An eight-mRNA signature outperforms the lncRNA-based signature in predicting prognosis of patients with glioblastoma

Shedding light on function of long non-coding RNAs (lncRNAs) in glioblastoma

The brain tumors and especially glioblastoma, are affecting life of many people worldwide and due to their high mortality and morbidity, their treatment is of importance and has gained attention in recent years. The abnormal expression of genes is commonly observed in GBM and long non-coding RNAs (lncRNAs) have demonstrated dysregulation in this tumor. LncRNAs have length more than 200 nucleotides and they have been located in cytoplasm and nucleus. The current review focuses on the role of lncRNAs in GBM. There two types of lncRNAs in GBM including tumor-promoting and tumor-suppressor lncRNAs and overexpression of oncogenic lncRNAs increases progression of GBM. LncRNAs can regulate proliferation, cell cycle arrest and metastasis of GBM cells. Wnt, STAT3 and EZH2 are among the molecular pathways affected by lncRNAs in GBM and for regulating metastasis of GBM cells, these RNA molecules mainly affect EMT mechanism. LncRNAs are involved in drug resistance and can induce resistance of GBM cells to temozolomide chemotherapy. Furthermore, lncRNAs stimulate radio-resistance in GBM cells. LncRNAs increase PD-1 expression to mediate immune evasion. LncRNAs can be considered as diagnostic and prognostic tools in GBM and researchers have developed signature from lncRNAs in GBM.

Extracellular cancer‑associated fibroblasts: A novel subgroup in the cervical cancer microenvironment that exhibits tumor‑promoting roles and prognosis biomarker functions

Tumor invasion and metastasis are the processes that primarily cause adverse outcomes in patients with cervical cancer. Cancer-associated fibroblasts (CAFs), which participate in cancer progression and metastasis, are novel targets for the treatment of tumors. The present study aimed to assess the heterogeneity of CAFs in the cervical cancer microenvironment through single-cell RNA sequencing. After collecting five cervical cancer samples and obtaining the CAF-associated gene sets, the CAFs in the cervical cancer microenvironment were divided into myofibroblastic CAFs and extracellular (ec)CAFs. The ecCAFs appeared with more robust pro-tumorigenic effects than myCAFs according to enrichment analysis. Subsequently, through combining the ecCAF hub genes and bulk gene expression data for cervical cancer obtained from The Cancer Genome Atlas and Gene Ontology databases, univariate Cox regression and least absolute shrinkage and selection operator analyses were performed to establish a CAF-associated risk signature for patients with cancer. The established risk signature demonstrated a stable and strong prognostic capability in both the training and validation cohorts. Subsequently, the association between the risk signature and clinical data was evaluated, and a nomogram to facilitate clinical application was established. The risk score was demonstrated to be associated with both the tumor immune microenvironment and the therapeutic responses. Moreover, the signature also has predictive value for the prognosis of head and neck squamous cell carcinoma, and bladder urothelial carcinoma, which were also associated with human papillomavirus infection. In conclusion, the present study assessed the heterogeneity of CAFs in the cervical cancer microenvironment, and a subgroup of CAFs that may be closely associated with tumor progression was defined. Moreover, a signature based on the hub genes of ecCAFs was shown to have biomarker functionality in terms of predicting survival rates, and therefore this CAF subgroup may become a therapeutic target for cervical cancer in the future.

