Pseudogenes in gastric cancer pathogenesis: a review article

SNAI2/FTH1P3/miR-218-5p Positive Feedback Loop Promotes Colorectal Cancer Metastasis

Colorectal cancer (CRC) is a type of intestinal cancer that causes more than 600,000 deaths every year. Overcoming the problems of metastasis requires detailed studies to reveal the potential molecular mechanisms. This study aims to reveal the molecular mechanism of CRC metastasis involving non-coding RNA regulation. The expression profile of FTH1P3 was analyzed based on the data of TCGA-COAD patient cohorts. Q-PCR analysis was performed to validate the expression of FTH1P3 in colorectal cancer cells. JASPR was used to screen transcription factors of FTH1P3. q-ChIP analysis was used to validate the target between FTH1P3 and transcription factor. Scratch assay and transwell assay were used to evaluate the migration and invasion ability of colorectal cancer cells. FTH1P3 is highly expressed in CRC patient cohort. FTH1P3 induced migration and invasion of SW480 cell through regulating epithelial-mesenchymal transition (EMT). In addition, FTH1P3 is a direct target of SNAI2. SNAI2 promotes the expression of FTH1P3. Both FTH1P3 and SNAI2 were directly targeted and repressed by miR-218-5p. Interestingly, ectopic FTH1P3 caused a decreased miR-218-5p level and an elevated nucleic SNAI2 protein expression level. Of note, only ectopic SNAI2 protein resulted in a repressed miR-218-5p and an increased FTH1P3, whereas SNAI2 3'UTR failed to affect the expression of miR-218-5p and FTH1P3. SNAI2 transcriptionally activates FTH1P3 expression. Both SNAI2 and FTH1P3 are targets of miR-218-5p. SNAI2/FTH1P3/miR-218-5p form a positive feedback loop in the regulation of CRC metastasis.

Identification of potential pseudogenes for predicting the prognosis of hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) remains a highly deadly malignant tumor with high recurrence and metastasis rates. Cancer stem cells (CSCs) are involved in tumor metastasis, recurrence, and resistance to drugs, which have attracted widespread attention in recent years. Research has shown that pseudogenes may regulate stemness to promote the progression of HCC, but its specific mechanisms and impact on prognosis remain unclear.

METHODS

In this study, clinical prognosis information of HCC was first downloaded from The Cancer Genome Atlas (TCGA) database. Then we calculated the mRNA expression-based stemness index (mRNAsi) of HCC. We also screened the differentially expressed pseudogene (DEPs) and conducted univariate Cox regression analysis to investigate their effect on the prognosis of HCC. Further, genomic mutation frequency analysis and weighted gene co-expression network analysis (WGCNA) were performed to compare the role of pseudogenes and stemness in promoting the progression of HCC. Finally, we conducted the correlation analysis to examine the potential mechanism of pseudogenes regulating stemness to promote the progression of HCC and detected the possible pathways through the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.

RESULTS

Herein, we revealed that the high stemness of HCC correlated with an unfavorable prognosis. We obtained 31 up-regulated and 8 down-regulated DEPs in HCC and screened CTB-63M22.1, a poor prognostic indicator of HCC. In addition, CTB-63M22.1 had a mutation frequency similar to mRNAsi and acted in a module similar to that of mRNAsi on HCC. We then screened two RNA-binding proteins (RBPs) LIN28B and NOP56 with the highest correlation with stemness. We also discovered that they were primarily enriched in the biological process as examples of mitotic nuclear division and cell cycle.

CONCLUSIONS

Collectively, these results revealed that pseudogenes CTB-63M22.1 may regulate cancer stemness by regulating RBPs, suggesting that CTB-63M22.1 may serve as an innovative therapeutic target and a reliable prognostic marker for HCC.

Pseudogenes in Cardiovascular Disease

Cardiovascular disease is the main disease that affects human life span. In recent years, the disease has been increasingly addressed at the molecular levels, for example, pseudogenes are now known to be involved in the pathogenesis and development of cardiovascular diseases. Pseudogenes are non-coding homologs of protein-coding genes and were once called “junk gene.” Since they are highly homologous to their functional parental genes, it is somewhat difficult to distinguish them. With the development of sequencing technology and bioinformatics, pseudogenes have become readily identifiable. Recent studies indicate that pseudogenes are closely related to cardiovascular diseases. This review provides an overview of pseudogenes and their roles in the pathogenesis of cardiovascular diseases. This new knowledge adds to our understanding of cardiovascular disease at the molecular level and will help develop new biomarkers and therapeutic approaches designed to prevent and treat the disease.

