Identification of KIF18B as a Hub Candidate Gene in the Metastasis of Clear Cell Renal Cell Carcinoma by Weighted Gene Co-expression Network Analysis

KIF18B drives the malignant progression of gliomas by activating the Notch pathway

Kinesin family member 18B (KIF18B) is expressed differently in multiple malignancies and contributes to tumorigenesis. However, the relevance of KIF18B in glioma remains undetermined. This work evaluated the level and clinical significance of KIF18B in glioma. The upregulation of KIF18B was frequently detected in glioma specimens, which was related to clinicopathological features and therapeutic outcomes. A decrease in KIF18B expression in glioma cells was found to suppress malignant proliferation and metastasis, while simultaneously enhancing the cells' sensitivity to chemotherapeutic agents. Bioinformatics analysis demonstrated a significant correlation between KIF18B and the Notch signaling pathway in glioma cells. Further experimental validation confirmed that silencing KIF18B effectively inhibited the activation of the Notch signaling pathway. Reactivation of the Notch signaling pathway remarkably reversed the cancer-suppressing effects of KIF18B knockdown. Moreover, the ability of KIF18B-silenced glioma cells to form xenografts in nude mice was markedly impaired, accompanied by the downregulation of the Notch signaling pathway. This work indicates that KIF18 is crucial for maintaining glioma progression and proposes its potential as a promising therapeutic target for glioma treatment.

KIF18B: an important role in signaling pathways and a potential resistant target in tumor development

KIF18B is a key member of the kinesin-8 family, involved in regulating various physiological processes such as microtubule length, spindle assembly, and chromosome alignment. This article briefly introduces the structure and physiological functions of KIF18B, examines its role in malignant tumors, and the associated carcinogenic signaling pathways such as PI3K/AKT, Wnt/β-catenin, and mTOR pathways. Research indicates that the upregulation of KIF18B enhances tumor malignancy and resistance to radiotherapy and chemotherapy. KIF18B could become a new target for anticancer drugs, offering significant potential for the treatment of malignant tumors and reducing chemotherapy resistance.

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

Objective

Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.

Methods

RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein-protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).

Results

As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.

Conclusion

The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.

Both simulation and sequencing data reveal coinfections with multiple SARS-CoV-2 variants in the COVID-19 pandemic

SARS-CoV-2 is a single-stranded RNA betacoronavirus with a high mutation rate. The rapidly emerging SARS-CoV-2 variants could increase transmissibility and diminish vaccine protection. However, whether coinfection with multiple SARS-CoV-2 variants exists remains controversial. This study collected 12,986 and 4,113 SARS-CoV-2 genomes from the GISAID database on May 11, 2020 (GISAID20May11), and Apr 1, 2021 (GISAID21Apr1), respectively. With single-nucleotide variant (SNV) and network clique analyses, we constructed single-nucleotide polymorphism (SNP) coexistence networks and discovered maximal SNP cliques of sizes 16 and 34 in the GISAID20May11 and GISAID21Apr1 datasets, respectively. Simulating the transmission routes and SNV accumulations, we discovered a linear relationship between the size of the maximal clique and the number of coinfected variants. We deduced that the COVID-19 cases in GISAID20May11 and GISAID21Apr1 were coinfections with 3.20 and 3.42 variants on average, respectively. Additionally, we performed Nanopore sequencing on 42 COVID-19 patients and discovered recurrent heterozygous SNPs in twenty of the patients, including loci 8,782 and 28,144, which were crucial for SARS-CoV-2 lineage divergence. In conclusion, our findings reported SARS-CoV-2 variants coinfection in COVID-19 patients and demonstrated the increasing number of coinfected variants.

