Biological and Clinical Relevance of Long Non-Coding RNA PCAT-1 in Cancer, A Systematic Review and Meta-Analysis

Upregulation of Long Noncoding RNA PCAT1 in Iranian Patients with Colorectal Cancer and Its Performance as a Potential Diagnostic Biomarker

Background: Long noncoding RNAs (lncRNAs) as critical molecules play an essential role in the development of cancers. In colorectal cancer (CRC), various lncRNAs are related to cell proliferation, apoptosis, migration, and invasion. LncRNA prostate cancer-associated transcript 1 (PCAT-1), as an oncogenic factor, is a diagnostic biomarker that regulates cell proliferation, migration, invasion, and apoptosis. Methods: This study evaluated the relationship between PCAT-1, CRC occurrence, and pathological features of Iranian patients. The studied samples included 100 colorectal tumor tissues and 100 adjacent healthy tissues of Iranian CRC patients. RNAs were extracted from cancerous and noncancerous tissues to synthesize complementary DNA. The expression level of PCAT-1 was assessed using the real-time PCR method, and the data analysis was assessed using SPSS software. Results: In this study, expression level of PCAT-1 in tumor tissue was significantly increased in Iranian patients, and pathological studies of the patients had no significant relationship with the PCAT-1 expression profile. Conclusion: Our results suggested that the high expression of PCAT-1 resulted in the occurrence of colorectal tumor tissues in Iranian patients, which can be considered a diagnostic biomarker in CRC.

Machine learning-based classifiers to predict metastasis in colorectal cancer patients

Background

The increasing prevalence of colorectal cancer (CRC) in Iran over the past three decades has made it a key public health burden. This study aimed to predict metastasis in CRC patients using machine learning (ML) approaches in terms of demographic and clinical factors.

Methods

This study focuses on 1,127 CRC patients who underwent appropriate treatments at Taleghani Hospital, a tertiary care facility. The patients were divided into training and test datasets in an 80:20 ratio. Various ML methods, including Naive Bayes (NB), random rorest (RF), support vector machine (SVM), neural network (NN), decision tree (DT), and logistic regression (LR), were used for predicting metastasis in CRC patients. Model performance was evaluated using 5-fold cross-validation, reporting sensitivity, specificity, the area under the curve (AUC), and other indexes.

Results

Among the 1,127 patients, 183 (16%) had experienced metastasis. In the predictionof metastasis, both the NN and RF algorithms had the highest AUC, while SVM ranked third in both the original and balanced datasets. The NN and RF algorithms achieved the highest AUC (100%), sensitivity (100% and 100%, respectively), and accuracy (99.2% and 99.3%, respectively) on the balanced dataset, followed by the SVM with an AUC of 98.8%, a sensitivity of 97.5%, and an accuracy of 97%. Moreover, lower false negative rate (FNR), false positive rate (FPR), and higher negative predictive value (NPV) can be confirmed by these two methods. The results also showed that all methods exhibited good performance in the test datasets, and the balanced dataset improved the performance of most ML methods. The most important variables for predicting metastasis were the tumor stage, the number of involved lymph nodes, and the treatment type. In a separate analysis of patients with tumor stages I-III, it was identified that tumor grade, tumor size, and tumor stage are the most important features.

Conclusion

This study indicated that NN and RF were the best among ML-based approaches for predicting metastasis in CRC patients. Both the tumor stage and the number of involved lymph nodes were considered the most important features.

Relationship between long non-coding RNAs and Hippo signaling pathway in gastrointestinal cancers; molecular mechanisms and clinical significance

Long non-coding RNAs (lncRNAs) play a significant biological role in the regulation of various cellular processes such as cell proliferation, differentiation, apoptosis and migration. In various malignancies, lncRNAs interplay with some main cancer-associated signaling pathways, including the Hippo signaling pathway to regulate the various cellular processes. It has been revealed that the cross-talking between lncRNAs and Hippo signaling pathway involves in gastrointestinal (GI) cancers development and progression. Considering the clinical significance of these lncRNAs, they have also been introduced as potential biomarkers in diagnostic, prognostic and therapeutic strategies in GI cancers. Herein, we review the mechanisms of lncRNA-mediated regulation of Hippo signaling pathway and focus on the corresponding molecular mechanisms and clinical significance of these non-coding RNAs in GI cancers.

An Updated Review on Molecular Biomarkers in Diagnosis and Therapy of Colorectal Cancer

Colorectal cancer is one of the most common cancer types worldwide. Since colorectal cancer takes time to develop, its incidence and mortality can be treated effectively if it is detected in its early stages. As a result, non-invasive or invasive biomarkers play an essential role in the early diagnosis of colorectal cancer. Many experimental studies have been carried out to assess genetic, epigenetic, or protein markers in feces, serum, and tissue. It may be possible to find biomarkers that will help with the diagnosis of colorectal cancer by identifying the genes, RNAs, and/or proteins indicative of cancer growth. Recent advancements in the molecular subtypes of colorectal cancer, DNA methylation, microRNAs, long noncoding RNAs, exosomes, and their involvement in colorectal cancer have led to the discovery of novel biomarkers. In small-scale investigations, most biomarkers appear promising. However, large-scale clinical trials are required to validate their effectiveness before routine clinical implementation. Hence, this review focuses on small-scale investigations and results of big data analysis that may provide an overview of the biomarkers for the diagnosis, therapy, and prognosis of colorectal cancer.

