Integrative analysis of KIF4A, 9, 18A, and 23 and their clinical significance in low-grade glioma and glioblastoma

Signature Genes Selection and Functional Analysis of Astrocytoma Phenotypes: A Comparative Study

Novel cancer biomarkers discoveries are driven by the application of omics technologies. The vast quantity of highly dimensional data necessitates the implementation of feature selection. The mathematical basis of different selection methods varies considerably, which may influence subsequent inferences. In the study, feature selection and classification methods were employed to identify six signature gene sets of grade 2 and 3 astrocytoma samples from the Rembrandt repository. Subsequently, the impact of these variables on classification and further discovery of biological patterns was analysed. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and hierarchical clustering revealed that the data set (10,096 genes) exhibited a high degree of noise, feature redundancy, and lack of distinct patterns. The application of feature selection methods resulted in a reduction in the number of genes to between 28 and 128. Notably, no single gene was selected by all of the methods tested. Selection led to an increase in classification accuracy and noise reduction. Significant differences in the Gene Ontology terms were discovered, with only 13 terms overlapping. One selection method did not result in any enriched terms. KEGG pathway analysis revealed only one pathway in common (cell cycle), while the two methods did not yield any enriched pathways. The results demonstrated a significant difference in outcomes when classification-type algorithms were utilised in comparison to mixed types (selection and classification). This may result in the inadvertent omission of biological phenomena, while simultaneously achieving enhanced classification outcomes.

The kinesin motor Kif9 regulates centriolar satellite positioning and mitotic progression

Centrosomes are the principal microtubule-organizing centers of the cell and play an essential role in mitotic spindle function. Centrosome biogenesis is achieved by strict control of protein acquisition and phosphorylation prior to mitosis. Defects in this process promote fragmentation of pericentriolar material culminating in multipolar spindles and chromosome missegregation. Centriolar satellites, membrane-less aggrupations of proteins involved in the trafficking of proteins toward and away from the centrosome, are thought to contribute to centrosome biogenesis. Here we show that the microtubule plus-end directed kinesin motor Kif9 localizes to centriolar satellites and regulates their pericentrosomal localization during interphase. Lack of Kif9 leads to aggregation of satellites closer to the centrosome and increased centrosomal protein degradation that disrupts centrosome maturation and results in chromosome congression and segregation defects during mitosis. Our data reveal roles for Kif9 and centriolar satellites in the regulation of cellular proteostasis and mitosis.

KIF4A promotes epithelial-mesenchymal transition by activating the TGF-β/SMAD signaling pathway in glioma cells

Gliomas are the most prevalent type of primary brain tumor, with poor prognosis reported in patients with high-grade glioma. Kinesin family member 4 A (KIF4A) stimulates the proliferation, migration, and invasion of tumor cells. However, its function in gliomas has not been clearly established. Therefore, this study aimed to investigate the effects of KIF4A on the epithelial-mesenchymal transition and invasion of glioma cells. We searched The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases to identify KIF4A-related signaling pathways and downstream genes. We further validated them using western blotting, transwell migration and invasion, wound-healing scratch, and dual-luciferase reporter assays in U251 and U87 human glioblastoma cells. Our analysis of the Cancer Genome Atlas and Chinese Glioma Genome Atlas data showed elevated KIF4A expression in patients with gliomas and was associated with clinical grade. Here, KIF4A overexpression promoted the migration, invasion, and proliferation of glioma cells, whereas KIF4A knockdown showed contrasting results. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) analyses demonstrated that KIF4A positively controls TGF-β/SMAD signaling in glioma cells. Additionally, genetic correlation analysis revealed that KIF4A transcriptionally controls benzimidazoles-1 expression in glioma cells. KIF4A promotes the epithelial-mesenchymal transition by regulating the TGF-β/SMAD signaling pathway via benzimidazoles-1 in glioma cells.

