Transcriptome and Network Dissection of Microsatellite Stable and Highly Instable Colorectal Cancer.

Background: Colorectal cancer (CRC) is one of the most common cancers worldwide with high number of mortality every year. Microsatellite instability (MSI) is a considerable feature of CRC which affects prognosis and treatment. High level of MSI or MSI-high (MSI-H) colorectal cancer has better prognosis and immunotherapy response, while microsatellite stable (MSS) CRC has better response to 5-fluorouracil (5-FU)-based chemotherapy. More studies are needed, specifically on MSS CRC which has worse prognosis, to further reveal biological differences and similarities between MSS and MSI colorectal cancer, which may equip us with the knowledge to develop more promising therapeutic approaches to target both types or be more effective for each type. Methods: We aimed to find affected biological processes and their regulators in both type, MSS and MSI-H, of CRC; as well as reveal specific ones in each type. We applied meta- and network analysis on freely available transcriptome data in MSS and MSI-H colorectal cancer from gene expression omnibus (GEO) database to detect common differentially expressed genes (DEGs) and critical biological processes and predict their most significant regulators. Results: Our results demonstrate considerable up and downregulation in cell cycle and lipid catabolism processes, respectively; and introduced MYC and FOXM1 as two central and up-stream regulators of DEGs in both type of CRC. Chemokine-mediated processes displayed up-regulation in MSI-H type, while metastasis-related processes showed more activation in MSS CRC. Additionally, DACH1 and TP53 were detected as two important transcription factors that differentially expressed just in MSS and MSI-H, respectively. Conclusion: Our results can explain why MSI and MSS CRC display different immunotherapy response, prognosis, and metastasis feature. Moreover, our predicted up-stream regulators in the regulatory networks may be promising therapeutic targets.

Transcriptome mining of non-BRCA1/A2 and BRCA1/A2 familial breast cancer.

About 10% of all breast cancer cases are the familial type. Mutations in two highly penetrance breast cancer susceptibility genes, BRCA1 and BRCA2, can only explain 20% to 25% of genetic susceptibility to breast cancer, and most familial breast cancer cases have intact BRCA1 and BRCA2 genes that refer to non-BRCA1/A2 or BRCAX familial breast cancer. Despite extensive studies, more than 50% of genetic susceptibility to breast cancer remained to be disclosed. Finding the differences between these two types of breast cancer (non-BRCA1/A2 and BRCA1/A2) at genomic, transcriptomic, and proteomic levels can help us to elucidate fundamental molecular processes and develope more promising therapeutic targets. Here, we used expression data of 391 patients with familial breast cancer including 195 non-BRCA1/A2 and 196 BRCA1 and/or BRCA2 cases from four independent studies by means of meta-analysis to find differences in gene expression signature between these two types of familial breast cancer. As well as, we applied comprehensive network analysis to find crucial protein complexes and regulators for each condition. Our results revealed significant overexpression of cell cycle processes in BRCA1/A2 patients and significant overexpression of estrogen axis in non-BRCA1/A2 patients. Moreover, we found FOXM1 as the central regulator of cell cycle processes and GATA3, FOXA1, and ESR1 as the main regulators of estrogen axis.