Identification of Key Modules and Genes Associated with Major Depressive Disorder in Adolescents

Major depressive disorder (MDD) is a leading cause of disability worldwide. Adolescence is a crucial period for the occurrence and development of depression. There are essential distinctions between adolescent and adult depression patients, and the etiology of depressive disorder is unclear. The interactions of multiple genes in a co-expression network are likely to be involved in the physiopathology of MDD. In the present study, RNA-Seq data of mRNA were acquired from the peripheral blood of MDD in adolescents and healthy control (HC) subjects. Co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA) to investigate the relationships between the underlying modules and MDD in adolescents. In the combined MDD and HC groups, the dynamic tree cutting method was utilized to assign genes to modules through hierarchical clustering. Moreover, functional enrichment analysis was conducted on those co-expression genes from interested modules. The results showed that eight modules were constructed by WGCNA. The blue module was significantly associated with MDD after multiple comparison adjustment. Several Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with stress and inflammation were identified in this module, including histone methylation, apoptosis, NF-kappa β signaling pathway, and TNF signaling pathway. Five genes related to inflammation, immunity, and the nervous system were identified as hub genes: CNTNAP3, IL1RAP, MEGF9, UBE2W, and UBE2D1. All of these findings supported that MDD was associated with stress, inflammation, and immune responses, helping us to obtain a better understanding of the internal molecular mechanism and to explore biomarkers for the diagnosis or treatment of depression in adolescents.

Silencing of KIF18B restricts proliferation and invasion and enhances the chemosensitivity of breast cancer via modulating Akt/GSK-3β/β-catenin pathway

Kinesin family member 18B (KIF18B) is a new tumor-associated protein that contributes to the carcinogenesis of multiple malignancies. However, the detailed relevance of KIF18B in breast cancer has not been fully elucidated. This work aimed was to evaluate a possible relationship between KIF18B and breast cancer progression. Our findings show KIF18B is increased in breast cancer and demonstrate that high KIF18B level predicts a reduced survival rate. Cellular functional studies revealed that knockdown of KIF18B markedly reduces the proliferation, invasion, and epithelial-mesenchymal transition of breast cancer cells and enhances their chemosensitivity toward doxorubicin. Further studies showed that KIF18B modulates the level of phospho-Akt, phospho-glycogen synthase kinase-3β, and β-catenin. Notably, suppression of Akt abolished KIF18B-overexpression-induced increases in activation of Wnt/β-catenin pathway. In addition, re-expression of β-catenin reversed KIF18B-silencing-induced cancer-promoting effect. In vivo animal experiments elucidated that knockdown of KIF18B significantly weakened the tumorigenicity of breast cancer cells. Taken together, data of this study illustrate that KIF18B exerts a potential cancer-promoting function in breast cancer via enhancement of Wnt/β-catenin pathway through modulation of the Akt/GSK-3β axis.

IQGAP3 May Serve as a Promising Biomarker in Clear Cell Renal Cell Carcinoma

Aim

The study was designed to mine the expression and roles of IQGAP3 in clear cell renal cell carcinoma (ccRCC).

Methods

Expression profiles and clinical information were obtained from the Cancer Genome Atlas (TCGA) to estimate IQGAP3 expression in ccRCC, its relationship with patients’ clinicopathological variables and prognosis, and the potential biological mechanisms.

Results

IQGAP3 was highly expressed in ccRCC and indicated advanced clinical outcome and poor prognosis. IQGAP3 affected the progression of ccRCC through several cancer-related pathways. IQGAP3 might play a vital role in the ccRCC tumor microenvironment.

Conclusion

IQGAP3 could serve as a promising prognostic biomarker and therapeutic target.

Identification of Disease-Specific Hub Biomarkers and Immune Infiltration in Osteoarthritis and Rheumatoid Arthritis Synovial Tissues by Bioinformatics Analysis

Background

Osteoarthritis (OA) and rheumatoid arthritis (RA) are well-known cause of joint disability. Although they have shown the analogous clinical features involving chronic synovitis that progresses to cartilage and bone destruction, the pathogenesis that initiates and perpetuates synovial lesions between RA and OA remains elusive.

Objective

This study is aimed at identifying disease-specific hub genes, exploring immune cell infiltration, and elucidating the underlying mechanisms associated with RA and OA synovial lesion.

