Genome‑wide investigation of the clinical significance and prospective molecular mechanisms of kinesin family member genes in patients with lung adenocarcinoma

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.

KIF20B Correlates with LUAD Progression and Is an Independent Risk Factor

OBJECTIVE

Kinesin family proteins (KIFs) play crucial roles in human tumorigenesis and progression. This study aimed to investigate the expression and association of Kinesin family member 20B (KIF20B) with lung adenocarcinoma (LUAD).

METHODS

RNA-seq data from LUAD patients (n = 535) were extracted from TCGA. KIF20B expression was compared between tumor tissues and controls, and between different stages of the disease. Survival and Cox regression analyses were performed, as well as in vitro cellular experiments on A549 cells.

RESULTS

KIF20B is upregulated in LUAD tumor tissues compared with controls and is higher in advanced stages. Patients with high expression of KIF20B have shorter survival times. KIF20B is an independent risk factor for the prognosis of LUAD. High KIF20B expression samples were enriched in signaling pathways related to tumor progression. si-KIF20B transfection reduced migration and invasion of A549 cells and increased apoptosis. The expression of p53 and Bax proteins was upregulated by si-KIF20B, while Bcl-2 was down-regulated.

DISCUSSION

This study reveals that high KIF20B expression is an independent risk factor for the poor prognosis of LUAD. The inhibition of KIF20B might be of great value for suppressing LUAD progression.

Comprehensive analysis of FOXM1 immune infiltrates, m6a, glycolysis and ceRNA network in human hepatocellular carcinoma

Background

Forkhead box M1 (FOXM1) is a member of the Forkhead box (Fox) transcription factor family. It regulates cell mitosis, cell proliferation, and genome stability. However, the relationship between the expression of FOXM1 and the levels of m6a modification, immune infiltration, glycolysis, and ketone body metabolism in HCC has yet to be fully elucidated.

Methods

Transcriptome and somatic mutation profiles of HCC were downloaded from the TCGA database. Somatic mutations were analyzed by maftools R package and visualized in oncoplots. GO, KEGG and GSEA function enrichment was performed on FOXM1 co-expression using R. We used Cox regression and machine learning algorithms (CIBERSORT, LASSO, random forest, and SVM-RFE) to study the prognostic value of FOXM1 and immune infiltrating characteristic immune cells in HCC. The relationship between FOXM1 and m6A modification, glycolysis, and ketone body metabolism were analyzed by RNA-seq and CHIP-seq. The competing endogenous RNA (ceRNA) network construction relies on the multiMiR R package, ENCORI, and miRNET platforms.

Results

FOXM1 is highly expressed in HCC and is associated with a poorer prognosis. At the same time, the expression level of FOXM1 is significantly related to the T, N, and stage. Subsequently, based on the machine learning strategies, we found that the infiltration level of T follicular helper cells (Tfh) was a risk factor affecting the prognosis of HCC patients. The high infiltration of Tfh was significantly related to the poor overall survival rate of HCC. Besides, the CHIP-seq demonstrated that FOXM1 regulates m6a modification by binding to the promoter of IGF2BP3 and affects the glycolytic process by initiating the transcription of HK2 and PKM in HCC. A ceRNA network was successfully obtained, including FOXM1 - has-miR-125-5p - DANCR/MIR4435-2HG ceRNA network related to the prognosis of HCC.

Conclusion

Our study implicates that the aberrant infiltration of Tfh associated with FOXM1 is a crucial prognostic factor for HCC patients. FOXM1 regulates genes related to m6a modification and glycolysis at the transcriptional level. Furthermore, the specific ceRNA network can be used as a potential therapeutic target for HCC.

A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection

Great advances in deep learning have provided effective solutions for prediction tasks in the biomedical field. However, accurate prognosis prediction using cancer genomics data remains challenging due to the severe overfitting problem caused by curse of dimensionality inherent to high-throughput sequencing data. Moreover, there are unique challenges to perform survival analysis, arising from the difficulty in utilizing censored samples whose events of interest are not observed. Convolutional neural network (CNN) models provide us the opportunity to extract meaningful hierarchical features to characterize cancer subtype and prognosis outcomes. On the other hand, feature selection can mitigate overfitting and reduce subsequent model training computation burden by screening out significant genes from redundant genes. To accomplish model simplification, we developed a concise and efficient survival analysis model, named CNN-Cox model, which combines a special CNN framework with prognosis-related feature selection cascaded Wx, with the advantage of less computation demand utilizing light training parameters. Experiment results show that CNN-Cox model achieved consistent higher C-index values and better survival prediction performance across seven cancer type datasets in The Cancer Genome Atlas cohort, including bladder carcinoma, head and neck squamous cell carcinoma, kidney renal cell carcinoma, brain low-grade glioma, lung adenocarcinoma (LUAD), lung squamous cell carcinoma, and skin cutaneous melanoma, compared with the existing state-of-the-art survival analysis methods. As an illustration of model interpretation, we examined potential prognostic gene signatures of LUAD dataset using the proposed CNN-Cox model. We conducted protein-protein interaction network analysis to identify potential prognostic genes and further analyzed the biological function of 13 hub genes, including ANLN, RACGAP1, KIF4A, KIF20A, KIF14, ASPM, CDK1, SPC25, NCAPG, MKI67, HJURP, EXO1, HMMR, whose high expression is significantly associated with poor survival of LUAD patients. These findings confirmed that CNN-Cox model is effective in extracting not only prognosis factors but also biologically meaningful gene features. The codes are available at the GitHub website: https://github.com/wangwangCCChen/CNN-Cox .

