SATB1 enhances gastric cancer progression and metastasis via upregulation of CLIP4/DZIP1/ PRICKLE2 axis in gastric cancer patients

SATB1 (Special AT-rich sequence binding protein 1) plays key role in chromatin remodeling and geneexpression. SATB1 has been shown to promote invasion, migration and metastasis. However, the underlying molecular mechanisms, function and clinicopathological features of SATB1 in gastric cancer (GC) remains poorly understood. Here, we show that SATB1 plays critical role in GC progression. SATB1 upregulates in GC patients’ samples and shows co-apmlification with a subset of oncogenic proteins (PRICKLE2, ZEB1, CBWX7, WWTR1, ENAH, DZIP1, IGSF11, ZSCAN18, GFRA2, GFRA1, FGFR1, HDAC4, GHR, TIMP3, CLIP4, TAGLN and ILK) in different subtypes of GC. SATB1 shows positive correlation with GC promoting oncogenes and enhances the expression of metastasis associated genes in GC samples to potentiate GC progression. SATB1 enhances WNT/NOTCH signaling axis in GC samples. In contrast, STAB1 suppresses a network of tumor suppressor genes in GC samples. Moreover, SATB1 expression negatively correlates with GC patient’s survival. Importantly, we found that SATB1 co-amplified genes CLIP4, DZIP1 and PRICKLE2 independently involve in GC progression. Overexpression of CLIP4, DZIP1 and PRICKLE2 show poor survival rates in GC patients. DZIP1 empowers AKT3/FGF2/FGFR1 and CXCR3/CXCR6 in GC patients. PRICKLE2 enhances GC progression by targeting FGFR1/NOTCH3/WNT4 signaling. Taken together, these results identified novel roles of SATB1, CLIP4, DZIP1 and PRICKLE2 in the GC invasion, migration and metastasis using cancer bioinformatics approach. These results highlights clinical significance of SATB1, CLIP4, DZIP1 and PRICKLE2 in GC patients and furthermore, these proteins may serve as prognostic markers in GC. These results also provide SATB1, CLIP4, DZIP1 and PRICKLE2 as potential chemotherapeutic targets in treatment of gastric cancer patients.

The Association of MEG3 Gene rs7158663 Polymorphism With Cancer Susceptibility

Although the association of MEG3 gene rs7158663 polymorphism with cancer susceptibility has been investigated, the findings are inconsistent. The aim of this study was to analyze the association between the rs7158663 polymorphism and cancer susceptibility through a case-control study and meta-analysis. In a case-control study with 430 colorectal cancer (CRC) cases and 445 healthy controls, the rs7158663 polymorphism was genotyped by direct sequencing. STATA software was used to calculate the pooled odds ratio and 95% confidence interval in a meta-analysis including 4,649 cancer cases and 5,590 controls. Both the case-control study and meta-analysis showed that the rs7158663 polymorphism was associated with increased susceptibility to CRC. Individuals carrying the AA or GA genotype were more likely to develop CRC than those carrying the rs7158663 GG genotype. Interestingly, MEG3 expression was significantly lower in colorectal tissues of the AA or GA genotype compared to those of the rs7158663 GG genotype. In addition, the meta-analysis suggested that the rs7158663 polymorphism was also associated with increased susceptibility to breast cancer and gastric cancer. Bioinformatics analysis showed that the rs7158663 A allele contributed to the binding of hsa-miR-4307 and hsa-miR-1265 to MEG3. In conclusion, the current findings suggest that the MEG3 gene rs7158663 polymorphism may serve as a genetic marker for predicting the risk of cancers, such as breast cancer, gastric cancer and CRC. However, the sample size of the current study is still insufficient, especially in the subgroup analysis. Therefore large and well-designed studies are needed to validate our findings.

