TP53 Mutation Infers a Poor Prognosis and Is Correlated to Immunocytes Infiltration in Breast Cancer

Background: This study aimed to investigate the TP53 mutation, its potential immune features, its prognostic value, and its impact on immune infiltration in patients with breast cancer (BC).Methods: We downloaded the somatic mutation data and clinicopathologic features of BC patients from the TCGA GDC database, UCSC Xena platform, and International Cancer Genome Consortium (ICGC) database. The association between the TP53 mutation, clinicopathology features, and overall survival (OS) in BC patients was analyzed. We evaluated the potential role of the TP53 mutation in the immune therapy response, including the tumor mutation burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion (TIDE). Moreover, ESTIMATE was employed to assess the ImmuneScore and StromalScore in BC patients. We also explored immunocyte infiltration related to the TP53 mutation and its potential mechanism. Immunohistochemistry (IHC) was performed to validate the association between the expression of CXCL1, CXCL10, and CCL20 and TP53 status.Results: We found that the TP53 mutation was significantly associated with the shorter OS (p = 0.038) and was also an independent predictive factor of OS for BC patients (p < 0.001). Compared to that in the wild type group, the TP53-mutant group showed a higher TMB value (P< 0.001), MSI value (p = 0.077), and TIDE value (p < 0.001) with respect to BC patient immunotherapy. In addition, the ImmuneScore and StromalScore were both significantly increased in the TP53-mutant group (ImmuneScore: p < 0.001; StromalScore: p = 0.003). The results of CIBERSORT suggested that the TP53 mutation significantly promoted the infiltration of Tregs, T helper cells, and M0-type macrophages. KEGG and GSEA enrichment results suggested that the IL-17 signaling pathway and antigen processing and presentation pathways were significantly enriched in the TP53-mutant group. Importantly, based on IHC results of immune-related hub-genes, the chemokines CXCL1, CXCL10, and CCL20 were significantly upregulated in the TP53-mutant group in BC patients.Conclusion: These results indicate that a TP53 mutation might serve as a biomarker for BC prognosis and is related to immunocyte infiltration in the tumor microenvironment.

Identification of Clinically Relevant Subgroups of Chronic Lymphocytic Leukemia Through Discovery of Abnormal Molecular Pathways

Chronic lymphocytic leukemia (CLL) is the most common form of adult leukemia in the Western world with a highly variable clinical course. Its striking genetic heterogeneity is not yet fully understood. Although the CLL genetic landscape has been well-described, patient stratification based on mutation profiles remains elusive mainly due to the heterogeneity of data. Here we attempted to decrease the heterogeneity of somatic mutation data by mapping mutated genes in the respective biological processes. From the sequencing data gathered by the International Cancer Genome Consortium for 506 CLL patients, we generated pathway mutation scores, applied ensemble clustering on them, and extracted abnormal molecular pathways with a machine learning approach. We identified four clusters differing in pathway mutational profiles and time to first treatment. Interestingly, common CLL drivers such as ATM or TP53 were associated with particular subtypes, while others like NOTCH1 or SF3B1 were not. This study provides an important step in understanding mutational patterns in CLL.

The m6A Methylation Gene SNRPC can be used as a Biomarker for the Prognosis and Immunotherapy of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world. Due to its complex pathogenic factors, the prognosis of HCC is poor. Therefore, a credible prognostic biomarker is urgently needed for this disease. N6-methyladenosine (m6A) RNA methylation plays an important role in the tumorigenesis, progression and prognosis of many tumors. However, studies on the prognostic and therapeutic value of this modification in HCC are lacking.Case Presentation: The HCC RNA-seq profiles in The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases, including 421 LIHC and 440 LIRI samples respectively, were used in this study. The expressive distinction of 21 RNA methylation regulators between HCC and normal tissue were firstly assessed and SNRPC was obtained. Then the expression of SNRPC was validated as a risk factor for prognosis by Kaplan-Meier analysis and employed to establish a nomogram with T pathologic stage. By GSVA and GSEA analyses, we found SNRPC was mainly related to protein metabolism and immune process. Further, ESTIMATE, MCP-counter and single sample GSEA (ssGSEA) algorithm showed high-SNRPC expression group had lower stromal scores, a lower abundance of endothelial cells, fibroblasts and immune infiltration. Ultimately, Tumor Immune Dysfunction and Exclusion (TIDE) analysis exhibited high-SNRPC expression group showed non-response to immune checkpoint inhibitor therapy, especially to a PD-1 inhibitor.Conclusion: SNRPC could serve as valuable prognostic and immunotherapeutic marker in HCC. We provide here an accurate nomogram for clinical diagnosis using SNRPC as a biomarker.

Genomics and Prognosis Analysis of Circadian clock genes in Hepatocellular Carcinoma

Background: Circadian clock genes have been reported to exhibit a regulatory effect on the carcinogenesis and progression of numerous cancers. Nevertheless, the specific relationship between hepatocellular carcinoma (HCC) and circadian rhythm associated genes still remain to be clarified. Therefore, we evaluate the prognosis function of circadian clock genes in HCC with the online datasets of The Cancer Genome Atlas (TCGA) and the international cancer genome consortium (ICGC). Methods: In our research, the RNA-seq of the selected core circadian genes in HCC patients and their relevant clinical data were acquired from the online TCGA database and the ICGC database. R software and cBioPortal website were performed. Results: As consequence, among the 22 typical circadian clock genes, 16 genes were statistically expressed between HCC and adjacent normal tissues. Accordingly, 11 clock genes with regression coefficients were used to constitute a new risk score formula, which was related to the prognosis in HCC. Moreover, the new nomogram, which consisting risk score and several clinical traits , could be applied for the purpose of accurate prediction of the OS time for the patients. Finally, we identified a novel nomogram related with OS in HCC patients with a comprehensive analysis of circadian clock genes and other clinical characteristics profiles. It was also the first time we systematically demonstrated the relationship between clock genes and the HCC prognosis, which would contribute to the treatment of HCC. Conclusions: The current study demonstrated the potential of circadian clock genes as clinically associated biomarkers for prognosis prediction in HCC, which may make a significant contribution to the further investigations of HCC progression.