The Comprehensive Analysis of m6A-Associated Anoikis Genes in Low-Grade Gliomas

The relationship between N6-methyladenosine (m6A) regulators and anoikis and their effects on low-grade glioma (LGG) is not clear yet. The TCGA-LGG cohort, mRNAseq 325 dataset, and GSE16011 validation set were separately obtained via the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Altas (CGGA), and Gene Expression Omnibus (GEO) databases. In total, 27 m6A-related genes (m6A-RGs) and 508 anoikis-related genes (ANRGs) were extracted from published articles individually. First, differentially expressed genes (DEGs) between LGG and normal samples were sifted out by differential expression analysis. DEGs were respectively intersected with m6A-RGs and ANRGs to acquire differentially expressed m6A-RGs (DE-m6A-RGs) and differentially expressed ANRGs (DE-ANRGs). A correlation analysis of DE-m6A-RGs and DE-ANRGs was performed to obtain DE-m6A-ANRGs. Next, univariate Cox and least absolute shrinkage and selection operator (LASSO) were performed on DE-m6A-ANRGs to sift out risk model genes, and a risk score was gained according to them. Then, gene set enrichment analysis (GSEA) was implemented based on risk model genes. After that, we constructed an independent prognostic model and performed immune infiltration analysis and drug sensitivity analysis. Finally, an mRNA-miRNA-lncRNA regulatory network was constructed. There were 6901 DEGs between LGG and normal samples. Six DE-m6A-RGs and 214 DE-ANRGs were gained through intersecting DEGs with m6A-RGs and ANRGs, respectively. A total of 149 DE-m6A-ANRGs were derived after correlation analysis. Four genes, namely ANXA5, KIF18A, BRCA1, and HOXA10, composed the risk model, and they were involved in apoptosis, fatty acid metabolism, and glycolysis. The age and risk scores were finally sifted out to construct an independent prognostic model. Activated CD4 T cells, gamma delta T cells, and natural killer T cells had the largest positive correlations with risk model genes, while activated B cells were significantly negatively correlated with KIF18A and BRCA1. AT.9283, EXEL.2280, Gilteritinib, and Pracinostat had the largest correlation (absolute value) with a risk score. Four risk model genes (mRNAs), 12 miRNAs, and 21 lncRNAs formed an mRNA-miRNA-lncRNA network, containing HOXA10-hsa-miR-129-5p-LINC00689 and KIF18A-hsa-miR-221-3p-DANCR. Through bioinformatics, we constructed a prognostic model of m6A-associated anoikis genes in LGG, providing new ideas for research related to the prognosis and treatment of LGG.

Delineating the glioblastoma stemness by genes involved in cytoskeletal rearrangements and metabolic alterations

Literature data on glioblastoma ongoingly underline the link between metabolism and cancer stemness, the latter is one responsible for potentiating the resistance to treatment, inter alia due to increased invasiveness. In recent years, glioblastoma stemness research has bashfully introduced a key aspect of cytoskeletal rearrangements, whereas the impact of the cytoskeleton on invasiveness is well known. Although non-stem glioblastoma cells are less invasive than glioblastoma stem cells (GSCs), these cells also acquire stemness with greater ease if characterized as invasive cells and not tumor core cells. This suggests that glioblastoma stemness should be further investigated for any phenomena related to the cytoskeleton and metabolism, as they may provide new invasion-related insights. Previously, we proved that interplay between metabolism and cytoskeleton existed in glioblastoma. Despite searching for cytoskeleton-related processes in which the investigated genes might have been involved, not only did we stumble across the relation to metabolism but also reported genes that were found to be implicated in stemness. Thus, dedicated research on these genes in GSCs seems justifiable and might reveal novel directions and/or biomarkers that could be utilized in the future. Herein, we review the previously identified cytoskeleton/metabolism-related genes through the prism of glioblastoma stemness.

KIF4A promotes genomic stability and progression of endometrial cancer through regulation of TPX2 protein degradation

Kinesin family member 4A (KIF4A) belongs to the kinesin superfamily proteins, which are closely associated with mitophagy. Nonetheless, the role of KIF4A in endometrial cancer (EC) remains poorly characterized. The present study showed that KIF4A not only was upregulated but also predicted poor prognosis in patients with EC. KIF4A knockdown in EC cells resulted in attenuated proliferative capacity in vitro and in vivo. Transcriptome sequencing and gene function analysis revealed that KIF4A contributed to the maintenance of EC cells' genomic stability and that KIF4A knockdown induced the DNA damage response, cell cycle arrest, and apoptosis. Mechanistically, KIF4A interacted with TPX2 (a protein involved in DNA damage repair to cope with the replication pressure) to enhance its stability via inhibition of TPX2 ubiquitination and eventually ensured the genomic stability of EC cells during mitosis. Taken together, our results indicated that KIF4A functions as a tumor oncogene that facilitates EC progression via the maintenance of genomic stability. Therefore, targeting the KIF4A/TPX2 axis may provide new concepts and strategies for the treatment of patients with EC.

