Comprehensive Analysis of Large-Scale Transcriptomes from Multiple Cancer Types

Various abnormalities of transcriptional regulation revealed by RNA sequencing (RNA-seq) have been reported in cancers. However, strategies to integrate multi-modal information from RNA-seq, which would help uncover more disease mechanisms, are still limited. Here, we present PipeOne, a cross-platform one-stop analysis workflow for large-scale transcriptome data. It was developed based on Nextflow, a reproducible workflow management system. PipeOne is composed of three modules, data processing and feature matrices construction, disease feature prioritization, and disease subtyping. It first integrates eight different tools to extract different information from RNA-seq data, and then used random forest algorithm to study and stratify patients according to evidences from multiple-modal information. Its application in five cancers (colon, liver, kidney, stomach, or thyroid; total samples n = 2024) identified various dysregulated key features (such as PVT1 expression and ABI3BP alternative splicing) and pathways (especially liver and kidney dysfunction) shared by multiple cancers. Furthermore, we demonstrated clinically-relevant patient subtypes in four of five cancers, with most subtypes characterized by distinct driver somatic mutations, such as TP53, TTN, BRAF, HRAS, MET, KMT2D, and KMT2C mutations. Importantly, these subtyping results were frequently contributed by dysregulated biological processes, such as ribosome biogenesis, RNA binding, and mitochondria functions. PipeOne is efficient and accurate in studying different cancer types to reveal the specificity and cross-cancer contributing factors of each cancer.It could be easily applied to other diseases and is available at GitHub

A Comprehensive Pan-Cancer Analysis of the Potential Biological Functions and Prognosis Values of RICTOR

The importance of the network defined by phosphatidylinositol-3-kinase (PI3K), AKT and mammalian target of rapamycin (mTOR) downstream of Receptor Tyrosine Kinase (RTK) has been recognized for many years. However, the central role of RICTOR (rapamycin-insensitive companion of mTOR) in this pathway has only recently come to light. The function of RICTOR in pan-cancer still needs to be systematically elucidated. In this study, we examined RICTOR’s molecular characteristics and clinical prognostic value by pan-cancer analysis. Our findings indicate that RICTOR was overexpressed in twelve cancer types, and a high RICTOR expression was linked to poor overall survival. Moreover, the CRISPR Achilles’ knockout analysis revealed that RICTOR was a critical gene for the survival of many tumor cells. Function analysis revealed that RICTOR-related genes were mainly involved in TOR signaling and cell growth. We further demonstrated that the RICTOR expression was significantly influenced by genetic alteration and DNA-methylation in multiple cancer types. Additionally, we found a positive relationship between RICTOR expression and the immune infiltration of macrophages and cancer-associated fibroblasts in Colon adenocarcinoma and Head and Neck squamous cell carcinoma. Finally, we validated the ability of RICTOR in sustaining tumor growth and invasion in the Hela cell line using cell-cycle analysis, the cell proliferation assay, and wound-healing assay. Our pan-cancer analysis highlights the critical role of RICTOR in tumor progression and its potential as a prognostic marker for various cancer types

APOBEC Alteration Contributes to Tumor Growth and Immune Escape in Pan-Cancer

The accumulating evidence demonstrates that the apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC), DNA-editing protein plays an important role in the molecular pathogenesis of cancer. In particular, the APOBEC3 family was shown to induce tumor mutations by an aberrant DNA editing mechanism. However, knowledge regarding the reconstitution of the APOBEC family genes across cancer types is still lacking. Here, we systematically analyzed the molecular alterations, immuno-oncological features, and clinical relevance of the APOBEC family in pan-cancer. We found that APOBEC genes were widely and significantly differentially expressed between normal and cancer samples in 16 cancer types, and that their expression levels are significantly correlated with the prognostic value in 17 cancer types. Moreover, two patterns of APOBEC-mediated stratification with distinct immune characteristics were identified in different cancer types, respectively. In ACC, for example, the first pattern of APOBEC-mediated stratification was closely correlated with the phenotype of immune activation, which was characterized by a high immune score, increased infiltration of CD8 T cells, and higher survival. The other pattern of APOBEC-mediated stratification was closely correlated with the low-infiltration immune phenotype, which was characterized by a low immune score, lack of effective immune infiltration, and poorer survival. Further, we found the APOBEC-mediated pattern with low-infiltration immune was also highly associated with the advanced tumor subtype and the CIMP-high tumor subtype (CpG island hypermethylation). Patients with the APOBEC-mediated pattern with immune activation were more likely to have therapeutic advantages in ICB (immunological checkpoint blockade) treatment. Overall, our results provide a valuable resource that will be useful in guiding oncologic and therapeutic analyses of the role of APOBEC family in cancer