Signatures and Prognostic Values of Related Immune Targets in Tongue Cancer

Tongue cancer, as one of the most malignant oral cancers, is highly invasive and has a high risk of recurrence. At present, tongue cancer in the advanced stage is not obvious, easy to miss the opportunity of early diagnosis. It is important to find markers that can predict the occurrence and progression of tongue cancer. Bioinformatics analysis plays an important role in the acquisition of marker genes. GEO and TCGA data are very important public databases. In addition to expression data, TCGA database also contains corresponding clinical data. In this study, we screened three GEO datasets included GSE13601, GSE34105 and GSE34106 that met the standard. These data sets were combined using the SVA package to prepare the data for differential expression analysis, and then the LIMMA package was used to set the standard to p<0.05 and |log2 (FC)| ≥1.5. We got 170 DEGs (104, raised 66 downgrade). Besides, the DEseq package was used for differential expression analysis using the same criteria for samples in TCGA database. It ended up with 1589 DEGs (644 up-regulated, 945 down-regulated). By merging these two sets of DEGs, 5 common up-regulated DEGs (CCL20, SCG5, SPP1, KRT75 and FOLR3) and 15 common down-regulated DEGs were obtained. Further functional analysis of the DEGs showed that CCL20, SCG5 and SPP1 is closely related to prognosis and may be a therapeutic target of TSCC.

A pan-cancer analysis of the glutaminase and their association with prognosis, tumor microenvironment, and therapeutic targets

BackgroundGlutamine metabolism plays a key role in various biological processes of tumor. Glutaminase (GLS) is involved in Glutamine metabolism and plays a conserved regulatory role in the process. Nevertheless, there is no comprehensively analysis of GLS in pan-cancer.MaterialsComprehensive bioinformatics analysis was adopted to investigate the expression level, prognostic values, and association between expression of GLS and TME, immune cells' infiltration, immune checkpoint genes, TMB, MSI, drug sensitivity in pan-cancer. Bioinformatics tools including CCLE, immunophenoscore (IPS), Tumor Immune Dysfunction and Exclusion (TIDE), GSEA, and TIMER databases were used.ResultsDifferently expressed GLS between tumor and normal tissues were analyzed, and the clinical prognoses, MMR, MSI, and TMB in multiple types of cancer were associated with GLS expression. Furthermore, GLS closely correlated with tumor immunity and drug sensitivity, and found GLS were predicted to be involved in cancer metabolism and immunity pathways, through gene set enrichment analysis (GSEA).ConclusionThe GLS expression could be used as a prognostic biomarker for determining prognosis and provide further insights into anti-glutamine metabolism for cancer.

Hiding in the dark: pan-cancer characterization of expression and clinical relevance of CD40 to immune checkpoint blockade therapy

Highlights CD40 expression correlates with the type I anti-tumor response and better survival. Pan-cancer bioinformatics characterization reveals reduced CD40 expression in 11 cancer types, including RASmut melanoma compared to nevi. RAS mutation correlates with reduced CD40 expression in malignant melanoma. CD40 expression is associated with better response to immune checkpoint blockade therapy in melanoma.

Online Tools for Teaching Cancer Bioinformatics

The rise of deep molecular characterization with omics data as a standard in biological sciences has highlighted a need for expanded instruction in bioinformatics curricula. Many large biology data sets are publicly available and offer an incredible opportunity for educators to help students explore biological phenomena with computational tools, including data manipulation, visualization, and statistical assessment.

Epigenetic dysregulation of immune-related pathways in cancer: bioinformatics tools and visualization

Cancer immune evasion is one of the hallmarks of carcinogenesis. Cancer cells employ multiple mechanisms to avoid immune recognition and suppress antitumor immune responses. Recently, accumulating evidence has indicated that immune-related pathways are epigenetically dysregulated in cancer. Most importantly, the epigenetic footprint of immune-related pathways is associated with the patient outcome, underscoring the crucial need to understand this process. In this review, we summarize the current evidence for epigenetic regulation of immune-related pathways in cancer and describe bioinformatics tools, informative visualization techniques, and resources to help decipher the cancer epigenome.