Six-long non-coding RNA signature predicts recurrence-free survival in hepatocellular carcinoma

Six-long non-coding RNA signature predicts recurrence-free survival in hepatocellular carcinoma.

The clinical relevance of gene expression based prognostic signatures in colorectal cancer.

The protein kinase CK2 (CK2) family encompasses a small number of acidophilic serine/threonine kinases that phosphorylate substrates involved in numerous biological processes including apoptosis, cell proliferation, and the DNA damage response. CK2 has also been implicated in many human malignancies and other disorders including Alzheimer's and Parkinson's diseases, and COVID-19. Interestingly, no single mechanism describes how CK2 is regulated, including activation by external proteins or domains, phosphorylation, or dimerization. Furthermore, the kinase has an elongated activation loop that locks the kinase into an active conformation, leading CK2 to be labelled a constitutively active kinase. This presents an interesting paradox that remains unanswered: how can a constitutively active kinase regulate biological processes that require careful control? Here, we highlight a selection of studies where CK2 activity is regulated at the substrate level, and discuss them based on the regulatory mechanism. Overall, this review describes numerous biological processes where CK2 activity is regulated, highlighting how a constitutively active kinase can still control numerous cellular activities. It is also evident that more research is required to fully elucidate the mechanisms that regulate CK2 and what causes aberrant CK2 signaling in disease.

CK2 Regulation: Perspectives in 2021.

Long non-coding RNA MINCR aggravates colon cancer via regulating miR-708-5p-mediated Wnt/β-catenin pathway.

Circular RNA circSNX6 promotes sunitinib resistance in renal cell carcinoma through the miR-1184/GPCPD1/ lysophosphatidic acid axis

Objective: Our study aimed to construct a robust long non-coding RNA (lncRNA) prognostic signature for colorectal cancer (CRC) metastasis. Methods: Differentially expressed lncRNAs were identified between metastatic CRC and non-metastatic CRC samples from The Cancer Genome Atlas Database (TCGA) using the edgeR package. The differentially expressed lncRNAs with prognosis of patients with CRC metastasis were identified by univariate Cox regression analysis, followed by a stepwise multivariate Cox regression model. The survminer package in R was used to identify the optimal cutoff point for high-risk and low-risk groups. The receiver operating characteristic (ROC) curves were plotted to assess this signature. To explore potential signaling pathways associated with these lncRNAs, Gene Set Enrichment Analysis (GSEA) was performed. Results: A 6-lncRNA signature was built based on the lncRNA expression profile for CRC metastasis. The optimal cutoff value was used to classify high-risk and low-risk groups using the survminer package. The high-risk groups could have poorer survival time than the low-risk groups. ROC curve result indicated that this lncRNA signature had high sensitivity and accuracy. GSEA analysis results showed that the six lncRNAs were significantly enriched in several CRC metastasis-related signaling pathways such as "cell cycle," "DNA replication," "mismatch repair," "oxidative phosphorylation," "regulation of autophagy," and "insulin signaling pathway." Conclusion: Our study constructed a 6-lncRNA model for predicting the survival outcomes of patients with CRC metastasis, which could become potential prognostic biomarkers, and therapeutic targets for CRC metastasis.

/pdf/a-robust-6-lncrna-prognostic-signature-for-predicting-the-3z0koe69sz.pdf

A Robust 6-lncRNA Prognostic Signature for Predicting the Prognosis of Patients With Colorectal Cancer Metastasis.

miR-1184 regulates the proliferation and apoptosis of colon cancer cells via targeting CSNK2A1.

