New prognostic markers revealed by RNA-Seq transcriptome analysis after MYC silencing in a metastatic gastric cancer cell line

Cytokine-induced apoptosis inhibitor 1: a comprehensive analysis of potential diagnostic, prognosis, and immune biomarkers in invasive breast cancer

Background

Cytokine-induced apoptosis inhibitor 1 (CIAPIN1) is strictly associated with the incidence and progress of several malignant tumors, but its effect on invasive breast cancer (IBC) remains unclear. We directed to research the potential diagnostic and prognostic significance of CIAPIN1 in IBC.

Methods

The Cancer Genome Atlas (TCGA) database and Tumor Immune Estimation Resource (TIMER) database were utilized to examine CIAPIN1 expression level in IBC and its relationship with clinicopathological features. The diagnostic value and prognostic importance of CIAPIN1 in IBC were assessed by Kaplan-Meier analysis, Cox regression analysis, receiver operating characteristic (ROC) curve and nomogram model. The STRING database and enrichment analysis were utilized to discover the interacting proteins, biological roles and possible cellular mechanisms related to CIAPIN1. The methylation status of CIAPIN1 was analyzed using MethSurv database and the University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN). By using Spearman correlation assessment, how the expression of CIAPIN1 was related to TP53, immune checkpoint genes and immune cell infiltration was determined.

Results

CIAPIN1 mRNA and protein levels were overexpressed in IBC, and significantly correlated with T stage, histological type, age, ER status, PR status and PAM50 (P<0.001). CIAPIN1 overexpression significantly decreased overall survival, distant metastasis free survival (DMFS) and relapse free survival in IBC patients (P<0.001). Similarly, hypermethylation of CIAPIN1 was associated with adverse outcomes in IBC patients. Multivariate Cox analysis identified CIAPIN1 as a potential risk factor for disease specific survival (DSS) and progression free survival (PFS) in individuals with IBC. The outcomes of the ROC curve showed that CIAPIN1 had a better accuracy in predicting ER(-), PR(-) and Asian breast cancer subtypes. Furthermore, there was a substantial correlation between the CIAPIN1 expression level in IBC and immune cell infiltration, TP53, and immune checkpoint genes.

Conclusions

The high expression of CIAPIN1 in IBC is significantly related to the infiltration status of various tumor immune cells and the poor prognosis of IBC patients. According to this current study, CIAPIN1 is a promising diagnostic and prognostic marker for IBC.

Prediction of CIAPIN1 (Cytokine-Induced Apoptosis Inhibitor 1) Signaling Pathway and Its Role in Cholangiocarcinoma Metastasis

Cholangiocarcinoma (CCA), a malignancy of the biliary epithelium, can arise at any point in the biliary system. We previously reported that CIAPIN1 is detectable in the sera and that its overexpression was associated with poor prognosis and metastasis of CCA patients. In this study, we investigated further its expression in CCA tissues, biological functions, and related signaling pathways in CCA cells. First, we examined CIAPIN1 expression in CCA tissues of 39 CCA patients using immunohistochemistry (IHC). Then, CIAPIN1-related proteins expressed in CCA cells were identified using RNA interference (siRNA) and liquid chromatography–mass spectrometry (LC–MS/MS). To predict the functions and signaling pathways of CIAPIN1 in CCA cells, the identified proteins were analyzed using bioinformatics tools. Then, to validate the biological functions of CIAPIN1 in the CCA cell line, transwell migration/invasion assays were used. CIAPIN1 was overexpressed in CCA tissues compared with adjacent noncancerous tissues. Its overexpression was correlated with lymph node metastasis. Bioinformatic analyses predicted that CIAPIN1 is connected to the TGF-β/SMADs signaling pathway via nitric oxide synthase 1 (NOS1) and is involved in the metastasis of CCA cells. In fact, cell migration and invasion activities of the KKU-100 CCA cell line were significantly suppressed by CIAPIN1 gene silencing. Our results unravel its novel function and potential signaling pathway in metastasis of CCA cells. CIAPIN1 can be a poor prognostic factor and can be a promising target molecule for CCA chemotherapy.