Knockdown of lncRNA X inactive specific transcript (XIST) radiosensitizes non-small cell lung cancer (NSCLC) cells through regulation of miR-16-5p/WEE1 G2 checkpoint kinase (WEE1) axis

Long non-coding RNA (lncRNA) X inactive specific transcript (XIST) is reported to play an oncogenic role in non-small cell lung cancer (NSCLC). However, the role of XIST in regulating the radiosensitivity of NSCLC cells remains unclear. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expressions of XIST and miR-16-5p in NSCLC in tissues and cells, and Western blot was used to assess the expression of WEE1 G2 checkpoint kinase (WEE1). Cell counting kit-8 (CCK-8), colony formation and flow cytometry assays were used to determine cell viability and apoptosis after NSCLC cells were exposed to different doses of X-rays. The interaction between XIST and miR-16-5p was confirmed by StarBase database, qRT-PCR and dual-luciferase reporter gene assays. TargetScan database was used to predict WEE1 as a target of miR-16-5p, and their targeting relationship was further validated by Western blot, qRT-PCR and dual-luciferase reporter gene assays. XIST was highly expressed in both NSCLC tissue and cell lines, and knockdown of XIST repressed NSCLC cell viability and cell survival, and facilitated apoptosis under the irradiation. MiR-16-5p was a target of XIST, and rescue experiments demonstrated that miR-16-5p inhibitors could reverse the role of XIST knockdown on radiosensitivity in NSCLC cells. WEE1 was validated as a target gene of miR-16-5p, and WEE1 could be negatively regulated by XIST. XIST promotes the radioresistance of NSCLC cells by regulating the expressions of miR-16-5p and WEE1, which can be a novel target for NSCLC therapy.

Development and validation of a five-lncRNA signature with prognostic value in colon cancer

Dysregulation of long noncoding RNAs (lncRNAs) has been found in a large number of human cancers, including colon cancer. Therefore, the implementation of potential lncRNAs biomarkers with prognostic prediction value are very much essential. GSE39582 data set was downloaded from database of Gene Expression Omnibus. Re-annotation analysis of lncRNA expression profiles was performed by NetAffx annotation files. Univariate and multivariate Cox proportional analyses helped select prognostic lncRNAs. Algorithm of random survival forest-variable hunting (RSF-VH) together with stepwise multivariate Cox proportional analysis were performed to establish lncRNA signature. The log-rank test was carried out to analyze and compare the Kaplan-Meier survival curves of patients' overall survival (OS). Receiver operating characteristic (ROC) analysis was used for comparing the survival prediction regarding its specificity and sensitivity based on lncRNA risk score, followed by calculating the values of area under the curve (AUC). The single-sample GSEA (ssGSEA) analysis was used to describe biological functions associated with this signature. Finally, to determine the robustness of this model, we used the validation sets including GSE17536 and The Cancer Genome Atlas data set. After re-annotation analysis of lncRNAs, a total of 14 lncRNA probes were obtained by univariate and multivariate Cox proportional analysis. Then, the RSF-VH algorithm and stepwise multivariate Cox analysis helped to build a five-lncRNA prognostic signature for colon cancer. The patients in group with high risk showed an obviously shorter survival time compared with patients in group with low risk with AUC of 0.75. In addition, the five-lncRNA signature can be used to independently predict the survival of patients with colon cancer. The ssGSEA analysis revealed that pathways such as extracellular matrix-receptor interaction was activated with an increase in risk score. These findings determined the strong power of prognostic prediction value of this five-lncRNA signature for colon cancer.

PCAT1: An oncogenic lncRNA in diverse cancers and a putative therapeutic target

The critical roles of long non-coding RNAs (lncRNAs) in the regulation of diverse biological functions has potentiated them as cancer biomarkers. Among these transcripts is the prostate cancer associated transcript 1 (PCAT1) which has been initially shown to exert oncogenic roles in prostate cancer. Further studies revealed its similar roles in various kinds of human malignancies including both solid tumors and hematological malignancies. Animal studies have shown that down-regulation of this lncRNA can attenuate tumor growth in a wide array of cancers including prostate cancer, colorectal cancer, squamous cell carcinoma lung cancer and hepatocellular carcinoma. Studies aimed at identification of diagnostic value of this lncRNA in human cancers reported various values ranging from 0.66 to 0.89 in diverse cancers with the best value reported in multiple myeloma. This lncRNA has a number of putative functional genomic variants such as rs1902432, rs2632159, rs1026411, rs710886, rs16901904 and rs710886 which can modify expression or function of PCAT1 thus altering the risk of human cancers. Based on aberrant expression of PCAT1 in malignancies of diverse origins, this lncRNA can be regarded as a therapeutic target in a vast array of cancers. Thus, modalities for efficient reduction of its expression would be beneficial for several patients.

Biological and clinical relevance of metastasis-associated long noncoding RNAs in esophageal squamous cell carcinoma: A systematic review

Esophageal squamous cell carcinoma (ESCC) is a foremost cancer-related death worldwide owing to rapid metastasis and poor prognosis. Metastasis, as the most important reason for death, is biologically a multifaceted process involving a range of cell signaling pathways. Long noncoding RNAs (lncRNAs), as transcriptional regulators, can regulate numerous genomic processes and cellular processes such as cell proliferation, migration, and invasion. LncRNAs have also been shown to involve in/regulate the cancer metastasis-related signaling pathways. Hence, they have increasingly been brought to international attention in molecular oncology research. A number of researchers have attempted to reveal the biological and clinical relevance of lncRNAs in ESCC tumourigenesis and metastasis. The aberrant expression of these molecules in ESCC has regularly been reported to involve in various cellular processes and clinical features, including diagnosis, prognosis, and therapeutic responses. Here, we especially consider the pathways in which lncRNAs act as metastasis-mediated effectors, mainly by interacting with epithelial-mesenchymal transition-associated factors. We review the biological roles of lncRNAs through involving in ESCC metastasis as well as the clinical significance of the metastasis-related lncRNAs in cancer diagnosis and prognosis.