Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis

Protosappanin B enhances the chemosensitivity of 5-fluorouracil in colon adenocarcinoma by regulating the LINC00612/microRNA-590-3p/Golgi phosphoprotein 3 axis

BACKGROUND

5-fluorouracil (5-FU) is conventionally used in chemotherapy for colon adenocarcinomas. Acquired resistance of 5-FU remains a clinical challenge in colon cancer, and efforts to develop targeted agents to reduce resistance have not yielded success. Protosappanin B (PSB), the main component of Lignum Sappan extract, is known to exhibit anti-tumor effects. However, whether and how PSB could improve 5-FU resistance in colon cancer have not yet been established. In this study, we aimed to explore the effects and underlying mechanisms of PSB in 5-FU-induced chemoresistance in colon adenocarcinoma.

METHODS

Forty-seven paired colon cancer tissue samples from patients who received 5-FU chemotherapy were collected as clinical samples. Two 5-FU resistant colon cancer cell lines were established for in vitro experiments. Reverse transcription-quantitative PCR (RT-qPCR) was performed to determine the mRNA and microRNA (miRNA) expression levels in colon adenocarcinoma tissues and cell lines. Cell Counting Kit-8 (CCK-8) and flow cytometry assays were performed to evaluate cell proliferation and apoptosis, respectively.

RESULTS

LINC00612 was highly expressed in colon adenocarcinoma samples and 5-FU resistant colon cancer cells. LINC00612 knockdown enhances 5-FU chemosensitivity in 5-FU resistant cells. Notably, PSB treatment attenuated LINC00612 expression in 5-FU resistant colon adenocarcinoma cells. Moreover, PSB treatment reversed the increase in LINC00612-induced 5-FU resistance. Mechanistically, LINC00612 specifically bound to miR-590-3p, which promoted 5-FU resistance in colon adenocarcinoma cells and attenuated the inhibitory effect of LINC00612 on GOLPH3 expression.

CONCLUSION

PSB attenuates 5-FU chemoresistance in colon adenocarcinoma by regulating the LINC00612/miRNA-590-3p/GOLPH3 axis.

lncRNA Biomarkers of Glioblastoma Multiforme

Long noncoding RNAs (lncRNAs) are RNA molecules of 200 nucleotides or more in length that are not translated into proteins. Their expression is tissue-specific, with the vast majority involved in the regulation of cellular processes and functions. Many human diseases, including cancer, have been shown to be associated with deregulated lncRNAs, rendering them potential therapeutic targets and biomarkers for differential diagnosis. The expression of lncRNAs in the nervous system varies in different cell types, implicated in mechanisms of neurons and glia, with effects on the development and functioning of the brain. Reports have also shown a link between changes in lncRNA molecules and the etiopathogenesis of brain neoplasia, including glioblastoma multiforme (GBM). GBM is an aggressive variant of brain cancer with an unfavourable prognosis and a median survival of 14-16 months. It is considered a brain-specific disease with the highly invasive malignant cells spreading throughout the neural tissue, impeding the complete resection, and leading to post-surgery recurrences, which are the prime cause of mortality. The early diagnosis of GBM could improve the treatment and extend survival, with the lncRNA profiling of biological fluids promising the detection of neoplastic changes at their initial stages and more effective therapeutic interventions. This review presents a systematic overview of GBM-associated deregulation of lncRNAs with a focus on lncRNA fingerprints in patients' blood.

