Comprehensive Analysis of Survival-Related lncRNAs, miRNAs, and mRNAs Forming a Competing Endogenous RNA Network in Gastric Cancer

Analysis of PANoptosis-Related LncRNA-miRNA-mRNA Network Reveals LncRNA SNHG7 Involved in Chemo-Resistance in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most common malignancies, and its metastatic lesions are the leading cause of death in COAD patients. PANoptosis is a recently identified pathway for programmed cell death implicated in developing COAD. Long non-coding RNAs (lncRNAs) are key regulators of cancer occurrence and progress. Although their function has captured much attention in COAD, the relationship between COAD metastasis-associated lncRNA expression and PANoptosis remains elusive. Therefore, this study aimed to explore the potential regulatory roles of metastasis- and PANoptosis-associated lncRNAs in COAD. Nine lncRNAs associated with metastasis and PANoptosis in COAD were identified from The Cancer Genome Atlas (TCGA) and GEO databases. Their functions were analyzed by multiple bioinformatics methods, and the lncRNA-miRNA-mRNA network was constructed. Multivariate Cox analysis identified one lncRNA (SNHG7) significantly related to COAD prognosis. Subsequent analyses showed its expression correlated with tumor stage and lymph node metastasis. Moreover, drug sensitivity analysis and in vitro experiments suggest that lncRNA SNHG7 contributes to drug resistance in COAD. In summary, lncRNA SNHG7 is a potential target for diagnosing and treating COAD and plays a crucial role in regulating apoptosis, metastasis, and drug resistance in COAD.

N6-Methyladenosine Methylation Regulator RBM15 is a Potential Prognostic Biomarker and Promotes Cell Proliferation in Pancreatic Adenocarcinoma

RNA binding motif protein 15 (RBM15) is a key regulatory factor involved in N6-methyladenosine (m6A) methylation. It has been reported that RBM15 plays an important role in the progress of laryngeal squamous cell carcinoma (LSCC), promoting LSCC migration and invasion. However, the role of RBM15 in human different cancers remains unknown. This study aims to analyze the prognostic value of RBM15, and to demonstrate the correlation between RBM15 expression and tumor immunity, as well as to provide clues for further mechanism research. The results showed that RBM15 was mutated or copy number varied in 25 types of cancer. RBM15 mRNA was abnormally up-regulated across various cancers. Survival analysis suggested high expression of RBM15 was associated with poor prognosis in many cancer types. Among these, it affected patients’ overall survival (OS) in 10 cancer types, disease-free interval (DFI) in 8 cancer types, progression-free interval (PFI) in 12 cancer types and disease-specific survival (DSS) in 7 cancer types. Importantly, in pancreatic adenocarcinoma (PAAD), overexpression of RBM15 is associated with patients’ OS, DFI, PFI, or DSS. In addition, RBM15 expression was positively correlated with immune infiltrating cells in kidney renal clear cell carcinoma (KIRC), brain lower grade glioma (LGG), and PAAD. Moreover, RBM15 expression showed a strong correlation with immune checkpoint markers in PAAD. Cell counting kit-8 (CCK-8) assay showed that knockdown of RBM15 significantly inhibited the proliferation of pancreatic cancer cells. PPI analysis showed USP10, USP24, SMG1, NRAS were closely connected with RBM15 alterations. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that many biological processes (BP), cellular components (CC), molecular functions (MF), cancer related pathways including “sister chromatid cohesion”, “peptidyl-serine phosphorylation”, “cell division”, “nucleoplasm”, “nucleus”, “protein binding”, “protein serine/threonine kinase activity”, “T cell receptor signaling pathway”, “Cell cycle” were regulated by RBM15 alterations. Taken together, pan-cancer analysis of RBM15 suggested it may be served as a prognostic biomarker and immunotherapeutic target for PAAD.

Profiling and Integrated Analysis of Differentially Expressed MicroRNAs as Novel Biomarkers of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a heterogeneous disease that has multiple etiologies. It is the most common primary liver cancer, the sixth highest cause of cancer incidences, and the fourth highest cause of cancer-related deaths. The discovery of new biomarkers for the early detection, treatment, and prognosis of HCC would therefore be extremely useful. This study investigated differentially expressed ribonucleic acid (RNA) profiles by constructing a genome-wide profile of clinical samples. Differential expression analysis identified 1,280 differentially expressed messenger RNAs (dif-mRNAs), 99 differentially expressed microRNAs (dif-miRNAs), 181 differentially expressed long non-coding RNAs (dif-lncRNAs), and 31 differentially expressed circular RNAs (dif-circRNAs). Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) path analysis were then conducted on these differentially expressed RNAs, revealing that they were clearly related to cell division, foreign body metabolism, and ribosome assembly. A competing endogenous RNA (ceRNA) network was then constructed based on the regulatory dif-miRNA-dif-mRNA and dif-miRNA-dif-lncRNA relationships. These results were also verified using HCC data from the Cancer Genome Atlas (TCGA); seven dif-miRNAs were verified in clinical samples by real-time quantitative polymerase chain reaction (RT-qPCR). Kaplan-Meier survival analysis revealed that the expression levels of Hsa-miR-1269a, Hsa-miR-421, and Hsa-miR-190b were correlated with overall survival. (P <0.05). Survival analysis of clinical samples showed that hsa-mir-1269a, hsa-mir-421 were associated with prognosis (p<0.05).This study revealed the general expression characteristics of specific differentially expressed miRNAs using a ceRNA network constructed from HCC samples. Hsa-mir-1269a, hsa-mir-421 may be promising candidates.

