Low incidence of IL6ST (gp130) mutations in exon 6 in lung cancer of a Chinese cohort

The Human GP130 Cytokine Receptor and Its Expression-an Atlas and Functional Taxonomy of Genetic Variants

Genetic variants in IL6ST encoding the shared cytokine receptor for the IL-6 cytokine family GP130 have been associated with a diverse number of clinical phenotypes and disorders. We provide a molecular classification for 59 reported rare IL6ST pathogenic or likely pathogenic variants and additional polymorphisms. Based on loss- or gain-of-function, cytokine selectivity, mono- and biallelic associations, and variable cellular mosaicism, we grade six classes of IL6ST variants and explore the potential for additional variants. We classify variants according to the American College of Medical Genetics and Genomics criteria. Loss-of-function variants with (i) biallelic complete loss of GP130 function that presents with extended Stüve-Wiedemann Syndrome; (ii) autosomal recessive hyper-IgE syndrome (HIES) caused by biallelic; and (iii) autosomal dominant HIES caused by monoallelic IL6ST variants both causing selective IL-6 and IL-11 cytokine loss-of-function defects; (iv) a biallelic cytokine-specific variant that exclusively impairs IL-11 signaling, associated with craniosynostosis and tooth abnormalities; (v) somatic monoallelic mosaic constitutively active gain-of-function variants in hepatocytes that present with inflammatory hepatocellular adenoma; and (vi) mosaic constitutively active gain-of-function variants in hematopoietic and non-hematopoietic cells that are associated with an immune dysregulation syndrome. In addition to Mendelian IL6ST coding variants, there are common non-coding cis-acting variants that modify gene expression, which are associated with an increased risk of complex immune-mediated disorders and trans-acting variants that affect GP130 protein function. Our taxonomy highlights IL6ST as a gene with particularly strong functional and phenotypic diversity due to the combinatorial biology of the IL-6 cytokine family and predicts additional genotype-phenotype associations.

Extracellular Matrix-Based Gene Expression Signature Defines Two Prognostic Subtypes of Hepatocellular Carcinoma With Different Immune Microenvironment Characteristics

Background: Accumulating evidence has suggested that the extracellular matrix (ECM) plays a vital role in the development and progression of cancer, and could be recognized as a biomarker of the response to immunotherapy. However, the effect of the ECM signature in hepatocellular carcinoma (HCC) is not well understood.

Methods: HCC patients derived from the TCGA-LIHC dataset were clustered according to the ECM signature. The differences in prognosis, functional enrichment, immune infiltration, and mutation characteristics between distinct molecular clusters were examined, and its predictive value on the sensitivities to chemotherapy and immunotherapy was further analyzed. Then, a prognostic model was built based on the ECM-related gene expression pattern.

Results: HCC patients were assigned into two molecular subtypes. Approximately 80% of HCC patients were classified into cluster A with poor prognosis, more frequent TP53 mutation, and lower response rate to immunotherapy. In contrast, patients in cluster B had better survival outcomes and higher infiltration levels of dendritic cells, macrophages, and regulatory T cells. The prognostic risk score model based on the expression profiles of six ECM-related genes (SPP1, ADAMTS5, MMP1, BSG, LAMA2, and CDH1) demonstrated a significant association with higher histologic grade and advanced TNM stage. Moreover, the prognostic risk score showed good performance in both the training dataset and validation dataset, as well as improved prognostic capacity compared with TNM stage.

Conclusions: We characterized two HCC subtypes with distinct clinical outcomes, immune infiltration, and mutation characteristics. A novel prognostic model based on the ECM signature was further developed, which may contribute to individualized prognostic prediction and aid in clinical decision-making.

Transcriptomic analysis associated with reversal of cisplatin sensitivity in drug resistant osteosarcoma cells after a drug holiday

Background

Resistance to chemotherapy is one of the major hurdles in current cancer therapy. With the increasing occurrence of drug resistance, a paradigm shift in treatment strategy is required. Recently “medication vacation” has emerged as a unique, yet uncomplicated strategy in which withdrawal of drug pressure for certain duration allowed tumor cells to regain sensitivity to the drug. However, little is known about the molecular alterations associated with such an outcome.

Methods

In this study, human osteosarcoma (OS) cells resistant to the extensively used drug cisplatin, were withdrawn from drug pressure, and thereafter cytotoxic response of the cells to the drug was evaluated. We further performed next-generation RNA sequencing and compared transcriptome between parental (OS), resistant (OS-R) and the drug withdrawn (OS-DW) cells. Differentially expressed transcripts were identified, and biological association network (BAN), gene ontology (GO) and pathway enrichment analysis of the differentially regulated transcripts were performed to identify key events associated with withdrawal of drug pressure.

Results

Following drug withdrawal, the sensitivity of the cells to the drug was found to be regained. Analysis of the expression profile showed that key genes like, IRAK3, IL6ST, RELA, AKT1, FKBP1A and ADIPOQ went significantly down in OS-DW cells when compared to OS-R. Also, genes involved in Wnt signaling, PI3K-Akt, Notch signaling, and ABC transporters were drastically down-regulated in OS-DW cells compared to OS-R. Although, a very small subset of genes maintained similar expression pattern between OS, OS-R and OS-DW, nonetheless majority of the transcriptomic pattern of OS-DW was distinctively different and unique in comparison to either the drug sensitive OS or drug resistant OS-R cells.

Conclusion

Our data suggests that though drug withdrawal causes reversal of sensitivity, the transcriptomic pattern does not necessarily show significant match with resistant or parental control cells. We strongly believe that exploration of the molecular basis of drug holiday might facilitate additional potential alternative treatment options for aggressive and resistant cancers.

High expression of APC is an unfavorable prognostic biomarker in T4 gastric cancer patients

BACKGROUND

Adenoma polyposis coli (APC) mutation is associated with tumorigenesis via the Wnt signaling pathway.

AIM

To investigate the clinical features and mechanism of APC expression in gastric cancer (GC).

METHODS

Based on APC expression profile, the related genome-wide mRNA expression, microRNA (miRNA) expression, and methylation profile in GC, the relationship between APC and GC, as well as the prognostic significance of APC were systematically analyzed by multi-dimensional methods.

RESULTS

We found that high expression of APC (APC^high) was significantly associated with adverse outcomes of T4 GC patients. Genome-wide gene expression analysis revealed that varying APC expression levels in GC were associated with some important oncogenes, and corresponding cellular functional pathways. Genome-wide miRNA expression analysis indicated that most of miRNAs associated with high APC expression were downregulated. The mRNA-miRNA regulatory network analysis revealed that down-regulated miRNAs affected their inhibitory effect on tumor genes. Genome-wide methylation profiles associated with APC expression showed that there was differential methylation between the APC^high and APC^low groups. The number of hypermethylation sites was larger than that of hypomethylation sites, and most of hypermethylation sites were enriched in CpG islands.

CONCLUSION

Our research demonstrated that high APC expression is an unfavorable prognostic factor for T4 GC patients and may be used as a novel biomarker for pathogenesis research, diagnosis, and treatment of GC.

Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the "Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project" show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.