Circular RNAs emerge as important regulators with great potential for clinical application in gastric cancer

Gastric cancer (GC) is a common digestive malignancy with a high-ranking morbidity and mortality. Therefore, it is urgent to identify novel indicators and develop new strategies for clinical diagnosis and treatment of GC. As a type of noncoding RNA, circular RNAs (circRNAs) have received increased attention in GC during recent years. To more comprehensively understand current research progress on circRNAs in GC, in this review, we introduce basic knowledge of circRNAs, summarize abnormally expressed circRNAs and discuss their functions and regulatory molecular mechanisms in GC. Then, we review potential applications of circRNAs for GC diagnosis, prognosis and treatment. Finally, we conclude by highlighting major advancements of circRNAs in GC research, and we discuss existing challenges and possible future research directions of GC-associated circRNAs.

Re-recognition of pseudogenes: From molecular to clinical applications

Pseudogenes were initially regarded as "nonfunctional" genomic elements that did not have protein-coding abilities due to several endogenous inactivating mutations. Although pseudogenes are widely expressed in prokaryotes and eukaryotes, for decades, they have been largely ignored and classified as gene "junk" or "relics". With the widespread availability of high-throughput sequencing analysis, especially omics technologies, knowledge concerning pseudogenes has substantially increased. Pseudogenes are evolutionarily conserved and derive primarily from a mutation or retrotransposon, conferring the pseudogene with a "gene repository" role to store and expand genetic information. In contrast to previous notions, pseudogenes have a variety of functions at the DNA, RNA and protein levels for broadly participating in gene regulation to influence the development and progression of certain diseases, especially cancer. Indeed, some pseudogenes have been proven to encode proteins, strongly contradicting their "trash" identification, and have been confirmed to have tissue-specific and disease subtype-specific expression, indicating their own value in disease diagnosis. Moreover, pseudogenes have been correlated with the life expectancy of patients and exhibit great potential for future use in disease treatment, suggesting that they are promising biomarkers and therapeutic targets for clinical applications. In this review, we summarize the natural properties, functions, disease involvement and clinical value of pseudogenes. Although our knowledge of pseudogenes remains nascent, this field deserves more attention and deeper exploration.

Neuroevolution as a tool for microarray gene expression pattern identification in cancer research

Microarrays are still one of the major techniques employed to study cancer biology. However, the identification of expression patterns from microarray datasets is still a significant challenge to overcome. In this work, a new approach using Neuroevolution, a machine learning field that combines neural networks and evolutionary computation, provides aid in this challenge by simultaneously classifying microarray data and selecting the subset of more relevant genes. The main algorithm, FS-NEAT, was adapted by the addition of new structural operators designed for this high dimensional data. In addition, a rigorous filtering and preprocessing protocol was employed to select quality microarray datasets for the proposed method, selecting 13 datasets from three different cancer types. The results show that Neuroevolution was able to successfully classify microarray samples when compared with other methods in the literature, while also finding subsets of genes that can be generalized for other algorithms and carry relevant biological information. This approach detected 177 genes, and 82 were validated as already being associated to their respective cancer types and 44 were associated to other types of cancer, becoming potential targets to be explored as cancer biomarkers. Five long non-coding RNAs were also detected, from which four don't have described functions yet. The expression patterns found are intrinsically related to extracellular matrix, exosomes and cell proliferation. The results obtained in this work could aid in unraveling the molecular mechanisms underlying the tumoral process and describe new potential targets to be explored in future works.

Sheep skeletal muscle transcriptome analysis reveals muscle growth regulatory lncRNAs

As widely distributed domestic animals, sheep are an important species and the source of mutton. In this study, we aimed to evaluate the regulatory lncRNAs associated with muscle growth and development between high production mutton sheep (Dorper sheep and Qianhua Mutton Merino sheep) and low production mutton sheep (Small-tailed Han sheep). In total, 39 lncRNAs were found to be differentially expressed. Using co-expression analysis and functional annotation, 1,206 co-expression interactions were found between 32 lncRNAs and 369 genes, and 29 of these lncRNAs were found to be associated with muscle development, metabolism, cell proliferation and apoptosis. lncRNA–mRNA interactions revealed 6 lncRNAs as hub lncRNAs. Moreover, three lncRNAs and their associated co-expressed genes were demonstrated by cis-regulatory gene analyses, and we also found a potential regulatory relationship between the pseudogene lncRNA LOC101121401 and its parent gene FTH1. This study provides a genome-wide resolution of lncRNA and mRNA regulation in muscles from mutton sheep.