Aim To develop an autophagy-gene-based signature that could help to anticipate the therapeutic effects of Colorectal Cancer (CRC). Methods We downloaded the gene expression profiles of CRC samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. Genes with significant prognostic value in CRC were screened through univariate Cox regression analysis, while the LASSO Cox regression method was applied to screen optimal genes to construct the autophagy-related prognostic signature. Results 11 autophagy genes were identified and selected for the establishment of prognosis prediction model for CRC patients. The CRC patients were classified into the low- and high-risk groups according to the optimal cutoff value. The time-dependent ROC curves indicated the good performance of this model in prognosis prediction, with AUC values of 0.66, 0.66, and 0.67 at 1, 3 and 5 years for TCGA samples, as well as AUC values of 0.63, 0.65 and 0.64 for GEO samples, respectively. The multivariate Cox regression analysis results confirmed risk score as the independent marker for prognosis prediction in CRC. Besides, the constructed nomogram also had high predictive value. The results analysis on the tumor infiltrating immune cells (TIICs) relative ratios and mRNA levels of key immune checkpoint receptors indicated the signature was closely related to immune microenvironment of CRC in the context of TIICs and immune checkpoint receptors' mRNA level. The proportion of MSI-L + MSI-H in the high-risk group was higher than that in the low-risk group. Moreover, the tumor purity was evaluated by estimate function package suggested that lower tumor purity in CRC might lead to a poorer prognosis. Conclusion The autophagy-related features obtained in this study were able to divide the CRC patients into low- and high-risk groups, which should be contribute to the decision-making of CRC treatment.

/pdf/a-signature-based-on-11-autophagy-genes-for-prognosis-hnqbyy9qcb.pdf

A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer.

Long noncoding RNAs (lncRNAs) are ubiquitous transcripts that play key roles in regulating gene expression at the levels of transcription, RNA processing, and translation. Aberrant expression and mutations of lncRNAs represent a driving force behind oncogenesis and development of tumours. However, most of the lncRNAs are still being undiscovered, and conclusive experimental evidence for their functional relevance continues to be lacking for most malignancies. We have found that lncRNA long intergenic non-protein-coding RNA 341 (LINC00341) is aberrantly downregulated by microarray-based screenings on nonmetastatic and metastatic colorectal carcinoma (CRC) specimens; LINC00341 is a novel long intergenic non-protein-coding RNA with unknown functions. LINC00341 overexpression restricts tumour growth and promotes its apoptosis. Instead, LINC00341 silencing accelerates CRC cell proliferation and migration. RNA-pulldown assay identifies LINC00341 physically binds to HMGB2 and stabilizes the localization of HMGB2 in the cytoplasm. Notably, LINC00341 knockdown leads to the shift of HMGB2 into nuclear, in which it triggers epithelial to mesenchymal transition (EMT) programming. Moreover, LINC00341 can also promote apoptosis. SIGNIFICANCE OF THE STUDY: LncRNAs are ubiquitous transcripts that play key roles in regulating gene expression at the levels of transcription, RNA processing, and translation. Aberrant expression and mutations of lncRNAs represent a driving force behind oncogenesis and development of tumours. However, the function of lncRNA still needs further exploration. Our study has revealed a new noncoding RNA-mediated regulatory network that highly likely protects colorectal carcinoma by preventing migration and apoptosis. The results will help further explore the molecular details about the progression of colorectal carcinoma and stimulate efforts to develop effective therapies.

The long noncoding RNA LINC00341 suppresses colorectal carcinoma by preventing cell migration and apoptosis.

Long noncoding RNAs (lncRNAs) have recently emerged as important biomarkers of cancer progression. Here, we proposed to develop a lncRNA-based signature with a prognostic value for colorectal cancer (CRC) overall survival (OS). Through mining microarray datasets, we analyzed the lncRNA expression profiles of 122 patients with CRC from Gene Expression Omnibus. Associations between lncRNA and CRC OS were firstly evaluated through univariate Cox regression analysis. A random survival forest method was applied for further screening of the lncRNA signature, which resulted in eight lncRNAs, including PEG3-AS1, LOC100505715, MINCR, DBH-AS1, LINC00664, FAM224A, LOC642852, and LINC00662. Combination of the eight lncRNAs weighted by their multivariate Cox regression coefficients formed a prognostic signature, through which, we could divide the 122 patients with CRC into two subgroups with significantly different OS. Good robustness of the lncRNA signature's prognostic value was verified through an independent data set consisting of 55 patients with CRC. In addition, gene set enrichment analysis indicated the potential association between high prognostic value and oxygen metabolism-related processes. This result should indicate that lncRNAs could be a useful signature for CRC prognosis.

An eight-long noncoding RNA expression signature for colorectal cancer patients' prognosis

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