The Role of miR-4256/HOXC8 Signaling Axis in the Gastric Cancer Progression: Evidence From lncRNA-miRNA-mRNA Network Analysis

Gastric cancer is a deadly human malignancy and the molecular mechanisms underlying gastric cancer pathophysiology are very complicated. Thus, further investigations are warranted to decipher the underlying molecular mechanisms. With the development of high-throughput screening and bioinformatics, gene expression profiles with large scale have been performed in gastric cancer. In the present study, we mined The Cancer Genome Atlas (TCGA) database and analyzed the gene expression profiles between gastric cancer tissues and normal gastric tissues. A series of differentially expressed lncRNAs, miRNAs and mRNAs between gastric cancer tissues and normal gastric tissues were identified. Based on the differentially expressed genes, we constructed miRNA-mRNA network, lncRNA-mRNA network and transcriptional factors-mRNA-miRNA-lncRNA network. Furthermore, the Kaplan survival analysis showed that high expression levels of EVX1, GBX2, GCM1, HOXC8, HOXC9, HOXC10, HOXC11, HOXC12 and HOXC13 were all significantly correlated with shorter overall survival of the patients with gastric cancer. On the other hand, low expression level of HOXA13 was associated with shorter overall survival of patients with gastric cancer. Among these hub genes, we performed the in vitro functional studies of HOXC8 in the gastric cancer cells. Knockdown of HOXC8 and overexpression of miR-4256 both significantly repressed the gastric cancer cell proliferation and migration, and miR-4256 repressed the expression of HOXC8 via targeting its 3' untranslated region in gastric cancer cells. Collectively, our results revealed that a complex interaction networks of differentially expressed genes in gastric cancer, and further functional studies indicated that miR-4256/HOXC8 may be an important axis in regulating gastric cancer progression.

The Potential Regulatory Mechanism of lncRNA 122K13.12 and lncRNA 326C3.7 in Ankylosing Spondylitis

This work aims to analyze and construct a novel competing endogenous RNA (ceRNA) network in ankylosing spondylitis (AS) with bone bridge formation, lncRNA. Using RNA sequencing and bioinformatics, we analyzed expression profiles of long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in whole blood cells from 5 AS patients and 3 healthy individuals. Next, we verified the expression levels of candidate lncRNAs in 97 samples using the ΔΔCt value of real-time quantitative polymerase chain reaction (qRT-PCR). We used multivariate logistic regression analysis to screen lncRNAs and clinical indicators for use in the prediction model. Both SPSS 24.0 and R software were used for data analysis and prediction model construction. The results showed that compared with the normal controls, 205 long noncoding RNAs (lncRNAs), 961 microRNAs (miRNAs), and 200 mRNAs (DEmRNAs) were differentially expressed in the AS patients. We identified lncRNA 122K13.12 and lncRNA 326C3.7 among 205 lncRNAs differentially expressed between AS patients and healthy humans. Then, we noted that 30 miRNAs and five mRNAs formed a ceRNA network together with these two lncRNAs. These ceRNA networks might regulate the tumor necrosis factor (TNF) signaling pathway in AS development. In addition, the expression level of lncRNA 122K13.12 and lncRNA 326C3.7 correlated with various structural damage indicators in AS. Specifically, the lncRNA 326C3.7 expression level was an independent risk factor in bone bridge formation [area under the ROC curve (AUC) = 0.739 (0.609–0.870) and p = 0.003], and the best Youden Index was 0.405 (sensitivity = 0.800 and specificity = 0.605). Moreover, we constructed a lncRNA-based nomogram that could effectively predict bone bridge formation [AUC = 0.870 (0.780–0.959) and p < 0.001, and the best Youden Index was 0.637 (sensitivity = 0.900 and specificity = 0.737)]. In conclusion, we uncovered a unique ceRNA signaling network in AS with bone bridge formation and identified novel biomarkers and prediction models with the potential for clinical applications.