Identification of a Novel Gene Signature Based on Kinesin Family Members to Predict Prognosis in Glioma

Background and Objectives: Extensive research indicates that the kinesin superfamily (KIFs) regulates tumor progression. Nonetheless, the potential prognostic and therapeutic role of KIFs in glioma has been limited. Materials and Methods: Four independent cohorts from The Cancer Genome Atlas (TCGA) database and the Chinese Glioma Genome Atlas (CGGA) database were generated into a large combination cohort for identification of the prognostic signature. Following that, systematic analyses of multi-omics data were performed to determine the differences between the two groups. In addition, IDH1 was selected for the differential expression analysis. Results: The signature consists of five KIFs (KIF4A, KIF26A, KIF1A, KIF13A, and KIF13B) that were successfully identified. Receiver operating characteristic (ROC) curves indicated the signature had a suitable performance in prognosis prediction with the promising predictive area under the ROC curve (AUC) values. We then explored the genomic features differences, including immune features and tumor mutation status between high- and low-risk groups, from which we found that patients in the high-risk group had a higher level of immune checkpoint modules, and IDH1 was identified mutated more frequently in the low-risk group. Results of gene set enrichment analysis (GSEA) analysis showed that the E2F target, mitotic spindle, EMT, G2M checkpoint, and TNFa signaling were significantly activated in high-risk patients, partially explaining the differential prognosis between the two groups. Moreover, we also verified the five signature genes in the Human Protein Atlas (HPA) database. Conclusion: According to this study, we were able to classify glioma patients based on KIFs in a novel way. More importantly, the discovered KIFs-based signature and related characteristics may serve as a candidate for stratification indicators in the future for gliomas.

Follicular Thyroid Adenoma and Follicular Thyroid Carcinoma-A Common or Distinct Background? Loss of Heterozygosity in Comprehensive Microarray Study

Pre- and postsurgical differentiation between follicular thyroid adenoma (FTA) and follicular thyroid cancer (FTC) represents a significant diagnostic challenge. Furthermore, it remains unclear whether they share a common or distinct background and what the mechanisms underlying follicular thyroid lesions malignancy are. The study aimed to compare FTA and FTC by the comprehensive microarray and to identify recurrent regions of loss of heterozygosity (LOH). We analyzed formalin-fixed paraffin-embedded (FFPE) samples acquired from 32 Caucasian patients diagnosed with FTA (16) and FTC (16). We used the OncoScan™ microarray assay (Affymetrix, USA), using highly multiplexed molecular inversion probes for single nucleotide polymorphism (SNP). The total number of LOH was higher in FTC compared with FTA (18 vs. 15). The most common LOH present in 21 cases, in both FTA (10 cases) and FTC (11 cases), was 16p12.1, which encompasses many cancer-related genes, such as TP53, and was followed by 3p21.31. The only LOH present exclusively in FTA patients (56% vs. 0%) was 11p11.2-p11.12. The alteration which tended to be detected more often in FTC (6 vs. 1 in FTA) was 12q24.11-q24.13 overlapping FOXN4, MYL2, PTPN11 genes. FTA and FTC may share a common genetic background, even though differentiating rearrangements may also be detected.

Screening for Biomarkers for Progression from Oral Leukoplakia to Oral Squamous Cell Carcinoma and Evaluation of Diagnostic Efficacy by Multiple Machine Learning Algorithms

The aim of the study is to identify key genes during the progression from oral leukoplakia (OL) to oral squamous cell carcinoma (OSCC) and predict effective diagnoses. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to identify seven genes associated with the progression from OL to OSCC. Twelve machine learning algorithms including k-nearest neighbor (KNN), neural network (NNet), and extreme gradient boosting (XGBoost) were used to construct multi-gene models, which revealed that each model had good diagnostic efficacy. The functional mechanism or the pathways associated with these genes were evaluated using enrichment analysis, subtype clustering, and immune infiltration analysis. The enrichment analysis revealed that the genes enriched were associated with the cell cycle, cell division, and intracellular energy metabolism. The immunoassay results revealed that the genes primarily affected the infiltration of proliferating T cells and macrophage polarization. Finally, a nomogram and Kaplan-Meier survival analysis were used to predict the prognostic efficacy of key genes in OSCC patients. The results showed that genes could predict the prognosis of the patients, and patients in the high-risk group had a poor prognosis. Our study identified that the seven key genes, including DHX9, BCL2L12, RAD51, MELK, CDC6, ANLN, and KIF4A, were associated with the progression from OL to OSCC. These genes had good diagnostic efficacy and could be used as potential biomarkers for the prognosis of OSCC patients.