Long Non-coding RNA Prader Willi/Angelman Region RNA 6 Suppresses Glioma Development by Modulating MicroRNA-106a-5p

As one of the most frequent intracranial tumors, glioma showed invasive development and poor prognosis. lncRNAs have been illustrated to serve as biomarkers in various cancers. Whether the long non-coding RNA Prader Willi/Angelman region RNA 6 (PWAR6) was involved in glioma development and the underlying mechanism was investigated. PWAR6 in glioma was evaluated by polymerase chain reaction and its clinical significance was assessed with a series of statistical analyses. The biological function of PWAR6 was investigated with the cell counting kit 8 and Transwell assay. The potential underlying mechanism was studied with the luciferase reporter assay. The significant downregulation of PWAR6 was observed in glioma, which showed a close relationship with the major clinicopathological features and poor prognosis of patients. PWAR6 restrained cell growth, migration and invasion of glioma, which was alleviated by the overexpression of microRNA-106a-5p (miR-106a-5p). PWAR6 functioned as a prognostic biomarker and tumor suppressor of glioma through regulating miR-106a-5p.

A novel immune-related long noncoding RNA (lncRNA) pair model to predict the prognosis of triple-negative breast cancer

Background

Breast cancer (BC) is the most prevalent cancer type and is the principal cause of cancer-related death in women. Anti-programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) immunotherapy has shown promising effects in metastatic triple-negative breast cancer (TNBC), but the potential factors affecting its efficacy have not been elucidated. Immune-related long noncoding RNAs (irlncRNAs) have been reported to be involved in immune escape to influence the carcinogenic process through the PD-1/PD-L1 signaling pathway. Therefore, exploring the potential regulatory mechanism of irlncRNAs in PD-1/PD-L1 immunotherapy in TNBC is of great importance.

Methods

We retrieved transcriptome profiling data from The Cancer Genome Atlas (TCGA) and identified differentially expressed irlncRNA (DEirlncRNA) pairs. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct a risk assessment model.

Results

Receiver operating characteristic (ROC) curve analysis indicated that the risk model may serve as a potential prediction tool in TNBC patients. Clinical stage and risk score were proved to be independent prognostic predictors by univariate and multivariate Cox regression analyses. Subsequently, we investigated the correlation between the risk model and tumor-infiltrating immune cells and immune checkpoints. Finally, we identified USP30-AS1 through the StarBase and Multi Experiment Matrix (MEM) databases, predicted the potential target genes of USP30-AS1, and then discovered that these target genes were closely associated with immune responses.

Conclusions

Our study constructed a risk assessment model by irlncRNA pairs regardless of expression levels, which contributed to predicting the efficacy of immunotherapy in TNBC. Furthermore, the lncRNA USP30-AS1 in the model was positively correlated with the expression of PD-L1 and provided a potential therapeutic target for TNBC.

Quantitative study of bioinformatics analysis on glioma: a bibliometric analysis

Background

The bioinformatics analysis on glioma has been a hot point recently. The purpose of this study was to provide an overview of the research in this field using a bibliometric method.

Methods

The Web of Science Core Collection (WOSCC) database was used to search for literature related to the bioinformatics analysis of gliomas. Countries, institutions, authors, references, and keywords were analyzed using VOSviewer, CiteSpace, and Microsoft Excel software.

Result

China was the most productive country, while the USA was the most cited. Capital Medical University had the largest number of publications and citations. Institutions tend to collaborate more with other institutions in their countries rather than foreign ones. The most productive and most cited author was Jiang Tao. Two citation paths were identified, with literature in basic research journals often cited in clinical journals. Immune-related vocabularies appeared frequently in recent studies.

Conclusion

Glioma bioinformatics analyses spanned a wide range of fields. The international communication in this field urgently needs to be strengthened. Glioma bioinformatics approaches are developing from basic research to clinical applications. Recently, immune-related research has become a focus.

LncRNA WEE2-AS1 is a diagnostic biomarker that predicts poor prognoses in patients with glioma

BACKGROUND

Glioma is characterized by high morbidity, high mortality, and poor prognosis. Despite tremendous advances in the treatment of glioma, the prognosis of patients with glioma is still unsatisfactory. There is an urgent need to discover novel molecular markers that effectively predict prognosis in patients with glioma. The investigation of the role of WEE2-AS1 in various tumors is an emerging research field, but the biological function and prognostic value of WEE2-AS1 in glioma have rarely been reported. This study aimed to assess the value of WEE2-AS1 as a potential prognostic marker of glioma.

