Expression level of long noncoding RNA NKILA-miR103-miR107 inflammatory axis and its clinical significance as potential biomarker in patients with colorectal cancer

Jafari, Davood; Noorbakhsh, Farshid; Delavari, Alireza; Tavakkoli-Bazzaz, Javad; Farashi-Bonab, Samad; Abdollahzadeh, Rasoul; Rezaei, Nima

doi:10.4103/jrms.jrms_943_19

Cited by 16 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, some new biomarkers were identified in basic scientific settings. As summarized in Table 1, for instance, microRNA 21 (miR-21) was identified as a positive regulator of Ki-67 and promoter of NF-κB activity in colitis-associated CRC mouse models [71]. Other targets were the combination of NF-κB-interacting lncRNA (NKILA) microRNA 103 (miR-103) and microRNA 107 (miR-107), which were shown to be upregulated or downregulated in CRC compared to normal tissue [72].…”

Section: Nf-κb Signaling-related Crc Biomarkersmentioning

confidence: 99%

The Pivotal Player: Components of NF-κB Pathway as Promising Biomarkers in Colorectal Cancer

Martin

Sun

Motolani

et al. 2021

IJMS

View full text Add to dashboard Cite

Over the last several decades, colorectal cancer (CRC) has been one of the most prevalent cancers. While significant progress has been made in both diagnostic screening and therapeutic approaches, a large knowledge gap still remains regarding the early identification and treatment of CRC. Specifically, identification of CRC biomarkers that can help with the creation of targeted therapies as well as increasing the ability for clinicians to predict the biological response of a patient to therapeutics, is of particular importance. This review provides an overview of CRC and its progression stages, as well as the basic types of CRC biomarkers. We then lay out the synopsis of signaling pathways related to CRC, and further highlight the pivotal and multifaceted role of nuclear factor (NF) κB signaling in CRC. Particularly, we bring forth knowledge regarding the tumor microenvironment (TME) in CRC, and its complex interaction with cancer cells. We also provide examples of NF-κB signaling-related CRC biomarkers, and ongoing efforts made at targeting NF-κB signaling in CRC treatment. We conclude and anticipate that with more emerging novel regulators of the NF-κB pathway being discovered, together with their in-depth characterization and the integration of large groups of genomic, transcriptomic and proteomic data, the day of successful development of more ideal NF-κB inhibitors is fast approaching.

show abstract

Section: Nf-κb Signaling-related Crc Biomarkersmentioning

confidence: 99%

The Pivotal Player: Components of NF-κB Pathway as Promising Biomarkers in Colorectal Cancer

Martin

Sun

Motolani

et al. 2021

IJMS

View full text Add to dashboard Cite

show abstract

“…Conversely, this immune reaction can also contribute to tumor growth [20] . Studies suggest that chronic in ammation could contribute to CRC [21] , which is a leading cause of this type of cancer [22] . The growth of colorectal cells is affected by the combined bene cial and detrimental effects of pyroptosis.…”

Section: Introductionmentioning

confidence: 99%

Prognosis analysis of pyroptosis- and aging-related genes in colorectal cancer based on bioinformatic analysis

Pan,

Jin

2024

Preprint

View full text Add to dashboard Cite

Background Colorectal cancer (CRC) is the most prevalent gastrointestinal cancer worldwide. Our goal was to construct a model based on pyroptosis- and aging-related genes (PARGs) to predict CRC outcomes of colorectal cancer. Methods The Colon Adenocarcinoma/Rectal Adenocarcinoma Esophageal Carcinoma (COADREAD) dataset from the cancer genome atlas (TCGA) was obtained using R. Colorectal cancer-related datasets, namely, GSE74602, GSE87211, and GSE161158 were acquired from the Gene Expression Omnibus (GEO) database. PARGs were collected from various sources such as the GeneCards database, Molecular Signatures Database (MSigDB), and relevant literature. Differential expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed using R. Prognostic models were constructed utilizing LASSO (least absolute shrinkage and selection) regression analyses. Column line plots and calibration curve plots were generated using the R package. Immunohistochemical analyses were performed using the HPA (Human Protein Atlas) database. Results To obtain sets of genes related to both pyroptosis and aging (PARGs), we identified overlapping genes from two distinct datasets: one consisting of genes associated with pyroptosis (PRGs), and the other consisting of genes associated with aging (ARGs). We then created a risk signature that encompassed both pyroptosis and aging factors, which was further validated using diagnostic tools such as a Calibration Curve and decision curve analysis (DCA). The risk score derived from this signature significantly affects the overall survival of patients (CRC) patients. The stability and accuracy of this association were further confirmed using stratified survival analysis and DCA. Additionally, GSEA was performed to obtain results for both high-risk and low-risk groups. Conclusions CRC severity may be predicted using the PARGs signature, which is a reliable prognostic analysis model.

show abstract

Chimeric Antigen Receptor (CAR) T Cell Immunotherapy for Solid Tumors

Esmaeilzadeh

Jafari

Elahi

et al. 2023

Interdisciplinary Cancer Research

View full text Add to dashboard Cite

Expression level of long noncoding RNA NKILA-miR103-miR107 inflammatory axis and its clinical significance as potential biomarker in patients with colorectal cancer

Cited by 16 publications

References 31 publications

The Pivotal Player: Components of NF-κB Pathway as Promising Biomarkers in Colorectal Cancer

The Pivotal Player: Components of NF-κB Pathway as Promising Biomarkers in Colorectal Cancer

Prognosis analysis of pyroptosis- and aging-related genes in colorectal cancer based on bioinformatic analysis

Chimeric Antigen Receptor (CAR) T Cell Immunotherapy for Solid Tumors

Contact Info

Product

Resources

About