Methods

Gene expression profiles (GSE55235, GSE55457, GSE55584, and GSE12021) were selected from Gene Expression Omnibus for analysis. Differentially expressed genes (DEGs) were identified by the “LIMMA” package in Bioconductor. The DEGs were identified by Gene Ontology (GO) and KEGG pathway analysis. A protein-protein interaction network was constructed to identify candidate hub genes by using STRING and Cytoscape. Hub genes were identified by validating from GSE12021. Furthermore, we employed the CIBERSORT website to assess immune cell infiltration between OA and RA. Finally, we explored the correlation between the levels of hub genes and relative proportion of immune cells in OA and RA.

Results

We identified 68 DEGs which were mainly enriched in immune response and chemokine signaling pathway. Six hub genes with a cutoff of AUC > 0.80 by ROC analysis and relative expression of P < 0.05 were identified successfully. Compared with OA, the RA synovial tissues consisted of a higher proportion of 7 immune cells, whereas 4 immune cells were found in relatively lower proportion (P < 0.05). In addition, the levels of 6 hub genes were closely associated with relative proportion of 11 immune cells in OA and RA.

Conclusions

We used bioinformatics analysis to identify hub genes and explored immune cell infiltration of immune microenvironment in synovial tissues. Our results should offer insights into the underlying molecular mechanisms of synovial lesion and provide potential target for immune-based therapies of OA and RA.

Systemic Multi-Omics Analysis Reveals Amplified P4HA1 Gene Associated With Prognostic and Hypoxic Regulation in Breast Cancer

Breast cancer (BC) is a common malignant tumor in females around the world. While multimodality therapies exist, the mortality rate remains high. The hypoxic condition was one of the potent determinants in BC progression. The molecular mechanisms underpinning hypoxia and their association with BC can contribute to a better understanding of tailored therapies. In this study, two hypoxic induced BC transcriptomic cohorts (GSE27813 and GSE47533) were assessed from the GEO database. The P4HA1 gene was identified as a putative candidate and significantly regulated in hypoxic BC cells compared to normal BC cells at different time intervals (6 h, 9 h, 16 h, 32 h, and 48 h). In patients with Luminal (p < 1E-12), triple-negative subclasses (p = 1.35059E-10), Stage 1 (p = 8.8817E-16), lymph node N1 (p = 1.62436E-12), and in the 40–80 age group (p = 1.62447E-12), the expression of P4HA1 was closely associated with the clinical subtypes of BC. Furthermore, at the 10q22.1 chromosomal band, the P4HA1 gene displayed a high copy number elevation and was associated with a poor clinical regimen with overall survival, relapse-free survival, and distant metastases-free survival in BC patients. In addition, using BioGRID, the protein–protein interaction (PPI) network was built and the cellular metabolic processes, and hedgehog pathways are functionally enriched with GO and KEGG terms. This tentative result provides insight into the molecular function of the P4HA1 gene, which is likely to promote hypoxic-mediated carcinogenesis, which may favor early detection of BC and therapeutic stratification.

Identification of Key Pathways and Genes Related to the Development of Hair Follicle Cycle in Cashmere Goats

The development of hair follicle in cashmere goats shows significant periodic change, as with mice and humans. However, for cashmere goat with double-coat, the periodic change may be due to other regulatory molecules and signal pathways. To understand the mechanism of periodic development of hair follicle, we performed a weighted gene coexpression network analysis (WGCNA) to mine key genes and establish an interaction network by utilizing the NCBI public dataset. Ten coexpression modules, including 7689 protein-coding genes, were constructed by WGCNA, six of which are considered to be significantly related to the development of the hair follicle cycle. A functional enrichment analysis for each model showed that they are closely related to ECM- receptor interaction, focal adhesion, PI3K-Akt signaling pathway, estrogen signaling pathway, and so on. Combined with the analysis of differential expressed genes, 12 hub genes from coexpression modules were selected as candidate markers, i.e., COL1A1, C1QTNF6, COL1A2, AQP3, KRTAP3-1, KRTAP11-1, FA2H, NDUFS5, DERL2, MRPL14, ANTKMT and XAB2, which might be applied to improve cashmere production.