METHODS

Gene expression (RNA-Seq) data of patients with glioma were extracted from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. The Wilcoxon rank sum test was used to analyze the expression of WEE2-AS1 in the cells and tissues of glioma. The Kruskal-Wallis rank sum test, Wilcoxon rank sum test, and logistic regression were used to evaluate the relationship between clinical variables and expression of WEE2-AS1. Cox regression analysis and the Kaplan-Meier method were used to evaluate the prognostic factors in glioma. A nomogram based on Cox multivariate analysis was used to predict the impact of WEE2-AS1 on glioma prognosis. Gene Set Enrichment Analysis (GSEA) was used to identify key WEE2-AS1-associated signaling pathways. Spearman's rank correlation was used to elucidate the association between WEE2-AS1 expression and immune cell infiltration levels.

RESULTS

We found that WEE2-AS1 was overexpressed in a variety of cancers, including glioma. High expression of WEE2-AS1 was associated with glioma progression. We determined that the expression of WEE2-AS1 might be an independent risk factor for the survival and prognosis of patients with glioma. We further observed that the mechanism of WEE2-AS1-mediated tumorigenesis involved neuroactive ligand-receptor interaction, cell cycle, and the infiltration of immune cells into the glioma microenvironment.

CONCLUSION

These findings demonstrate that WEE2-AS1 is a promising biomarker for the diagnosis and prognosis of patients with glioma. An increased understanding of its effects on the regulation of cell growth may lead to the development of clinical applications that improve the prognostic status of patients with glioma.

Identification of two robust subclasses of sepsis with both prognostic and therapeutic values based on machine learning analysis

Background

Sepsis is a heterogeneous syndrome with high morbidity and mortality. Optimal and effective classifications are in urgent need and to be developed.

Methods and results

A total of 1,936 patients (sepsis samples, n=1,692; normal samples, n=244) in 7 discovery datasets were included to conduct weighted gene co-expression network analysis (WGCNA) to filter out candidate genes related to sepsis. Then, two subtypes of sepsis were classified in the training sepsis set (n=1,692), the Adaptive and Inflammatory, using K-means clustering analysis on 90 sepsis-related features. We validated these subtypes using 617 samples in 5 independent datasets and the merged 5 sets. Cibersort method revealed the Adaptive subtype was related to high infiltration levels of T cells and natural killer (NK) cells and a better clinical outcome. Immune features were validated by single-cell RNA sequencing (scRNA-seq) analysis. The Inflammatory subtype was associated with high infiltration of macrophages and a disadvantageous prognosis. Based on functional analysis, upregulation of the Toll-like receptor signaling pathway was obtained in Inflammatory subtype and NK cell-mediated cytotoxicity and T cell receptor signaling pathway were upregulated in Adaptive group. To quantify the cluster findings, a scoring system, called, risk score, was established using four datasets (n=980) in the discovery cohorts based on least absolute shrinkage and selection operator (LASSO) and logistic regression and validated in external sets (n=760). Multivariate logistic regression analysis revealed the risk score was an independent predictor of outcomes of sepsis patients (OR [odds ratio], 2.752, 95% confidence interval [CI], 2.234-3.389, P<0.001), when adjusted by age and gender. In addition, the validation sets confirmed the performance (OR, 1.638, 95% CI, 1.309-2.048, P<0.001). Finally, nomograms demonstrated great discriminatory potential than that of risk score, age and gender (training set: AUC=0.682, 95% CI, 0.643-0.719; validation set: AUC=0.624, 95% CI, 0.576-0.664). Decision curve analysis (DCA) demonstrated that the nomograms were clinically useful and had better discriminative performance to recognize patients at high risk than the age, gender and risk score, respectively.

Conclusions

In-depth analysis of a comprehensive landscape of the transcriptome characteristics of sepsis might contribute to personalized treatments and prediction of clinical outcomes.