Sp1 induced gene TIMP1 is related to immune cell infiltration in glioblastoma

Tumor immune microenvironment exerts a profound effect on the population of infiltrating immune cells. Tissue inhibitor of matrix metalloproteinase 1 (TIMP1) is frequently overexpressed in a variety of cells, particularly during inflammation and tissue injury. However, its function in cancer and immunity remains enigmatic. In this study, we find that TIMP1 is substantially up-regulated during tumorigenesis through analyzing cancer bioinformatics databases, which is further confirmed by IHC tissue microarrays of clinical samples. The TIMP1 level is significantly increased in lymphocytes infiltrating the tumors and correlated with cancer progression, particularly in GBM. Notably, we find that the transcriptional factor Sp1 binds to the promoter of TIMP1 and triggers its expression in GBM. Together, our findings suggest that the Sp1-TIMP1 axis can be a potent biomarker for evaluating immune cell infiltration at the tumor sites and therefore, the malignant progression of GBM.

Prognostic Biomarker KIF18A and Its Correlations With Immune Infiltrates and Mitosis in Glioma

Background: Glioma is globally recognised as one of the most frequently occurring primary malignant brain tumours, making the identification of glioma biomarkers critically significant. The protein KIF18A (Kinesin Family Member 18A) is a member of the kinesin superfamily of microtubule-associated molecular motors and has been shown to participate in cell cycle and mitotic metaphase and anaphase. This is the first investigation into the expression of KIF18A and its prognostic value, potential biological functions, and effects on the immune system and mitosis in glioma patients. Methods: Gene expression and clinicopathological analysis, enrichment analysis, and immune infiltration analysis were based on data obtained from The Cancer Genome Atlas (TCGA), with additional bioinformatics analyses performed. Statistical analysis was conducted in R software. Clinical samples were used to evaluate the expression of KIF18A via immunohistochemical staining. In addition, the expression level of KIF18A was validated on U87 cell line. Results: Our results highlighted that KIF18A plays a key role as an independent prognostic factor in patients with glioma. KIF18A was highly expressed in glioma tissues, and KIF18A expression was associated with age, World Health Organization grade, isocitrate dehydrogenase (IDH) status, 1p/19q codeletion, primary therapy outcome, and overall survival (OS). Enrichment analysis revealed that KIF18A is closely correlated with the cell cycle and mitosis. Single sample gene set enrichment analysis (ssGSEA) analysis revealed that KIF18A expression was related to the immune microenvironment. The increased expression of KIF18A in glioma was verified in clinical samples and U87 cell line. Conclusion: The identification of KIF18A as a new biomarker for glioma could help elucidate how changes in the glioma cell and immune microenvironment promote glioma malignancy. With further analysis, KIF18A may serve as an independent prognostic indicator for human glioma.

GBP2 facilitates the progression of glioma via regulation of KIF22/EGFR signaling

Identifying the mechanism of glioma progression is critical for diagnosis and treatment. Although studies have shown that guanylate-binding protein 2(GBP2) has critical roles in various cancers, its function in glioma is unclear. In this work, we demonstrate that GBP2 has high expression levels in glioma tissues. In glioma cells, depletion of GBP2 impairs proliferation and migration, whereas overexpression of GBP2 enhances proliferation and migration. Regarding the mechanism, we clarify that epidermal growth factor receptor (EGFR) signaling is regulated by GBP2, and also demonstrate that GBP2 interacts directly with kinesin family member 22(KIF22) and regulates glioma progression through KIF22/EGFR signaling in vitro and in vivo. Therefore, our study provides new insight into glioma progression and paves the way for advances in glioma treatment.

