MicroRNA profiles in five pairs of early gastric cancer tissues and adjacent non-cancerous tissues

From sequencing to validation: NGS-based exploration of plasma miRNA in papillary thyroid carcinoma

Objective

A non-invasive method using plasma microRNAs provides new insights into thyroid cancer diagnosis. The objective of this study was to discover potential circulating biomarkers of papillary thyroid carcinoma (PTC) through the analysis of plasma miRNAs using next-generation sequencing (NGS).

Methods

Plasma miRNAs were isolated from peripheral blood samples collected from healthy individuals, patients diagnosed with PTC, and those with benign thyroid nodules. The Illumina NovaSeq 6000 platform was employed to establish the miRNA expression profiles. Candidate miRNAs for diagnostic purposes were identified utilizing the Random Forest (RF) algorithm. The selected miRNAs were subsequently validated in an independent validation set using RT-qPCR.

Results

NGS results revealed consistent plasma miRNA expression patterns among healthy individuals and patients with benign thyroid nodules in the discovery set (6 healthy cases, 17 benign cases), while differing significantly from those observed in the PTC group (17 PTC cases). Seven miRNAs exhibiting significant expression differences were identified and utilized to construct an RF classifier. Receiver operating characteristic (ROC) analysis for PTC diagnosis, and the area under the curve (AUC) was 0.978. Subsequent KEGG and GO analyses of the target genes associated with these 7 miRNAs highlighted pathways relevant to tumors and the cell cycle. Independent validation through RT-qPCR in a separate cohort (15 CONTROL, 15 PTC groups) underscored hsa-miR-301a-3p and hsa-miR-195-5p as promising candidates for PTC diagnosis.

Conclusion

In conclusion, our study established a seven-miRNA panel in plasma by Random Forest algorithm with significant performance in discriminating PTC from healthy or benign group. hsa-miR-301a-3p, hsa-miR-195-5p in plasma have potential for further study in the diagnosis of PTC in Asian ethnic.

Network analysis of H. pylori effect on AGS human gastric adenocarcinoma cells gene expression profile

Aim

To better understand the molecular mechanism of Helicobacter pylori (H. pylori) in adenocarcinoma, the gene expression profile of AGS cells was analyzed by complementary study.

Background

Gastric cancer, as one of the most lethal malignancies in the world, is important to be studied in terms of biomarkers. On the other hand, Helicobacter pylori is one of the key risk factors in this type of disease.

Methods

In this cross-sectional study, we evaluated the seroprevalence of total and IgM anti-HAV antibodies of 254 institutionalized people with intellectual disabilities. Total and IgM anti-HAV antibodies of the blood samples of these people were determined by ELISA method. Protein-protein interaction (PPI) network analysis is a bioinformatic study with validation values for biomarker identification and clarification of molecular mechanisms. Cytoscape V 3.10.2 and its application identified potential central elements of the PPI network and its corresponding roles.

Results

GAPDH and P53 are the most promising candidates in this study. In addition, the microRNA signatures assessment provided more information about these biomarkers and added more value.

Conclusion

Consequently, a new outlook for the relationship between gastric cancer and H. pylori was explored based on the new key biomarkers.

Identification of differentially expressed genes associated with the pathogenesis of gastric cancer by bioinformatics analysis

AIM

Gastric cancer (GC) is one of the most diagnosed cancers worldwide. GC is a heterogeneous disease whose pathogenesis has not been entirely understood. Besides, the GC prognosis for patients remains poor. Hence, finding reliable biomarkers and therapeutic targets for GC patients is urgently needed.

METHODS

GSE54129 and GSE26942 datasets were downloaded from Gene Expression Omnibus (GEO) database to detect differentially expressed genes (DEGs). Then, gene set enrichment analyses and protein-protein interactions were investigated. Afterward, ten hub genes were identified from the constructed network of DEGs. Then, the expression of hub genes in GC was validated. Performing survival analysis, the prognostic value of each hub gene in GC samples was investigated. Finally, the databases were used to predict microRNAs that could regulate the hub genes. Eventually, top miRNAs with more interactions with the list of hub genes were introduced.

