Integrated mRNAseq and microRNAseq data analysis for grade III gliomas

KIFC1 in cancer: Understanding its expression, regulation, and therapeutic potential

Kinesins are a family of motor proteins essential for intracellular transport and cellular dynamics, with kinesin family member C1 (KIFC1) emerging as a key regulator of cancer progression. Recent studies highlight KIFC1's crucial role in mitotic spindle assembly, chromosome segregation, and cell migration-processes frequently dysregulated in cancer. Its involvement in promoting malignant cell proliferation and metastasis underscores its significance in tumor biology. In various cancer types, aberrant KIFC1 expression correlates with poor prognosis and aggressive phenotypes, suggesting its potential as a biomarker for disease severity. Mechanistically, KIFC1 influences signaling pathways linked to cell cycle regulation and programmed cell death, reinforcing its role in oncogenesis. Given its pivotal function in cancer cell dynamics, KIFC1 represents a promising therapeutic target. Strategies aimed at modulating its activity, including small molecules or RNA interference, could disrupt cancer cell viability and proliferation. The current review article highlights KIFC1's importance in cancer biology, advocating for further investigation into its mechanisms and the development of KIFC1-targeted therapies to enhance treatment efficacy and improve patient outcomes across various malignancies.

The G Protein-Coupled Receptor-Related Gene Signatures for Diagnosis and Prognosis in Glioblastoma: A Deep Learning Model Using RNA-Seq Data

BACKGROUND

Glioblastoma (GBM) is the most aggressive cancer in the central nervous system in glial cells. Finding novel biomarkers in GBM offers numerous advantages that can contribute to early detection, personalized treatment, improved patient outcomes, and advancements in cancer research and drug development. Integrating machine learning with RNAseq data in medicine holds significant potential for identifying novel biomarkers in various diseases, including cancer.

METHODS

Gene expression raw data was used to detect differentially expressed genes (DEGs) within a cohort of 532 GBM patients. The molecular pathway analysis, disease ontology, and protein-protein interactions of DEGs were assessed. Machine learning methods were performed to identify candidate genes. Survival curves were estimated using the Kaplan-Meier method and Cox proportional hazard to find prognostic biomarkers.

RESULTS

The molecular pathway analysis revealed that key dysregulated genes are in GPCRs, class A rhodopsin-like, MAPK signaling pathway, and calcium regulation in cardiac cells. Additionally, survival analysis showed that ten downregulated genes, including CPLX3, GPR162, LCNL1, SLC5A5, GPR61, GPR68, IL1RL2, HCRTR1, AIPL1, and SYTL1, and also ten upregulated genes, including C1orf92, CATSPER1, CCDC19, EPS8L1, FAIM3, FAM70B, FCN3, GPR157, IGFBP1, and MYBPH decreased the overall survival in GBM patients. Furthermore, the machine learning detected twenty genes, among which LRRTM2 and OPRL1 were candidates with high correlation coefficients.

CONCLUSION

Our data suggest that genes belonging to G Protein-Coupled Receptors play a critical role in various aspects of glioblastoma progression and pathogenesis. Four members of GPCRs, including GPR162, GPR61, GPR68, and GPR157, can be considered prognostic biomarkers. Additionally, the combination of A2BP1 and GPR157 was reported as a diagnostic marker.

Inferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classifications: A Network-Based Approach

Tumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official World Health Organization (WHO) classification of the central nervous system (CNS). These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this context, the search for new potential diagnostic and prognostic targets, characteristic of each glioma type, is crucial to support the development of novel therapies. Based on The Cancer Genome Atlas (TCGA) glioma RNA-sequencing data set updated according to the 2016 and 2021 WHO guidelines, we proposed a 2-step variable selection approach for biomarker discovery. Our framework encompasses the graphical lasso algorithm to estimate sparse networks of genes carrying diagnostic information. These networks are then used as input for regularized Cox survival regression model, allowing the identification of a smaller subset of genes with prognostic value. In each step, the results derived from the 2016 and 2021 classes were discussed and compared. For both WHO glioma classifications, our analysis identifies potential biomarkers, characteristic of each glioma type. Yet, better results were obtained for the WHO CNS classification in 2021, thereby supporting recent efforts to include molecular data on glioma classification.

High Expression of KIFC1 in Glioma Correlates with Poor Prognosis

OBJECTIVE

Kinesin family member C1 (KIFC1), a non-essential kinesin-like motor protein, has been found to serve a crucial role in supernumerary centrosome clustering and the progression of several human cancer types. However, the role of KIFC1 in glioma has been rarely reported. Thus, the present study aimed to investigate the role of KIFC1 in glioma progression.

METHODS

Online bioinformatics analysis was performed to determine the association between KIFC1 expression and clinical outcomes in glioma. Immunohistochemical staining was conducted to analyze the expression levels of KIFC1 in glioma and normal brain tissues. Furthermore, KIFC1 expression was knocked in the glioma cell lines, U251 and U87MG, and the functional roles of KIFC1 in cell proliferation, invasion and migration were analyzed using cell multiplication, wound healing and Transwell invasion assays, respectively. The autophagic flux and expression levels matrix metalloproteinase-2 (MMP2) were also determined using imaging flow cytometry, western blotting and a gelation zymography assay.