Integrative analysis of mutated genes and mutational processes reveals seven colorectal cancer subtypes

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths in the world. It has been reported that ∼10%-15% of individuals with colorectal cancer experience a causative mutation in the known susceptibility genes, highlighting the importance of identifying mutations for early detection in high risk individuals. Through extensive sequencing projects such as the International Cancer Genome Consortium (ICGC), a large number of somatic point mutations have been identified that can be used to identify cancer-associated genes, as well as the signature of mutational processes defined by the tri-nucleotide sequence context (motif) of mutated sites. Mutation is the hallmark of cancer genome, and many studies have reported cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes, however, none of these cancer subtyping methods consider these features simultaneously. This highlights the need for a better and more inclusive subtype classification approach to enable biomarker discovery and thus inform drug development for CRC. In this study, we developed a statistical pipeline based on a novel concept ‘gene-motif’, which merges mutated gene information with tri-nucleotide motif of mutated sites, to identify cancer subtypes, in this case CRCs. Our analysis identified for the first time, 3,131 gene-motif combinations that were significantly mutated in 536 ICGC colorectal cancer samples compared to other cancer types, identifying seven CRC subtypes with distinguishable phenotypes and biomarkers. Interestingly, we identified several genes that were mutated in multiple subtypes but with unique sequence contexts. Taken together, our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers.

GPSM2 Serves as an Independent Prognostic Biomarker for Liver Cancer Survival

Background and Objective: Liver cancer is a malignancy with a poor prognosis. G protein signaling modulator 2 is mainly related to cell division and cell cycle regulation. In this review, the relationship between G protein signaling modulator 2 and clinical characteristics of patients with liver cancer has been explored, especially with respect to its prognostic value. Methods: G protein signaling modulator 2 messenger RNA expression and clinicopathological characteristics of patients with liver cancer were obtained from The Cancer Genome Atlas. The expression level of G protein signaling modulator 2 RNA-Seq was validated by using Gene Expression Omnibus. Chi-square test was performed to evaluate the relationship between G protein signaling modulator 2 expression and clinical characteristics. The threshold value of G protein signaling modulator 2 in the diagnosis of liver cancer was evaluated by a receiver–operating characteristic curve. Cox regression analysis and Kaplan-Meier curves were performed to evaluate the relationship between G protein signaling modulator 2 and liver cancer prognosis, which included overall and residual-free survival, and explored the prognostic value of G protein signaling modulator 2. Liver cancer survival analyses were validated by using the data of G protein signaling modulator 2 RNA-Seq from the International Cancer Genome Consortium. Results: The expression level of G protein signaling modulator 2 messenger RNA was remarkably higher in liver cancer than that in healthy tissues ( P < 2.2 × e−16), which was also validated by data from the GSE14520 database. In addition, high G protein signaling modulator 2 expression significantly correlated with histological grade ( P = .020), vital status ( P < .001), clinical ( P = .001), and T stage ( P = .001). The receiver–operating characteristic curves showed G protein signaling modulator 2 to be an advantageous diagnostic molecule for liver cancer (area under curve = 0.893). Furthermore, the results of Cox analysis and Kaplan-Meier curves suggested that the upregulation of G protein signaling modulator 2 expression is linked to poor prognosis and G protein signaling modulator 2 messenger RNA could be an independent predictor for liver cancer, which was validated by data from the International Cancer Genome Consortium database. Conclusions: G protein signaling modulator 2 messenger RNA was overexpressed in liver cancer, and G protein signaling modulator 2 is an independent prognostic factor. G protein signaling modulator 2 is expected to be a treatment target for cancer.

CANCERSIGN: a user-friendly and robust tool for identification and classification of mutational signatures and patterns in cancer genomes

Analyses of large somatic mutation datasets, using advanced computational algorithms, have revealed at least 30 independent mutational signatures in tumor samples. These studies have been instrumental in identification and quantification of responsible endogenous and exogenous molecular processes in cancer. The quantitative approach used to deconvolute mutational signatures is becoming an integral part of cancer research. Therefore, development of a stand-alone tool with a user-friendly graphical interface for analysis of cancer mutational signatures is necessary. In this manuscript, we introduce CANCERSIGN as an open access bioinformatics tool that uses raw mutation data (BED files) as input, and identifies 3-mer and 5-mer mutational signatures. CANCERSIGN enables users to identify signatures within whole genome, whole exome or pooled samples. It can also identify signatures in specific regions of the genome (defined by user). Additionally, this tool enables users to perform clustering on tumor samples based on the raw mutation counts as well as using the proportion of mutational signatures in each sample. Using this tool, we analysed all the whole genome somatic mutation datasets profiled by the International Cancer Genome Consortium (ICGC) and identified a number of novel signatures. By examining signatures found in exonic and non-exonic regions of the genome using WGS and comparing this to signatures found in WES data we observe that WGS can identify additional non-exonic signatures that are enriched in the non-coding regions of the genome while the deeper sequencing of WES may help identify weak signatures that are otherwise missed in shallower WGS data.