RESULTS

In total, 203 overlapping DEGs were identified between both datasets. The main enriched KEGG pathway was "Protein digestion and absorption." The most significant identified GO terms included "primary alcohol metabolic process," "basal part of cell," and "extracellular matrix structural constituent conferring tensile strength." Identified hub modules were COL1A1, COL1A2, TIMP1, SPP1, COL5A2, THBS2, COL4A1, MUC6, CXCL8, and BGN. The overexpression of seven hub genes was associated with overall survival. Moreover, among the list of selected miRNAs, hsa-miR-27a-3, hsa-miR-941, hsa-miR-129-2-3p, and hsa-miR-1-3p, were introduced as top miRNAs targeting more than five hub genes.

CONCLUSIONS

The present study identified ten genes associated with GC, which may help discover novel prognostic and diagnostic biomarkers as well as therapeutic targets for GC. Our results may advance the understanding of GC occurrence and progression.

Association of serum and fecal microRNA profiles in cats with gastrointestinal cancer and chronic inflammatory enteropathy

BACKGROUND

Differentiation of gastrointestinal cancer (GIC) from chronic inflammatory enteropathies (CIE) in cats can be challenging and often requires extensive diagnostic testing. MicroRNAs (miRNAs) have promise as non-invasive biomarkers in serum and feces for diagnosis of GIC.

HYPOTHESIS/OBJECTIVES

Cats with GIC will have serum and fecal miRNA profiles that differ significantly from healthy cats and cats with CIE. Identify serum and fecal miRNAs with diagnostic potential for differentiation between cats with GIC and CIE as compared to healthy cats.

ANIMALS

Ten healthy cats, 9 cats with CIE, and 10 cats with GIC; all client-owned.

METHODS

Cats were recruited for an international multicenter observational prospective case-control study. Serum and feces were screened using small RNA sequencing for miRNAs that differed in abundance between cats with GIC and CIE, and healthy cats. Diagnostic biomarker potential of relevant miRNAs from small RNA sequencing and the literature was confirmed using reverse transcription quantitative real-time PCR (RT-qPCR).

RESULTS

Serum miR-223-3p was found to distinguish between cats with GIC and CIE with an area under the curve (AUC) of 0.9 (95% confidence interval [CI], 0.760-1.0), sensitivity of 90% (95% CI, 59.6-99.5%), and specificity of 77.8% (95% CI, 45.3-96.1%). Serum miR-223-3p likewise showed promise in differentiating a subgroup of cats with small cell lymphoma (SCL) from those with CIE. No fecal miRNAs could distinguish between cats with GIC and CIE.

CONCLUSION AND CLINICAL IMPORTANCE

Serum miR-223-3p potentially may serve as a noninvasive diagnostic biomarker of GIC in cats, in addition to providing a much needed tool for the differentiation of CIE and SCL.

m6A-related lncRNAs predict prognosis and indicate cell cycle in gastric cancer

Background: N6-methyladenosine (m6A) modification is a common epigenetic methylation modification of RNA, which plays an important role in gastric carcinogenesis and progression by regulating long non-coding RNA (lncRNA). This study is aimed to investigate the potential prognostic signatures of m6A -related lncRNAs in STAD. Methods: The m6A-related lncRNAs with the most significant impact on gastric cancer prognosis in the TCGA database were identified by bioinformatics and machine learning methods. The m6A-related lncRNA prognostic model (m6A-LPS) and nomogram was constructed by Cox regression analysis with the minimum absolute contraction and selection operator (LASSO) algorithm. The functional enrichment analysis of m6A-related lncRNAs was also investigated. The miRTarBase, miRDB and TargetScan databases were utilized to establish a prognosis-related network of competing endogenous RNA (ceRNA) by bioinformatics methods. The correlation of AL391152.1 expressions and cell cycle were experimentally testified by qRT-PCR and flow cytometry. Results: In total, 697 lncRNAs that were identified as m6A-related lncRNAs in GC samples. The survival analysis showed that 18 lncRNAs demonstrated prognostic values. A risk model with 11 lncRNAs was established by Lasso Cox regression, and can predict the prognosis of GC patients. Cox regression analysis and ROC curve indicated that this lncRNA prediction model was an independent risk factor for survival rates. Functional enrichment analysis and ceRNA network revealed that the nomogram was notably associated with cell cycle. qRT-PCR and flow cytometry revealed that downregulation of GC m6A-related lncRNA AL391152.1 could decrease cyclins expression in SGC7901 cells. Conclusion: A m6A-related lncRNAs prognostic model was established in this study, which can be applied to predict prognosis and cell cycle in gastric cancer.