RESULTS

The results revealed that KIFC1 expression levels were significantly upregulated in glioma tissues compared with normal brain tissues, and the expression levels were positively associated with tumor grade. Patients with glioma with low KIFC1 expression levels had a more favorable prognosis compared with patients with high KIFC1 expression levels. In vitro, KIFC1 knockdown not only inhibited the proliferation, migration and invasion of glioma cells, but also increased the autophagic flux and downregulated the expression levels of MMP2.

CONCLUSION

Upregulation of KIFC1 expression may promote glioma progression and KIFC1 may serve as a potential prognostic biomarker and possible therapeutic target for glioma.

An in silico approach to the identification of diagnostic and prognostic markers in low-grade gliomas

Low-grade gliomas (LGG) are central nervous system Grade I tumors, and as they progress they are becoming one of the deadliest brain tumors. There is still great need for timely and accurate diagnosis and prognosis of LGG. Herein, we aimed to identify diagnostic and prognostic biomarkers associated with LGG, by employing diverse computational approaches. For this purpose, differential gene expression analysis on high-throughput transcriptomics data of LGG versus corresponding healthy brain tissue, derived from TCGA and GTEx, respectively, was performed. Weighted gene co-expression network analysis of the detected differentially expressed genes was carried out in order to identify modules of co-expressed genes significantly correlated with LGG clinical traits. The genes comprising these modules were further used to construct gene co-expression and protein-protein interaction networks. Based on the network analyses, we derived a consensus of eighteen hub genes, namely, CD74, CD86, CDC25A, CYBB, HLA-DMA, ITGB2, KIF11, KIFC1, LAPTM5, LMNB1, MKI67, NCKAP1L, NUSAP1, SLC7A7, TBXAS1, TOP2A, TYROBP, and WDFY4. All detected hub genes were up-regulated in LGG, and were also associated with unfavorable prognosis in LGG patients. The findings of this study could be applicable in the clinical setting for diagnosing and monitoring LGG.

An integrated multi-omics analysis of topoisomerase family in pan-cancer: Friend or foe?

BACKGROUND

Topoisomerases are nuclear enzymes that get to the bottom of topological troubles related with DNA all through a range of genetic procedures. More and more studies have shown that topoisomerase-mediated DNA cleavage plays crucial roles in tumor cell death and carcinogenesis. There is however still a lack of comprehensive multi-omics studies related to topoisomerase family genes from a pan-cancer perspective.

METHODS

In this study, a multiomics pan-cancer analysis of topoisomerase family genes was conducted by integrating over 10,000 multi-dimensional cancer genomic data across 33 cancer types from The Cancer Genome Atlas (TCGA), 481 small molecule drug response data from cancer therapeutics response portal (CTRP) as well as normal tissue data from Genotype-Tissue Expression (GTEx). Finally, overall activity-level analyses of topoisomerase in pan-cancers were performed by gene set variation analysis (GSVA), together with differential expression, clinical relevancy, immune cell infiltration and regulation of cancer-related pathways.

RESULTS

Dysregulated gene expression of topoisomerase family were related to genomic changes and abnormal epigenetic modifications. The expression levels of topoisomerase family genes could significantly impact cancer progression, intratumoral heterogeneity, alterations in the immunological condition and regulation of the cancer marker-related pathways, which in turn caused the differences in potential drugs sensitivity and the distinct prognosis of patients.

CONCLUSION

It was anticipated that topoisomerase family genes would become novel prognostic biomarkers for cancer patients and provide new insights for the diagnosis and treatment of tumors.

Identification of Molecular Characteristics and New Prognostic Targets for Thymoma by Multiomics Analysis

BACKGROUND

Thymoma is a heterogeneous tumor originated from thymic epithelial cells. The molecular mechanism of thymoma remains unclear.

METHODS

The expression profile, methylation, and mutation data of thymoma were obtained from TCGA database. The coexpression network was constructed using the variance of gene expression through WGCNA. Enrichment analysis using clusterProfiler R package and overall survival (OS) analysis by Kaplan-Meier method were carried out for the intersection of differential expression genes (DEGs) screened by limma R package and important module genes. PPI network was constructed based on STRING database for genes with significant impact on survival. The impact of key genes on the prognosis of thymoma was evaluated by ROC curve and Cox regression model. Finally, the immune cell infiltration, methylation modification, and gene mutation were calculated.

RESULTS

We obtained eleven coexpression modules, and three of them were higher positively correlated with thymoma. DEGs in these three modules mainly involved in MAPK cascade and PPAR pathway. LIPE, MYH6, ACTG2, KLF4, SULT4A1, and TF were identified as key genes through the PPI network. AUC values of LIPE were the highest. Cox regression analysis showed that low expression of LIPE was a prognostic risk factor for thymoma. In addition, there was a high correlation between LIPE and T cells. Importantly, the expression of LIPE was modified by methylation. Among all the mutated genes, GTF2I had the highest mutation frequency.

