The Associations between RNA Splicing Complex Gene SF3A1 Polymorphisms and Colorectal Cancer Risk in a Chinese Population

Cell-cycle and apoptosis related and proteomics-based signaling pathways of human hepatoma Huh-7 cells treated by three currently used multi-RTK inhibitors

Sorafenib, lenvatinib and regorafenib, the multi-RTK inhibitors with potent anti-angiogenesis effects, are currently therapeutic drugs generally recommended for the patients with advanced hepatocellular carcinoma (HCC). To date, however, there have been no published studies on the mechanism underling differential effects of the three drugs on HCC cell proliferation, and the proteomic analysis in HCC cell lines treated by regorafenib or lenvatinib. The present study for the first time performed a direct comparison of the cell cycle arrest and apoptosis induction in the Huh-7 cells caused by sorafenib, regorafenib and lenvatinib at respective IC₅₀ using flow cytometry technique, as well as their pharmacological interventions for influencing whole cell proteomics using tandem mass tag-based peptide-labeling coupled with the nLC-HRMS technique. Sorafenib, regorafenib and lenvatinib at respective IC₅₀ drove the remaining surviving Huh-7 cells into a G₀/G₁ arrest, but lenvatinib and regorafenib were much more effective than sorafenib. Lenvatinib produced a much stronger induction of Huh-7 cells into early apoptosis than sorafenib and regorafenib, while necrotic cell proportion induced by regorafenib was 2.4 times as large as that by lenvatinib. The proteomic study revealed 419 proteins downregulated commonly by the three drugs at respective IC₅₀. KEGG pathway analysis of the downregulated proteins indicated the ranking of top six signaling pathways including the spliceosome, DNA replication, cell cycle, mRNA surveillance, P53 and nucleotide excision repair involved in 33 proteins, all of which were directly related to their pharmacological effects on cell cycle and cell apoptosis. Notably, lenvatinib and regorafenib downregulated the proteins of PCNA, Cyclin B1, BCL-xL, TSP1, BUD31, SF3A1 and Mad2 much more strongly than sorafenib. Moreover, most of the proteins in the P53 signaling pathway were downregulated with lenvatinib and regorafenib by more than 36% at least. In conclusion, lenvatinib and regorafenib have much stronger potency against Huh-7 cell proliferation than sorafenib because of their more potent effects on cell cycle arrest and apoptosis induction. The underling mechanism may be at least due to the 33 downregulated proteins centralizing the signal pathways of cell cycle, p53 and DNA synthesis based on the present proteomics study.

Cervical cancer in low and middle-income countries

Cervical cancer is a malignant tumour that occurs in the cervix and is classified into two histological types, adenocarcinoma and squamous cell carcinoma (SCC); SCC is more common and accounts for 70% of all cases. In 2018 there were ~569,000 new cases of cervical cancer diagnosed worldwide and ~311,000 deaths were attributed to cervical cancer. Of these, between 84 and 90% occurred in low- and middle-income countries (LMICs) such as South Africa, India, China and Brazil. The most common cause of cervical cancer is persistent infection caused by the sexually transmitted human papilloma virus. Other factors that contribute to the incidence of cervical cancer include geography, traditional practices and beliefs, the screening levels, socioeconomic status, healthcare access, public awareness, use of oral contraceptives, smoking and co-infection with HIV. An estimated 11 million women from LMICs will be diagnosed with cervical cancer in the next 10-20 years. The aim of this review was to explore various types of genetic and epigenetic factors that influence the development, progression or suppression of cervical cancer.

Systematic analysis and prediction model construction of alternative splicing events in hepatocellular carcinoma: a study on the basis of large-scale spliceseq data from The Cancer Genome Atlas

Growing evidence showed that alternative splicing (AS) event is significantly related to tumor occurrence and progress. This study was performed to make a systematic analysis of AS events and constructed a robust prediction model of hepatocellular carcinoma (HCC). The clinical information and the genes expression profile data of 335 HCC patients were collected from The Cancer Genome Atlas (TCGA). Information of seven types AS events were collected from the TCGA SpliceSeq database. Overall survival (OS) related AS events and splicing factors (SFs) were identified using univariate Cox regression analysis. The corresponding genes of OS-related AS events were sent for gene network analysis and functional enrichment analysis. Optimal OS-related AS events were selected by LASSO regression to construct prediction model using multivariate Cox regression analysis. Prognostic value of the prediction models were assessed by receiver operating characteristic (ROC) curve and KaplanMeir survival analysis. The relationship between the Percent Spliced In (PSI) value of OS-related AS events and SFs expression were analyzed using Spearman correlation analysis. And the regulation network was generated by Cytoscape. A total of 34,163 AS events were identified, which consist of 3,482 OS-related AS events. UBB, UBE2D3, SF3A1 were the hub genes in the gene network of the top 800 OS-related AS events. The area under the curve (AUC) of the final prediction model based on seven types OS-related AS events was 0.878, 0.843, 0.821 in 1, 3, 5 years, respectively. Upon multivariate analysis, risk score (All) served as the risk factor to independently predict OS for HCC patients. SFs HNRNPH3 and HNRNPL were overexpressed in tumor samples and were signifcantly associated with the OS of HCC patients. The regulation network showed prominent correlation between the expression of SFs and OS-related AS events in HCC patients. The final prediction model performs well in predicting the prognosis of HCC patients. And the findings in this study improve our understanding of the association between AS events and HCC.