Overview of MicroRNAs as Diagnostic and Prognostic Biomarkers for High-Incidence Cancers in 2021

MicroRNAs (miRNAs) are small non-coding RNAs (ncRNAs) about 22 nucleotides in size, which play an important role in gene regulation and are involved in almost all major cellular physiological processes. In recent years, the abnormal expression of miRNAs has been shown to be associated with human diseases including cancer. In the past ten years, the link between miRNAs and various cancers has been extensively studied, and the abnormal expression of miRNAs has been reported in various malignant tumors, such as lung cancer, gastric cancer, colorectal cancer, liver cancer, breast cancer, and prostate cancer. Due to the high malignancy grade of these cancers, it is more necessary to develop the related diagnostic and prognostic methods. According to the study of miRNAs, many potential cancer biomarkers have been proposed for the diagnosis and prognosis of diseases, especially cancer, thus providing a new theoretical basis and perspective for cancer screening. The use of miRNAs as biomarkers for diagnosis or prognosis of cancer has the advantages of being less invasive to patients, with better accuracy and lower price. In view of the important clinical significance of miRNAs in human cancer research, this article reviewed the research status of miRNAs in the above-mentioned cancers in 2021, especially in terms of diagnosis and prognosis, and provided some new perspectives and theoretical basis for the diagnosis and treatment of cancers.

Association of fecal and serum microRNA profiles with gastrointestinal cancer and chronic inflammatory enteropathy in dogs

BACKGROUND

Reliable biomarkers to differentiate gastrointestinal cancer (GIC) from chronic inflammatory enteropathy (CIE) in dogs are needed. Fecal and serum microRNAs (miRNAs) have been proposed as diagnostic and prognostic markers of GI disease in humans and dogs.

HYPOTHESIS/OBJECTIVES

Dogs with GIC have fecal and serum miRNA profiles that differ from those of dogs with CIE.

AIMS

(a) identify miRNAs that differentiate GIC from CIE, (b) use high-throughput reverse transcription quantitative real-time PCR (RT-qPCR) to establish fecal and serum miRNA panels to distinguish GIC from CIE in dogs.

ANIMALS

Twenty-four dogs with GIC, 10 dogs with CIE, and 10 healthy dogs, all client-owned.

METHODS

An international multicenter observational prospective case-control study. Small RNA sequencing was used to identify fecal and serum miRNAs, and RT-qPCR was used to establish fecal and serum miRNA panels with the potential to distinguish GIC from CIE.

RESULTS

The best diagnostic performance for distinguishing GIC from CIE was fecal miR-451 (AUC: 0.955, sensitivity: 86.4%, specificity: 100%), miR-223 (AUC: 0.918, sensitivity: 90.9%, specificity: 80%), and miR-27a (AUC: 0.868, sensitivity: 81.8%, specificity: 90%) and serum miR-20b (AUC: 0.905, sensitivity: 90.5%, specificity: 90%), miR-148a-3p (AUC: 0.924, sensitivity: 85.7%, specificity: 90%), and miR-652 (AUC: 0.943, sensitivity: 90.5%, specificity: 90%). Slightly improved diagnostic performance was achieved when combining fecal miR-451 and miR-223 (AUC: 0.973, sensitivity: 95.5%, specificity: 90%).

CONCLUSIONS AND CLINICAL IMPORTANCE

When used as part of a diagnostic RT-qPCR panel, the abovementioned miRNAs have the potential to function as noninvasive biomarkers for the differentiation of GIC and CIE in dogs.

An immune-related microRNA signature prognostic model for pancreatic carcinoma and association with immune microenvironment