CONCLUSION

These results suggested that the molecular mechanism of thymoma may be related to immune inflammation. LIPE may be the key genes affecting prognosis of thymoma. Our findings will help to elucidate the pathogenesis and therapeutic targets of thymoma.

Identification of robust diagnostic and prognostic gene signatures in different grades of gliomas: a retrospective study

Background

Gliomas are the most common primary tumors of the central nervous system. The complexity and heterogeneity of the tumor makes it difficult to obtain good biomarkers for drug development. In this study, through The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), we analyze the common diagnostic and prognostic moleculer markers in Caucasian and Asian populations, which can be used as drug targets in the future.

Methods

The RNA-seq data from Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) were analyzed to identify signatures. Based on the signatures, the prognosis index (PI) of every patient was constructed to predict the prognostic risk. Also, gene ontology (GO) functional enrichment analysis and KEGG analysis were conducted to investigate the biological functions of these mRNAs. Glioma patients’ data in the CGGA database were introduced to validate the effectiveness of the signatures among Chinese populations. Excluding the previously reported prognostic markers of gliomas from this study, the expression of HSPA5 and MTPN were examined by qRT-PCR and immunohistochemical assay.

Results

In total, 20 mRNAs were finally selected to build PI for patients from TCGA, including 16 high-risk genes and four low-risk genes. For Chinese patients, the log-rank test p values of PI were both less than 0.0001 in two independent datasets. And the AUCs were 0.831 and 0.907 for 3 years of two datasets, respectively. Moreover, among these 20 mRNAs, 10 and 15 mRNAs also had a significant predictive effect via univariate COX analysis in CGGA_693 and CGGA_325, respectively. qRT-PCR and Immunohistochemistry assay indicated that HSPA5 and MTPN over-expressed in Glioma samples compared to normal samples.

Conclusion

The 20-gene signature can forecast the risk of Glioma in TCGA effectively, moreover it can also predict the risks of Chinese patients through validation in the CGGA database. HSPA5 and MTPN are possible biomarkers of gliomas suitable for all populations to improve the prognosis of these patients.

Investigation of miRNA and mRNA Co-expression Network in Ependymoma

Ependymoma (EPN) is a rare primary tumor of the central nervous system (CNS) that affects both children and adults. Despite the definition and classification of distinct molecular subgroups, there remains a group of EPNs with a balanced genome, which makes it difficult to predict a prognosis of patients with EPN. The role of miRNA-mRNA network on EPN is still poorly understood. We assessed the involvement of miRNA-mRNA pairs in EPN by applying a weighted co-expression network analysis (WGCNA) approach. Using whole genome expression profile analysis followed by functional enrichment, we detected hub genes involved in active proliferation and DNA replication of nerve cells. Key genes including CYP11B1, KRT33B, RUNX1T1, SIK1, MAP3K4, MLANA, and SFRP5 identified in co-expression networks were regulated by miR-15a and miR-24-1. These seven miRNA-mRNA pairs were considered to influence not only pathways in cancer and tumor suppression process, but also MAPK, NF-kappaB, and WNT signaling pathways which were associated with tumorigenesis and development. This study provides a novel insight into potential diagnostic biomarkers of EPN and may have value in choosing therapeutic targets with clinical utility.

Tumor Suppressor microRNA-138 Suppresses Low-Grade Glioma Development and Metastasis via Regulating IGF2BP2

Background

Low-grade gliomas (LGG), approximately constitute one-third of all types of gliomas, are prone to relapse and metastasis. MicroRNA-138 (miR-138) is reported to be dysregulated in diverse human tumors and mainly function as a tumor suppressor. In this study, we analyzed the expression profile and function of miR-138 in LGG.

Methods

Quantitative PCR (qPCR) and public database bioinformatics analysis were performed to determine the miR-138 levels in LGG. MiR-138 overexpression in LGG cells was achieved by miR-138 mimics transfection. Cell proliferation was assessed by CCK8, EdU and colony formation assays. Cell invasion and migration were analyzed by transwell and wound-healing assays. Xenograft model was employed to study the role of miR-138 in LGG growth in vivo. The target of miR-138 was validated by multiple methods, such as luciferase reporter assay, RT-qPCR and Western blot. Bioinformatics analysis was conducted to explore the molecular mechanisms by which miR-138 contributed to LGG progression.

Results

miR-138 was significantly downregulated in LGG tumor tissues and low expression of miR-138 was significantly associated with poor prognosis as well as relapse and metastasis in LGG patients. Functional analysis indicated that ectopic miR-138 expression suppressed LGG cell growth and invasive phenotype in vitro, and inhibited tumor development in vivo. Moreover, miR-138 directly targeted and repressed insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) by targeting the 3ʹ-UTR of IGF2BP2, inhibiting epithelial to mesenchymal transition (EMT) to attenuate LGG aggressiveness. In addition, we found that elevated IGF2BP2 expression correlates with poor survival of LGG patients.

Conclusion

miR-138 may function as a tumor inhibitor by directly inhibiting IGF2BP2 and suppressing EMT in the progression of LGG.