A Potential Prognostic Gene Signature Associated with p53-Dependent NTRK1 Activation and Increased Survival of Neuroblastoma Patients

Neuroblastoma is the most common extracranial solid tumour in children, comprising close to 10% of childhood cancer-related deaths. We have demonstrated that activation of NTRK1 by TP53 repression of PTPN6 expression is significantly associated with favourable survival in neuroblastoma. The molecular mechanisms by which this activation elicits cell molecular changes need to be determined. This is critical to identify dependable biomarkers for the early detection and prognosis of tumours, and for the development of personalised treatment. In this investigation we have identified and validated a gene signature for the prognosis of neuroblastoma using genes differentially expressed upon activation of the NTRK1-PTPN6-TP53 module. A random survival forest model was used to construct a gene signature, which was then assessed across validation datasets using Kaplan-Meier analysis and ROC curves. The analysis demonstrated that high BASP1, CD9, DLG2, FNBP1, FRMD3, IL11RA, ISGF10, IQCE, KCNQ3, and TOX2, and low BSG/CD147, CCDC125, GABRB3, GNB2L1/RACK1 HAPLN4, HEBP2, and HSD17B12 expression was significantly associated with favourable patient event-free survival (EFS). The gene signature was associated with favourable tumour histology and NTRK1-PTPN6-TP53 module activation. Importantly, all genes were significantly associated with favourable EFS in an independent manner. Six of the signature genes, BSG/CD147, GNB2L1/RACK1, TXNDC5, FNPB1, B3GAT1, and IGSF10, play a role in cell differentiation. Our findings strongly suggest that the identified gene signature is a potential prognostic biomarker and therapeutic target for neuroblastoma patients and that it is associated with neuroblastoma cell differentiation through the activation of the NTRK1-PTPN6-TP53 module.

Exploring the potential biomarkers for prognosis of glioblastoma via weighted gene co-expression network analysis

BACKGROUND

Glioblastoma (GBM) is the most common malignant tumor in the central system with a poor prognosis. Due to the complexity of its molecular mechanism, the recurrence rate and mortality rate of GBM patients are still high. Therefore, there is an urgent need to screen GBM biomarkers to prove the therapeutic effect and improve the prognosis.

RESULTS

We extracted data from GBM patients from the Gene Expression Integration Database (GEO), analyzed differentially expressed genes in GEO and identified key modules by weighted gene co-expression network analysis (WGCNA). GSE145128 data was obtained from the GEO database, and the darkturquoise module was determined to be the most relevant to the GBM prognosis by WGCNA (r =  - 0.62, p = 0.01). We performed enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to reveal the interaction activity in the selected modules. Then Kaplan-Meier survival curve analysis was used to extract genes closely related to GBM prognosis. We used Kaplan-Meier survival curves to analyze the 139 genes in the darkturquoise module, identified four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM. Low expression of DARS/GDI2/TRUB1 and high expression of P4HA2 had a poor prognosis. Finally, we used tumor genome map (TCGA) data, verified the characteristics of hub genes through Co-expression analysis, Drug sensitivity analysis, TIMER database analysis and GSVA analysis. We downloaded the data of GBM from the TCGA database, the results of co-expression analysis showed that DARS/GDI2/P4HA2/TRUB1 could regulate the development of GBM by affecting genes such as CDC73/CDC123/B4GALT1/CUL2. Drug sensitivity analysis showed that genes are involved in many classic Cancer-related pathways including TSC/mTOR, RAS/MAPK.TIMER database analysis showed DARS expression is positively correlated with tumor purity (cor = 0.125, p = 1.07e-02)), P4HA2 expression is negatively correlated with tumor purity (cor =-0.279, p = 6.06e-09). Finally, GSVA analysis found that DARS/GDI2/P4HA2/TRUB1 gene sets are closely related to the occurrence of cancer.

CONCLUSION

We used two public databases to identify four valuable biomarkers for GBM prognosis, namely DARS/GDI2/P4HA2/TRUB1, which have potential clinical application value and can be used as prognostic markers for GBM.

m5C RNA methyltransferase-related gene NSUN4 stimulates malignant progression of hepatocellular carcinoma and can be a prognostic marker