Pan-Cancer Analysis Reveals FH as a Potential Prognostic and Immunological Biomarker in Lung Adenocarcinoma

Fumarate hydratase (FH) is an important enzymatic component in the tricarboxylic acid cycle. Studies have reported that FH plays an important role in hereditary leiomyomatosis and renal cell cancer (HLRCC). However, the role of FH in human different cancers remains unknown. This study is aimed at analyzing the prognostic value of FH and demonstrating the correlation between FH expression and tumor immunity. Results showed that FH was mutated or copy number varied in 27 types of cancer. FH mRNA was abnormally upregulated across various cancers. Survival analysis suggested high expression of FH was associated with poor prognosis in many cancer types, including lung adenocarcinoma (LUAD). Additionally, FH expression was associated with immune infiltration, including B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells, especially in liver hepatocellular carcinoma (LIHC), LUAD, and lung squamous cell carcinoma (LUSC). Moreover, FH expression showed a strong correlation with immune checkpoint markers in LUAD and testicular germ cell tumors (TGCT). These results indicate that FH is an immunotherapeutic target and a potential prognostic biomarker in LUAD.

The Molecular Mechanism of Multiple Organ Dysfunction and Targeted Intervention of COVID-19 Based on Time-Order Transcriptomic Analysis

Coronavirus disease 2019 (COVID-19) pandemic is caused by the novel coronavirus that has spread rapidly around the world, leading to high mortality because of multiple organ dysfunction; however, its underlying molecular mechanism is unknown. To determine the molecular mechanism of multiple organ dysfunction, a bioinformatics analysis method based on a time-order gene co-expression network (TO-GCN) was performed. First, gene expression profiles were downloaded from the gene expression omnibus database (GSE161200), and a TO-GCN was constructed using the breadth-first search (BFS) algorithm to infer the pattern of changes in the different organs over time. Second, Gene Ontology enrichment analysis was used to analyze the main biological processes related to COVID-19. The initial gene modules for the immune response of different organs were defined as the research object. The STRING database was used to construct a protein-protein interaction network of immune genes in different organs. The PageRank algorithm was used to identify five hub genes in each organ. Finally, the Comparative Toxicogenomics Database played an important role in exploring the potential compounds that target the hub genes. The results showed that there were two types of biological processes: the body's stress response and cell-mediated immune response involving the lung, trachea, and olfactory bulb (olf) after being infected by COVID-19. However, a unique biological process related to the stress response is the regulation of neuronal signals in the brain. The stress response was heterogeneous among different organs. In the lung, the regulation of DNA morphology, angiogenesis, and mitochondrial-related energy metabolism are specific biological processes related to the stress response. In particular, an effect on tracheal stress response was made by the regulation of protein metabolism and rRNA metabolism-related biological processes, as biological processes. In the olf, the distinctive stress responses consist of neural signal transmission and brain behavior. In addition, myeloid leukocyte activation and myeloid leukocyte-mediated immunity in response to COVID-19 can lead to a cytokine storm. Immune genes such as SRC, RHOA, CD40LG, CSF1, TNFRSF1A, FCER1G, ICAM1, LAT, LCN2, PLAU, CXCL10, ICAM1, CD40, IRF7, and B2M were predicted to be the hub genes in the cytokine storm. Furthermore, we inferred that resveratrol, acetaminophen, dexamethasone, estradiol, statins, curcumin, and other compounds are potential target drugs in the treatment of COVID-19.

Identification of Key Circulating Exosomal microRNAs in Gastric Cancer

Exosomal miRNAs (EmiRs) can be used for prediction of gastric cancer (GC) development. Supposedly, both plasma and urinary microRNAs can also be potential biomarkers for screening, but the diagnostic values of EmiRs in blood and urine are not fully studied. We here collected both types of samples from GC patients and healthy individuals and conducted miRNA sequencing to identify key members of EmiRs in GC. The exosomes samples derived from blood and urine were collected from 3 healthy individuals and 7 GC patients. Differentially expressed miRNAs (DEmiRNAs) were acquired, ontology enrichment analysis and Protein-protein Interaction (PPI) enrichment analysis were performed. There were 8 DEmiRNAs in the serum and 3 DEmiRNAs in the urine. For GC patients, there were three up-regulated DEmiRNAs (hsa-miR-130b-3p, hsa-miR-151a-3p and hsa-miR-15b-3p) in the serum exosomes, and one up-regulated DEmiRNA (hsa-miR-1246) in the urinary exosomes. Using miRNA target prediction databases, we found 418 common targets of hsa-miR-15b-3p, 35 common targets of hsa-miR-151a-3p, 117 common targets of hsa-miR-130b-3p, and 357 common targets of hsa-miR-1246. Some commonly enriched ontology terms were found, including GO BP terms like cell surface receptor signaling pathway involved in cell-cell signaling, positive regulation of catabolic process, morphogenesis of an epithelium, and GO CC terms perinuclear region of cytoplasm. The PPI network show some key nodes, including TAOK1, CMTM6, SCN3A, WASF3, IGF1, CNOT7, GABRG1, PRKD1. Together, this study provided an integrative analysis of expression profile of key circulating exosomal microRNAs. Four key exosomal miRNAs (hsa-miR-130b-3p, hsa-miR-151a-3p and hsa-miR-15b-3p) and the interaction network or enrichments based on their targets (TAOK1, CMTM6, SCN3A, WASF3, IGF1, CNOT7, GABRG1, PRKD1) may provide a reference of the molecular mechanisms in the GC development.