Coordination of two kinesin superfamily motor proteins, KIF3A and KIF13A, is essential for pericellular matrix degradation by membrane-type 1 matrix metalloproteinase (MT1-MMP) in cancer cells

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MT1-MMP promotes cancer invasion by degrading barrier ECM at the leading edge, and its localization is carried out by direct vesicle transport of MT1-MMP containing vesicles along the microtubule.

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We identified KIF3A, KIF13A, and KIF9 as kinesins involved in MT1-MMP-containing vesicle trafficking in HT1080 cells.

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KIF3A and KIF13A transport MT1-MMP-containing vesicles from the trans-Golgi to the endosomes. KIF13A alone then transports the vesicles from endosomes to the plasma membrane for extracellular matrix degradation.

MT1-MMP plays a crucial role in promoting the cellular invasion of cancer cells by degrading the extracellular matrix to create a path for migration. During this process, its localization at the leading edge of migrating cells is critical, and it is achieved by targeted transport of MT1-MMP-containing vesicles along microtubules by kinesin superfamily motor proteins (KIFs). Here we identified three KIFs involved in MT1-MMP vesicle transport: KIF3A, KIF13A, and KIF9. Knockdown of KIF3A and KIF13A effectively inhibited MT1-MMP-dependent collagen degradation and invasion, while knockdown of KIF9 increased collagen degradation and invasion. Our data suggest that KIF3A/KIF13A dependent MT1-MMP vesicles transport takes over upon KIF9 knockdown. Live-cell imaging analyses have indicated that KIF3A and KIF13A coordinate to transport the same MT1-MMP-containing vesicles from the trans-Golgi to the endosomes, and KIF13A alone transports the vesicle from the endosome to the plasma membrane. Taken together, we have identified a unique interplay between three KIFs to regulate leading edge localization of MT1-MMP and MT1-MMP-dependent cancer cell invasion.

High STK40 Expression as an Independent Prognostic Biomarker and Correlated with Immune Infiltrates in Low-Grade Gliomas

Background

Expression of STK40 is observed in some cancer types, while its role in low-grade gliomas (LGG) is unclear. The present study aimed to demonstrate the relationship between STK40 and LGG based on The Cancer Genome Atlas (TCGA) database and bioinformatics analysis.

Methods

Kruskal-Wallis test, Wilcoxon sign-rank test, and logistic regression were used to evaluate the relationship between clinicopathological features and STK40 expression. Kaplan-Meier method and Cox regression analysis were used to evaluate prognostic factors. Gene set enrichment analysis (GSEA) and immuno-infiltration analysis were used to determine the significant involvement of STK40 in function.

Results

High STK40 expression in LGG was associated with WHO grade (P<0.001), IDH status (P<0.001), primary therapy outcome (P=0.027), 1p/19q codeletion (P<0.001) and histological type (P<0.001). High STK40 expression predicted a poorer overall survival (OS) (HR: 3.07; 95% CI: 2.09-4.51; P<0.001), progression-free survival (PFS) (HR:2.11; 95% CI: 1.59-2.81; P<0.001) and disease specific survival (DSS) (HR: 3.27; 95% CI: 2.17-4.92; P<0.001). STK40 expression (HR: 2.284; 95% CI: 1.125-4.637; P=0.022) was independently correlated with OS in LGG patients. GSEA demonstrated that pathways including cell cycle mitotic, neutrophil degranulation, signaling by Rho GTPases, signaling by interleukins, M phase, PI3K-Akt signaling pathway and naba secreted factors were differentially enriched in STK40 high expression phenotype. Immune infiltration analysis showed that STK40 expression was correlated with some types of immune infiltrating cells.

Conclusion

STK40 expression was significantly correlated with poor survival and immune infiltration in LGG, and it may be a promising prognostic biomarker in LGG.

KIF18B as a regulator in tumor microenvironment accelerates tumor progression and triggers poor outcome in hepatocellular carcinoma

BACKGROUND

The tumor microenvironment plays an important role in the progression and recurrence of tumors and immunotherapy outcomes. The use of immune checkpoint blockers to improve the overall survival rate of patients with advanced hepatocellular carcinoma has yielded inconsistent outcomes. We examined the tumor microenvironment-related genes for their clinical significance and biological functions in hepatocellular carcinoma.