Hepatocellular carcinoma (HCC) is a cancer with relatively high mortality, yet little attention has been devoted for related prognostic biomarkers. This study analyzed differential expression of m5C RNA methyltransferase-related genes in normal samples and tumors samples in TCGA-LIHC using Wilcoxon test. K-means consensus clustering analysis was implemented to subdivide samples. Independent prognostic factors were screened by univariate and multivariate Cox regression analyses. KEGG pathway enrichment analysis was performed on the screened independent prognostic factor using GSEA tools. qPCR was conducted to test mRNA expression of key m5C RNA methyltransferase-related genes in tissues and cells. There were 7 m5C RNA methyltransferase-related genes (NOP2, NSUN4, etc.) differentially expressed in HCC tumor tissues. HCC samples were classified into 3 subgroups through clustering analysis according to the expression mode of m5C RNA methyltransferase-related genes. It was also discovered that patients in different subgroups presented significant differences in survival rate and distribution of grade. Additionally, NOP2, NSUN4 and NSUN5 expression notable varied in different grades. Through regression analyses combined with various clinical pathological factors, it was displayed that NSUN4 could work as an independent prognostic factor. KEGG analysis showed that NSUN4 mainly enriched in signaling pathways involved in ADHERENS JUNCTION, RNA DEGRADATION, MTOR SIGNALING PATHWAY, COMPLEMENT and COAGULATION CASCADES. As examined by qPCR, NSUN4 was conspicuously upregulated in HCC patient's tissues and cells. Altogether, our study preliminarily developed a novel biomarker that could be independently used in prognosis of HCC, which may provide a new direction for the study of related molecular mechanism or treatment regimen.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Whole-Genome Sequencing Reveals Exonic Variation of ASIC5 Gene Results in Recurrent Pregnancy Loss

Family trio next-generation sequencing-based variant analysis was done to identify the genomic reason on unexplained recurrent pregnancy loss (RPL). A family (dead fetus and parents) from Saudi Arabia with an earlier history of three unexplained RPLs at the ninth week of pregnancy was included in the study. Whole-genome sequencing (WGS) of a dead fetus and the parents was done to identify the pathogenic variation and confirmed through Sanger sequencing. WGS of dead fetus identifies a novel homozygous exonic variation (NM_017419.3:c.680G>T) in ASIC5 (acid-sensing ion channel subunit family member 5) gene; the parents are heterozygous. Newly designed ARMS PCR followed by direct sequencing confirms the presence of heterozygous in one subject and absence of homozygous novel mutation among randomly selected healthy Saudis. The second family with heterozygous was confirmed with three unexplained RPLs. Pathogenicity analysis of R227I amino acid substitution in ASIC5 protein through molecular docking and interaction analysis revealed that the mutations are highly pathogenic, decrease the stability of the protein, and prevent binding of amiloride, which is an activator to open the acid-sensing ion channel of ASIC5. The identified rare and novel autosomal recessive mutation, c.680G>T:p.R227I (ASIC5Saudi), in two families confirm the ASIC5 gene association with RPL and can be fatal to the fetus.

Long Non-Coding RNAs in Diagnosis, Treatment, Prognosis, and Progression of Glioma: A State-of-the-Art Review