Broad regulation of gene isoform expression by Wnt signaling in cancer

Differential gene isoform expression is a ubiquitous mechanism to enhance proteome diversity and maintain cell homeostasis. Mechanisms such as splicing that drive gene isoform variability are highly dynamic and responsive to changes in cell signaling pathways. Wnt/β-catenin signaling has profound effects on cell activity and cell fate and is known to modify several splicing events by altering the expression of individual splicing factors. However, a global assessment of how extensively Wnt signaling regulates splicing and other mechanisms that determine mRNA isoform composition in cancer is lacking. We used deep time-resolved RNA-seq in two independent in vivo Wnt-addicted tumor models during treatment with the potent Wnt inhibitor ETC-159 and examined Wnt regulated splicing events and splicing regulators. We found 1025 genes that underwent Wnt regulated variable exon usage leading to isoform expression changes. This was accompanied by extensive Wnt regulated changes in the expression of splicing regulators. Many of these Wnt regulated events were conserved in multiple human cancers, and many were linked to previously defined cancer-associated splicing quantitative trait loci. This suggests that the Wnt regulated splicing events are components of fundamental oncogenic processes. These findings demonstrate the wide-ranging effects of Wnt signaling on the isoform composition of the cell and provides an extensive resource of expression changes of splicing regulators and gene isoforms regulated by Wnt signaling.

Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer

BACKGROUND

Breast cancer is a complex disease in which different genomic patterns exists depending on different subtypes. Recent researches present that multiple subtypes of breast cancer occur at different rates, and play a crucial role in planning treatment. To better understand underlying biological mechanisms on breast cancer subtypes, investigating the specific gene regulatory system via different subtypes is desirable.

METHODS

Gene expression, as an intermediate phenotype, is estimated based on methylation profiles to identify the impact of epigenomic features on transcriptomic changes in breast cancer. We propose a kernel weighted l1-regularized regression model to incorporate tumor subtype information and further reveal gene regulations affected by different breast cancer subtypes. For the proper control of subtype-specific estimation, samples from different breast cancer subtype are learned at different rate based on target estimates. Kolmogorov Smirnov test is conducted to determine learning rate of each sample from different subtype.

RESULTS

It is observed that genes that might be sensitive to breast cancer subtype show prediction improvement when estimated using our proposed method. Comparing to a standard method, overall performance is also enhanced by incorporating tumor subtypes. In addition, we identified subtype-specific network structures based on the associations between gene expression and DNA methylation.

CONCLUSIONS

In this study, kernel weighted lasso model is proposed for identifying subtype-specific associations between gene expressions and DNA methylation profiles. Identification of subtype-specific gene expression associated with epigenomic changes might be helpful for better planning treatment and developing new therapies.

Exome sequencing in one family with gastric- and rectal cancer

Background

Heritable factors are well known to increase the risk of cancer in families. Known susceptibility genes account for a small proportion of all colorectal cancer cases. The aim of this study was to identify the genetic background in a family suggested to segregate a dominant cancer syndrome with a high risk of rectal- and gastric cancer. We performed whole exome sequencing in three family members, 2 with rectal cancer and 1 with gastric cancer and followed it up in additional family members, other patients and controls.

Results

We identified 12 novel non-synonymous single nucleotide variants, which were shared among 5 affected members of this family. The mutations were found in 12 different genes; DZIP1L, PCOLCE2, IGSF10, SUCNR1, OR13C8, EPB41L4B, SEC16A, NOTCH1, TAS2R7, SF3A1, GAL3ST1, and TRIOBP. None of the mutations was suggested as a high penetrant mutation. It was not possible to completely rule out any of the mutations as contributing to disease, although seven were more unlikely than the others. Neither did we rule out the effect of all thousands of intronic, intergenic and synonymous variants shared between the three persons used for exome sequencing.

Conclusions

We propose this family, suggested to segregate dominant disease, could be an example of complex inheritance.