To establish a prognostic model based on immune-related microRNA (miRNA) for pancreatic carcinoma. Weighted correlation network analysis (WGCNA) was performed using the "WGCNA" package to find the key module genes involved in pancreatic carcinoma. Spearman correlation analysis was conducted to screen immune-related miRNAs. Uni- and multi-variate COX regression analyses were carried out to identify miRNAs prognostic for overall survival (OS) of pancreatic carcinoma, which were then combined to generate a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic (ROC) analysis, distribution plot of survival status in patients and regression analysis were collectively performed to study the accuracy of the model in prognosis. Target genes of the miRNAs in the model were intersected with the key module genes, and a miRNA–mRNA network was generated and visualized by Cytoscape3.8.0. TIMER analysis was conducted to study the abundance of immune infiltrates in tumor microenvironment of pancreatic carcinoma. Expression levels of immune checkpoint genes in subgroups stratified by the model were compared by Wilcoxon test. Gene Set Enrichment Analysis (GSEA) was performed to analyze the enriched signaling pathways between subgroups. Differential analysis revealed 1826 genes differentially up-regulated in pancreatic carcinoma and 1276 genes differentially down-regulated. A total of 700 immune-related miRNAs were obtained, of which 7 miRNAs were significantly associated with OS of patients and used to establish a prognostic model with accurate predictive performance. There were 99 mRNAs overlapped from the 318 target genes of the 7 miRNAs and the key modules genes analyzed by WGCNA. Patient samples were categorized as high or low risk according to the prognostic model, which were significantly associated with dendritic cell infiltration and expression of immune checkpoint genes (TNFSF9, TNFRSF9, KIR3DL1, HAVCR2, CD276 and CD80). GSEA showed remarkably enriched signaling pathways in the two subgroups. This study identified an immune-related 7-miRNA based prognostic model for pancreatic carcinoma, which could be used as a reliable tool for prognosis.

TTF1 suppresses neuroblastoma growth and induces neuroblastoma differentiation by targeting TrkA and the miR-204/TrkB axis

Neuroblastoma (NB) is the most common extracranial malignant solid tumor in children. We found that TTF1, TrkA, and miR-204 were lowly expressed, whereas TrkB was highly expressed in undifferentiated NB tissues. Meanwhile, TTF1 expression correlated positively with TrkA and miR-204 expression but negatively with TrkB expression. The TTF1 promoter was hypermethylated in undifferentiated NB tissues and SK-N-BE cells, leading to TTF1 downregulation. We also identified miR-204, which directly targets TrkB, as a transcriptional target of TTF1. Functionally, TTF1 suppressed proliferation, migration, and invasion of NB cells, whereas induced cell cycle arrest, apoptosis, and autophagy of NB cells by regulating TrkA and the miR-204-TrkB axis. Furthermore, TTF1 suppressed tumor growth and promoted neurogenic differentiation in a NB xenograft mouse model. Our study demonstrates that TTF1 reduces tumor growth and induces neurogenic differentiation in NB by directly targeting TrkA and the miR-204/TrkB axis.

•

TTF1, TrkA, and miR-204 were lowly expressed in undifferentiated NB tissues

•

TTF1 promoter was hypermethylated in undifferentiated NB tissues and cells

•

TTF1 suppressed proliferation of NB cells by regulating TrkA and the miR-204-TrkB axis

•

TTF1 suppressed tumor growth and promoted neurogenic differentiation in vivo

Hsa_circRNA_0040462: a sensor of cells' response to CAP treatment with double-edged roles on breast cancer malignancy

Cold atmospheric plasma (CAP) represents a novel onco-therapeutic approach that has demonstrated its efficacy in many types of tumors. The efficacy of CAP is dose-dependent that determines the panel of tumors feasible for receiving CAP treatment under a certain parameter configuration. Identifying markers for easy and fast prognosis of tumors' sensitivity in response to CAP exposure is of critical value towards optimized therapeutic outcome, the lack of which has largely limited the translation of CAP into clinics. Circular RNAs represent a novel type of biomarkers for disease diagnosis that is featured by easy detection and stability. Through whole transcriptome sequencing, followed by in vitro validations, computational predictions and preliminary functional studies, we identified hsa_circRNA_0040462 as a sensor of breast cancer cells' response to CAP treatment. Yet we warrant the use of hsa_circRNA_0040462 as an onco-therapeutic target given its double-edged roles on breast cancer progression, i.e., suppressive on the growth and promotive on the migrative ability of triple negative breast cancer cells. Our study for the first time focused on markers prognostic of CAP's efficacy and tumors' sensitivity to CAP treatment under a certain parameter configuration, and reported hsa_circRNA_0040462 as a sensor of cells' response to CAP treatment. Also, the uncovered dual roles of hsa_circRNA_0040462 further advanced our knowledge on the complex yet critical regulatory functionalities of circular RNAs in cancer progression.