diagnostic and Prognostic Biomarker for Colorectal Cancer

Objectives: Colorectal cancer (CRC) is a common carcinoma of the gastrointestinal tract with high incidence and mortality worldwide. Studies have shown that long noncoding RNAs (lncRNAs) play important roles in CRC. Our purpose is to investigate the potential of serum Linc01836 as a diagnostic and prognostic marker in CRC.

Methods: We evaluated the expression of Linc01836 via quantitative real-time polymerase chain reaction (qRT-PCR). The serum CEA, CA19-9, Cyfra21-1, and CA72-4 concentrations were measured by Architect I4000 SR. Receiver operating characteristic (ROC) curves were plotted to estimate the diagnostic value in CRC. Relationship between serum Linc01836 expression and clinicopathological characteristics of CRC cases was analyzed via chi-square test. The underlying mechanism of Linc01836 on the development and prognosis in CRC was predicted by bioinformatic analysis.

Results: The method of qRT-PCR for Linc01836 detection was confirmed with high precision and specificity. Serum Linc01836 expression in CRC patients was significantly higher than that in healthy donors (p < 0.0001) and benign patients (p < 0.0001), and declined after resection (p < 0.01). High expression of Linc01836 was associated with histological stage (p = 0.002) and lymph node metastasis (p = 0.006). In addition, serum Linc01836 could effectively differentiate CRC patients from the healthy folks, with favorable area under the curve (AUC) of 0.809 (95% CI: 0.757–0.861, p < 0.001). What is more, the combination of serum Linc01836, CEA, and Cyfra21-1 could improve diagnostic sensitivity (92.0%). Linc01836 was averagely located in the nucleus and cytoplasm, suggesting that it might participate in CRC progression and prognosis through the crosstalk among lncRNAs, miRNAs, and mRNAs.

Conclusion: Linc01836 may serve as a valuable noninvasive biomarker for population screening, early detection, and clinical surveillance of CRC.