A Personalized Genomics Approach of the Prostate Cancer

Decades of research identified genomic similarities among prostate cancer patients and proposed general solutions for diagnostic and treatments. However, each human is a dynamic unique with never repeatable transcriptomic topology and no gene therapy is good for everybody. Therefore, we propose the Genomic Fabric Paradigm (GFP) as a personalized alternative to the biomarkers approach. Here, GFP is applied to three (one primary—“A”, and two secondary—“B” & “C”) cancer nodules and the surrounding normal tissue (“N”) from a surgically removed prostate tumor. GFP proved for the first time that, in addition to the expression levels, cancer alters also the cellular control of the gene expression fluctuations and remodels their networking. Substantial differences among the profiled regions were found in the pathways of P53-signaling, apoptosis, prostate cancer, block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy, and sustained angiogenesis. ENTPD2, AP5M1 BAIAP2L1, and TOR1A were identified as the master regulators of the “A”, “B”, “C”, and “N” regions, and potential consequences of ENTPD2 manipulation were analyzed. The study shows that GFP can fully characterize the transcriptomic complexity of a heterogeneous prostate tumor and identify the most influential genes in each cancer nodule.

Downregulated genes by silencing MYC pathway identified with RNA-SEQ analysis as potential prognostic biomarkers in gastric adenocarcinoma

MYC overexpression is a common phenomenon in gastric carcinogenesis. In this study, we identified genes differentially expressed with a downregulated profile in gastric cancer (GC) cell lines with silenced MYC. The TTLL12, CDKN3, CDC16, PTPRA, MZT2B, UBE2T genes were validated using qRT-PCR, western blot and immunohistochemistry in tissues of 213 patients with diffuse and intestinal GC. We identified high levels of TTLL12, MZT2B, CDC16, UBE2T, associated with early and advanced stages, lymph nodes, distant metastases and risk factors such as H. pylori. Our results show that in the diffuse GC the overexpression of CDC16 and UBE2T indicate markers of poor prognosis higher than TTLL12. That is, patients with overexpression of these two genes live less than patients with overexpression of TTLL12. In the intestinal GC, patients who overexpressed CDC16 had a significantly lower survival rate than patients who overexpressed MZT2B and UBE2T, indicating in our data a worse prognostic value of CDC16 compared to the other two genes. PTPRA and CDKN3 proved to be important for assessing tumor progression in the early and advanced stages. In summary, in this study, we identified diagnostic and prognostic biomarkers of GC under the control of MYC, related to the cell cycle and the neoplastic process.

Identification of novel biomarkers, MUC5AC, MUC1, KRT7, GAPDH, CD44 for gastric cancer

Gastric cancer (GC) is one of the most common malignant tumors in the world, and it is also the third largest cause of cancer-related death in the world. As far as we know, no biomarker has been widely accepted for early diagnosis and prognosis prediction of gastric cancer. The purpose of this study is to find potential biomarkers to predict the prognosis of GC. The gene expression profiles of GSE2685 were downloaded from GEO database. Morpheus was used to calculate the differentially expressed genes (DEGs) between primary advanced gastric cancer tissues and noncancerous gastric tissues. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein-protein interaction (PPI) network of DEGs was constructed. Kaplan-Meier Plotter was used to determine the overall survival (OS) outcomes of UC5AC, MUC1, KRT7, GAPDH, CD44, and GEPIA was used to determine the Pearson correlation analysis. In total, 710 DEGs were identified in GC, including 396 upregulated genes and 314 downregulated genes. GO enrichment revealed that they were mainly enriched in binding, catalytic activity, cellular process and cell. KEGG pathway revealed that they were mainly enriched in metabolic pathways, pathways in cancer and PI3K-Akt signaling pathway. MUC5AC, MUC1, KRT7, GAPDH, CD44 were identified from the PPI network. MUC5AC, MUC1, KRT7, GAPDH, CD44 were demonstrated to have prognostic value for patients with GC. MUC5AC, MUC1 exhibited low expression levels in GC tissues, KRT7, GAPDH, CD44 presented high expression levels in GC tissues. In particular, KRT7 is hardly expressed in normal gastric tissues. MUC5AC and MUC1 were negatively correlated with GAPDH, CD44, respectively; and GAPDH was positively correlated with CD44 and KRT7, respectively. Moreover. MUC5AC, MUC1, KRT7, GAPDH, and CD44 are not only related to GC but also to apoptosis pathway. Results from the present study suggested that MUC5AC, MUC1, KRT7, GAPDH, CD44 may represent novel prognostic biomarkers for GC.