METHODS

Bioinformatic analysis was performed to screen the differentially expressed genes and to identify the core gene of the tumor microenvironment in hepatocellular carcinoma. The expression of KIF18B in hepatocellular carcinoma cell lines and tumor samples was determined using western blotting, quantitative real-time polymerase chain reaction, and immunohistochemistry. The malignancy-promoting ability of KIF18B was evaluated using Cell Counting Kit-8, colony formation, cell proliferation, migration and invasion, and xenograft tumor assays.

RESULTS

KIF18B was identified as one of the core genes in the hepatocellular carcinoma microenvironment and was significantly associated with infiltrating immune cell subtypes and tumor cell stemness. Upregulation of KIF18B was associated with poor clinicopathological characteristics and poor patient outcomes; its downregulation inhibited the proliferation ability of hepatocellular carcinoma cells, which was consistent with the findings of in vivo experiments. Knockdown of KIF18B inhibited epithelial-mesenchymal transition which reduced the migration and invasion abilities of tumor cells. A pulmonary metastasis model confirmed that the downregulation of KIF18B inhibited hepatocellular carcinoma cell metastasis in vivo.

CONCLUSION

KIF18B could be a useful marker for determining the treatment outcomes of immune checkpoint blockers in the context of hepatocellular carcinoma.

KIF14 and KIF23 Promote Cell Proliferation and Chemoresistance in HCC Cells, and Predict Worse Prognosis of Patients with HCC

Background

Hepatocellular carcinoma (HCC) is one of the most common human malignant tumors. The prognosis of HCC patients is still unsatisfying. In this study, we performed the integrated bioinformatics analysis to identify potential biomarkers and biological pathways in HCC.

Methods

Gene expression profiles were obtained from the Gene Expression Omnibus database (GSE55048, GSE55758, and GSE56545) for the screening of the common differentially expressed genes (DEGs) between HCC tissues and matched non-tumor tissues. DEGs were subjected to Gene Ontology, KEGG pathway, and Reactome pathway analysis. The hub genes were identified by using protein–protein interaction (PPI) network analysis. The hub genes in HCC were further subjected to overall survival analysis of HCC patients. The hub genes were further validated by in vitro functional assays.

Results

A total of 544 common differentially expressed genes were screened from three datasets. Gene Ontology, KEGG and Reactome analysis results showed that DEGs are significantly associated with the biological process of cell cycle, cell division, and DNA replication. PPI network analysis identified 20 hub genes from the DEGs. These hub genes except CENPE were all significantly up-regulated in the HCC tissues when compared to non-tumor tissues. The Kaplan–Meier survival analysis results showed that the high expression of the 20 hub genes was associated with shorter survival of the HCC patients. Further validation studies showed that knockdown of KIF14 and KIF23 both suppressed the proliferative potential, increased the caspase-3/-7 activity, up-regulated Bax expression, and promoted the invasive and migratory abilities in the HCC cells. In addition, knockdown of KIF14 and KIF23 enhanced chemosensitivity to cisplatin and sorafenib in the HCC cells. Finally, the high expression of KIF14 and KIF23 was associated with shorter progression-free survival, recurrence-free survival, and disease-specific survival of patients with HCC.

Conclusion

In conclusion, the present study performed the integrated bioinformatics analysis and showed that KIF14 and KIF23 silence attenuated cell proliferation, invasion, and migration, and promoted chemosensitivity of HCC cells. KIF14 and KIF23 may serve as potential biomarkers for predicting the worse prognosis of patients with HCC.

Dysregulated Expression and Methylation Analysis Identified TLX1NB as a Novel Recurrence Marker in Low-Grade Gliomas

Low-grade gliomas (LGGs) are the most common CNS tumors, and the main therapy for LGGs is complete surgical resection, due to its curative effect. However, LGG recurrence occurs frequently. Biomarkers play a crucial role in evaluating the recurrence and prognosis of LGGs. Numerous studies have focused on LGG prognosis. However, the multiomics research investigating the roles played by gene methylation and expression in LGG recurrence remains limited. In this study, we integrated the TCGA and GEO datasets, analyzing RNA and methylation data for recurrence (R) and nonrecurrence (NR) groups. We found a low expression of TLX1NB and high methylation in recurrence patients. Low expression of TLX1NB is associated with poor survival (OS: p = 0.04). The expression of TLX1NB is likely to play a role in the prognosis of LGG. Therefore, TLX1NB may represent an alternative early biomarker for the recurrence of low-grade gliomas.