Latest updates on cellular and molecular biomarkers of gliomas

Gliomas are the most common central nervous system malignancies, compromising almost 80% of all brain tumors and is associated with significant mortality. The classification of gliomas has shifted from basic histological perspective to one that is based on molecular biomarkers. Treatment of this type of tumors consists currently of surgery, chemotherapy and radiation therapy. During the past years, there was a limited development of effective glioma diagnostics and therapeutics due to multiple factors including the presence of blood-brain barrier and the heterogeneity of this type of tumors. Currently, it is necessary to highlight the advantage of molecular diagnosis of gliomas to develop patient targeted therapies based on multiple oncogenic pathway. In this review, we will evaluate the development of cellular and molecular biomarkers for the diagnosis of gliomas and the impact of these diagnostic tools for better tailored and targeted therapies.

Integrative analysis of expression profile indicates the ECM receptor and LTP dysfunction in the glioma-related epilepsy

Background

Seizures are a common symptom in glioma patients, and they can cause brain dysfunction. However, the mechanism by which glioma-related epilepsy (GRE) causes alterations in brain networks remains elusive.

Objective

To investigate the potential pathogenic mechanism of GRE by analyzing the dynamic expression profiles of microRNA/ mRNA/ lncRNA in brain tissues of glioma patients.

Methods

Brain tissues of 16 patients with GRE and 9 patients with glioma without epilepsy (GNE) were collected. The total RNA was dephosphorylated, labeled, and hybridized to the Agilent Human miRNA Microarray, Release 19.0, 8 × 60 K. The cDNA was labeled and hybridized to the Agilent LncRNA + mRNA Human Gene Expression Microarray V3.0, 4 × 180 K. The raw data was extracted from hybridized images using Agilent Feature Extraction, and quantile normalization was performed using the Agilent GeneSpring. P-value < 0.05 and absolute fold change > 2 were considered the threshold of differential expression data. Data analyses were performed using R and Bioconductor.

Results

We found that 3 differentially expressed miRNAs (miR-10a-5p, miR-10b-5p, miR-629-3p), 6 differentially expressed lncRNAs (TTN-AS1, LINC00641, SNHG14, LINC00894, SNHG1, OIP5-AS1), and 49 differentially expressed mRNAs play a vitally critical role in developing GRE. The expression of GABARAPL1, GRAMD1B, and IQSEC3 were validated more than twofold higher in the GRE group than in the GNE group in the validation cohort. Pathways including ECM receptor interaction and long-term potentiation (LTP) may contribute to the disease’s progression. Meanwhile, We built a lncRNA-microRNA-Gene regulatory network with structural and functional significance.

Conclusion

These findings can offer a fresh perspective on GRE-induced brain network changes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08665-8.

Role of Long Non-Coding RNA LINC00641 in Cancer

Long non-coding RNAs (lncRNAs) are non-protein coding RNAs with more than 200 nucleic acids in length. When lncRNAs are located in the nucleus, they regulate chromosome structure, participate in chromatin remodeling, and act as transcription regulators. When lncRNAs are exported to the cytoplasm, they regulate mRNA stability, regulate translation, and interfere with post-translational modification. In recent years, more and more evidences have shown that lncRNA can regulate the biological processes of tumor proliferation, apoptosis, invasion and metastasis, and can participate in a variety of tumor signaling pathways. Long-gene non-protein coding RNA641 (LINC00641), located on human chromosome 14q11.2, is differentially expressed in a variety of tumors and is related to overall survival and prognosis, etc. Interfering the expression of LINC00641 can lead to changes in tumor cell proliferation, invasion, metastasis, apoptosis and other biological behaviors. Therefore, LINC00641 is a promising new biomarker and potential clinical therapeutic target. In this review, the biological functions, related mechanisms and clinical significance of LINC00641 in many human cancers are described in detail.