Transcriptional information underlying the generation of CSCs and the construction of a nine-mRNA signature to improve prognosis prediction in colorectal cancer

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

BACKGROUND

Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches.

METHODS

To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins.

RESULTS

We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC.

CONCLUSION

The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

CDC20 is a novel biomarker for improved clinical predictions in epithelial ovarian cancer

Epithelial ovarian cancer (EOC), a common tumor of the female reproductive system, ranks first in fatalities among gynecological malignancies. Most patients find tumors at late stage and have extremely poor prognoses, which necessitates improvements in early detection. This study applied bioinformatic methods to identify potential biomarkers of EOC. First, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on differentially expressed genes (DEGs) and hub genes, and a protein-protein interaction (PPI) network was constructed. The network of hub genes was analyzed using GeneMANIA, and an analysis of biological processes was constructed with BINGO. Lastly, hub genes were analyzed for EOC-related oncology using the Oncomine and TCGA databases, and the cBioPortal online platform. Overall, cell division cycle 20 (CDC20) was identified as a key gene in EOC. Short hairpin RNA (shRNA) was used to silence CDC20 to explore its effects on EOC cell proliferation, apoptosis and SRY-related HMG-box 2 (SOX2) expression. DEGs were enriched in pathways related to cell cycle signaling, cancer, progesterone-mediated oocyte maturation, Wnt signaling and P53 signaling. Analysis revealed high expression of CDC20 in EOC tissues and a correlation with histology and tumor grade. CDC20 levels are highest in serous adenocarcinoma, when compared to ovarian clear cell carcinoma, ovarian endometrioid carcinoma and mucinous adenocarcinoma. High CDC20 expression within the tumor is associated with poor EOC prognosis. After silencing CDC20, EOC cell proliferation and migration decreased, apoptosis increased, and SOX2 expression decreased. In conclusion, CDC20 is likely a key biomarker of EOC and may act as an upstream regulator of SOX2 to mediate the SOX2 signaling in the progression of EOC. Future application of CDC20 analysis to early detection may improve prognosis, and it has the potential to be a therapeutic target.

Tubulin Isotypes: Emerging Roles in Defining Cancer Stem Cell Niche

Although the role of microtubule dynamics in cancer progression is well-established, the roles of tubulin isotypes, their cargos and their specific function in the induction and sustenance of cancer stem cells (CSCs) were poorly explored. But emerging reports urge to focus on the transport function of tubulin isotypes in defining orchestrated expression of functionally critical molecules in establishing a stem cell niche, which is the key for CSC regulation. In this review, we summarize the role of specific tubulin isotypes in the transport of functional molecules that regulate metabolic reprogramming, which leads to the induction of CSCs and immune evasion. Recently, the surface expression of GLUT1 and GRP78 as well as voltage-dependent anion channel (VDAC) permeability, regulated by specific isotypes of β-tubulins have been shown to impart CSC properties to cancer cells, by implementing a metabolic reprogramming. Moreover, βIVb tubulin is shown to be critical in modulating EphrinB1signaling to sustain CSCs in oral carcinoma. These tubulin-interacting molecules, Ephrins, GLUT1 and GRP78, are also important regulators of immune evasion, by evoking PD-L1 mediated T-cell suppression. Thus, the recent advances in the field implicate that tubulins play a role in the controlled transport of molecules involved in CSC niche. The indication of tubulin isotypes in the regulation of CSCs offers a strategy to specifically target those tubulin isotypes to eliminate CSCs, rather than the general inhibition of microtubules, which usually leads to therapy resistance.

Microtubule-Associated Protein ATIP3, an Emerging Target for Personalized Medicine in Breast Cancer

Breast cancer is the leading cause of death by malignancy among women worldwide. Clinical data and molecular characteristics of breast tumors are essential to guide clinician’s therapeutic decisions. In the new era of precision medicine, that aims at personalizing the treatment for each patient, there is urgent need to identify robust companion biomarkers for new targeted therapies. This review focuses on ATIP3, a potent anti-cancer protein encoded by candidate tumor suppressor gene MTUS1, whose expression levels are markedly down-regulated in breast cancer. ATIP3 is a microtubule-associated protein identified both as a prognostic biomarker of patient survival and a predictive biomarker of breast tumors response to taxane-based chemotherapy. We present here recent studies pointing out ATIP3 as an emerging anti-cancer protein and a potential companion biomarker to be combined with future personalized therapy against ATIP3-deficient breast cancer.

Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis

Background

Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer.

Methods

The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram.

Results

Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients’ age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions.

Conclusions

The 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-020-02116-y.