Kinesin family members KIF2C/4A/10/11/14/18B/20A/23 predict poor prognosis and promote cell proliferation in hepatocellular carcinoma

Kinesin superfamily proteins (KIFs) comprise a family of molecular motors that transport membranous organelles and protein complexes in a microtubule- and ATP-dependent manner, with multiple roles in cancers. Little is known about the function of KIFs in hepatocellular carcinoma (HCC). Here, we investigate the roles of KIFs in the prognosis and progression of HCC. Upregulation of eight KIFs (KIF2C, KIF4A, KIF10, KIF11, KIF14, KIF18B, KIF20A, and KIF23) was found to be significantly associated with the tumor stage and pathological tumor grade of HCC patients. Additionally, a high expression of these eight KIFs was significantly associated with shorter overall survival (OS) and disease-free survival (DFS) in patients with HCC. Cox regression analysis showed the mRNA expression levels of these eight KIF members to be independent prognostic factors for worse outcomes in HCC. Moreover, a risk score model based on the mRNA levels of the eight KIF members effectively predicted the OS rate of patients with HCC. Additional experiments revealed that downregulation of each of the eight KIFs effectively decreased the proliferation and increased the G1 arrest of liver cancer cells in vitro. Taken together, these results indicate that KIF2C/4A/10/11/14/18B/20A/23 may serve as prognostic biomarkers for survival and potential therapeutic targets in HCC patients.

Systematically Dissecting the Function of RNA-Binding Proteins During Glioma Progression

RNA-binding proteins (RBPs) play important roles in regulating gene expression and dysregulation of RBPs have been observed in various types of cancer. However, the role of RBPs during glioma progression, and particular in Chinese patients, is only starting to be unveiled. Here, we systematically analyzed the somatic mutation, gene expression patterns of 2949 RBPs during glioma progression. Our comprehensive study reveals several of highly mutated genes (such as ATRX, TTN and SETD2) and differentially expressed genes (such as KIF4A, TTK and CEP55). Integration of the expression of RBPs and genes, we constructed a regulatory network in glioma and revealed the functional links between RBPs and cancer-related genes. Moreover, we identified the prognosis spectrum of RBPs during glioma progression. The expression of a number of RBPs, such as SNRPN and IGF2BP3, are significantly associated with overall survival of patients in all grades. Taken together, our analyses provided a valuable RBP resource during glioma progression, and revealed several candidates that potentially contribute to development of therapeutic targets for glioma.

Kinesin family member 18B: A contributor and facilitator in the proliferation and metastasis of cutaneous melanoma

Melanoma is the most aggressive type of cutaneous tumor and the occurrence of metastasis makes it resistant to almost all available treatment and becomes incorrigible. Hence, identifying metastasis-related biomarkers and effective therapeutic targets will assist in preventing metastasis and ameliorating cutaneous melanoma. In our present study, we reported kinesin family member 18B (KIF18B) as a novel contributor in cutaneous melanoma proliferation and metastasis, and it was found to be of great significance in predicting the prognosis of cutaneous melanoma patients. Bioinformatics analysis based on ONCOMINE, The Cancer Genome Atlas, and Genotype-Tissue Expression database revealed that KIF18B was highly expressed in cutaneous melanoma and remarkably correlated with unfavorable clinical outcomes. Consistently, the results of the quantitative real-time polymerase chain reaction exhibited that the expression of KIF18B was significantly higher in cutaneous melanoma cell lines than that in normal cells. In vitro, biological assays found that knockdown of KIF18B in cutaneous melanoma cells noticeably repressed cell proliferation, migration, and invasion, while inducing cell apoptosis. Moreover, the protein expression of E-cadherin was enhanced while the expression of N-cadherin, vimentin, and Snail was decreased in M14 cells after knocking down KIF18B. In addition, the phosphorylation of phosphoinositide 3-kinase (PI3K) and extracellular-signal-regulated kinase (ERK) was significantly suppressed in M14 cells with silenced KIF18B. Above all, our results indicated that the repression of cutaneous melanoma cell migration and proliferation caused by KIF18B depletion suggested an oncogenic role of KIF18B in cutaneous melanoma, which acts through modulating epithelial-mesenchymal transition and ERK/PI3K pathway.