Glioma is the most common malignant central nervous system tumor with significant mortality and morbidity. Despite considerable advances, the exact molecular pathways involved in tumor progression are not fully elucidated, and patients commonly face a poor prognosis. Long non-coding RNAs (lncRNAs) have recently drawn extra attention for their potential roles in different types of cancer as well as non-malignant diseases. More than 200 lncRNAs have been reported to be associated with glioma. We aimed to assess the roles of the most investigated lncRNAs in different stages of tumor progression and the mediating molecular pathways in addition to their clinical applications. lncRNAs are involved in different stages of tumor formation, invasion, and progression, including regulating the cell cycle, apoptosis, autophagy, epithelial-to-mesenchymal transition, tumor stemness, angiogenesis, the integrity of the blood-tumor-brain barrier, tumor metabolism, and immunological responses. The well-known oncogenic lncRNAs, which are upregulated in glioma, are H19, HOTAIR, PVT1, UCA1, XIST, CRNDE, FOXD2-AS1, ANRIL, HOXA11-AS, TP73-AS1, and DANCR. On the other hand, MEG3, GAS5, CCASC2, and TUSC7 are tumor suppressor lncRNAs, which are downregulated. While most studies reported oncogenic effects for MALAT1, TUG1, and NEAT1, there are some controversies regarding these lncRNAs. Expression levels of lncRNAs can be associated with tumor grade, survival, treatment response (chemotherapy drugs or radiotherapy), and overall prognosis. Moreover, circulatory levels of lncRNAs, such as MALAT1, H19, HOTAIR, NEAT1, TUG1, GAS5, LINK-A, and TUSC7, can provide non-invasive diagnostic and prognostic tools. Modulation of expression of lncRNAs using antisense oligonucleotides can lead to novel therapeutics. Notably, a profound understanding of the underlying molecular pathways involved in the function of lncRNAs is required to develop novel therapeutic targets. More investigations with large sample sizes and increased focus on in-vivo models are required to expand our understanding of the potential roles and application of lncRNAs in glioma.

A Signature of Nine lncRNA Methylated Genes Predicts Survival in Patients With Glioma

Glioma is one of the most common malignant tumors of the central nervous system, and its prognosis is extremely poor. Aberrant methylation of lncRNA promoter region is significantly associated with the prognosis of glioma patients. In this study, we investigated the potential impact of methylation of lncRNA promoter region in glioma patients to establish a signature of nine lncRNA methylated genes for determining glioma patient prognosis. Methylation data and clinical follow-up data were obtained from The Cancer Genome Atlas (TCGA). The multistep screening strategy identified nine lncRNA methylated genes that were significantly associated with the overall survival (OS) of glioma patients. Subsequently, we constructed a risk signature that containing nine lncRNA methylated genes. The risk signature successfully divided the glioma patients into high-risk and low-risk groups. Compared with the low-risk group, the high-risk group had a worse prognosis, higher glioma grade, and older age. Furthermore, we identified two lncRNAs termed PCBP1-AS1 and LINC02875 that may be involved in the malignant progression of glioma cells by using the TCGA database. Loss-of-function assays confirmed that knockdown of PCBP1-AS1 and LINC02875 inhibited the proliferation, migration, and invasion of glioma cells. Therefore, the nine lncRNA methylated genes signature may provide a novel predictor and therapeutic target for glioma patients.

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

Accumulating studies have confirmed the crucial role of long non-coding RNAs (ncRNAs) as favorable biomarkers for cancer diagnosis, therapy, and prognosis prediction. In our recent study, we established a robust model which is based on multi-gene signature to predict the therapeutic efficacy and prognosis in glioblastoma (GBM), based on Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. lncRNA-seq data of GBM from TCGA and CGGA datasets were used to identify differentially expressed genes (DEGs) compared to normal brain tissues. The DEGs were then used for survival analysis by univariate and multivariate COX regression. Then we established a risk score model, depending on the gene signature of multiple survival-associated DEGs. Subsequently, Kaplan-Meier analysis was used for estimating the prognostic and predictive role of the model. Gene set enrichment analysis (GSEA) was applied to investigate the potential pathways associated to high-risk score by the R package “cluster profile” and Wiki-pathway. And five survival associated lncRNAs of GBM were identified: LNC01545, WDR11-AS1, NDUFA6-DT, FRY-AS1, TBX5-AS1. Then the risk score model was established and shows a desirable function for predicting overall survival (OS) in the GBM patients, which means the high-risk score significantly correlated with lower OS both in TCGA and CGGA cohort. GSEA showed that the high-risk score was enriched with PI3K-Akt, VEGFA-VEGFR2, TGF-beta, Notch, T-Cell pathways. Collectively, the five-lncRNAs signature-derived risk score presented satisfactory efficacies in predicting the therapeutic efficacy and prognosis in GBM and will be significant for guiding therapeutic strategies and research direction for GBM.