Inflammation-Related Genes Serve as Prognostic Biomarkers and Involve in Immunosuppressive Microenvironment to Promote Gastric Cancer Progression

Gastric cancer (GC) is a typical inflammatory-related malignant tumor which is closely related to helicobacter pylori infection. Tumor inflammatory microenvironment plays a crucial role in tumor progression and affect the clinical benefit from immunotherapy. In recent years, immunotherapy for gastric cancer has achieved promising outcomes, but not all patients can benefit from immunotherapy due to tumor heterogeneity. In our study, we identified 29 differentially expressed and prognostic inflammation-related genes in GC and normal samples. Based on those genes, we constructed a prognostic model using a least absolute shrinkage and selection operator (LASSO) algorithm, which categorized patients with GC into two groups. The high-risk group have the characteristics of "cold tumor" and have a poorer prognosis. In contrast, low-risk group was "hot tumor" and had better prognosis. Targeting inflammatory-related genes and remodeling tumor microenvironment to turn "cold tumor" into "hot tumor" may be a promising solution to improve the efficacy of immunotherapy for patients with GC.

A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model

There have been few investigations of cancer prognosis models based on Bayesian hierarchical models. In this study, we used a novel Bayesian method to screen mRNAs and estimate the effects of mRNAs on the prognosis of patients with lung adenocarcinoma. Based on the identified mRNAs, we can build a prognostic model combining mRNAs and clinical features, allowing us to explore new molecules with the potential to predict the prognosis of lung adenocarcinoma. The mRNA data (n = 594) and clinical data (n = 470) for lung adenocarcinoma were obtained from the TCGA database. Gene set enrichment analysis (GSEA), univariate Cox proportional hazards regression, and the Bayesian hierarchical Cox proportional hazards model were used to explore the mRNAs related to the prognosis of lung adenocarcinoma. Multivariate Cox proportional hazard regression was used to identify independent markers. The prediction performance of the prognostic model was evaluated not only by the internal cross-validation but also by the external validation based on the GEO dataset (n = 437). With the Bayesian hierarchical Cox proportional hazards model, a 14-gene signature that included CPS1, CTPS2, DARS2, IGFBP3, MCM5, MCM7, NME4, NT5E, PLK1, POLR3G, PTTG1, SERPINB5, TXNRD1, and TYMS was established to predict overall survival in lung adenocarcinoma. Multivariate analysis demonstrated that the 14-gene signature (HR 3.960, 95% CI 2.710–5.786), T classification (T₁, reference; T₃, HR 1.925, 95% CI 1.104–3.355) and N classification (N₀, reference; N₁, HR 2.212, 95% CI 1.520–3.220; N₂, HR 2.260, 95% CI 1.499–3.409) were independent predictors. The C-index of the model was 0.733 and 0.735, respectively, after performing cross-validation and external validation, a nomogram was provided for better prediction in clinical application. Bayesian hierarchical Cox proportional hazards models can be used to integrate high-dimensional omics information into a prediction model for lung adenocarcinoma to improve the prognostic prediction and discover potential targets. This approach may be a powerful predictive tool for clinicians treating malignant tumours.

Identification of a novel snoRNA expression signature associated with overall survival in patients with lung adenocarcinoma: A comprehensive analysis based on RNA sequencing dataset

Since multiple studies have reported that small nucleolar RNAs (snoRNAs) can be serve as prognostic biomarkers for cancers, however, the prognostic values of snoRNAs in lung adenocarcinoma (LUAD) remain unclear. Therefore, the main work of this study is to identify the prognostic snoRNAs of LUAD and conduct a comprehensive analysis. The Cancer Genome Atlas LUAD cohort whole-genome RNA-sequencing dataset is included in this study, prognostic analysis and multiple bioinformatics approaches are used for comprehensive analysis and identification of prognostic snoRNAs. There were seven LUAD prognostic snoRNAs were screened in current study. We also constructed a novel expression signature containing five LUAD prognostic snoRNAs (snoU109, SNORA5A, SNORA70, SNORD104 and U3). Survival analysis of this expression signature reveals that LUAD patients with high risk score was significantly related to an unfavourable overall survival (adjusted P = 0.01, adjusted hazard ratio = 1.476, 95% confidence interval = 1.096-1.987). Functional analysis indicated that LUAD patients with different risk score phenotypes had significant differences in cell cycle, apoptosis, integrin, transforming growth factor beta, ErbB, nuclear factor kappa B, mitogen-activated protein kinase, phosphatidylinositol-3-kinase and toll like receptor signaling pathway. Immune microenvironment analysis also indicated that there were significant differences in immune microenvironment scores among LUAD patients with different risk score. In conclusion, this study identified an novel expression signature containing five LUAD prognostic snoRNAs, which may be serve as an independent prognostic indicator for LUAD patients.

Prognostic Nomogram Based on Circular RNA-Associated Competing Endogenous RNA Network for Patients with Lung Adenocarcinoma

Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, the function and application of circRNAs in lung adenocarcinoma (LUAD) are still unknown. In this study, we constructed a circRNA-associated competitive endogenous RNA (ceRNA) network to investigate the regulatory mechanism of LUAD procession and further constructed a prognostic signature to predict overall survival for LUAD patients. Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network. Based on the TargetScan prediction tool and Pearson correlation coefficient, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs, and 49 DEmRNAs. GO and KEGG enrichment indicated that the ceRNA network might be involved in the regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate Cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using the LASSO method in the TCGA-LUAD training cohort. By applying the signature, patients could be categorized into the high-risk or low-risk subgroups with significant survival differences (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009). The prognostic performance was confirmed in an independent GEO cohort (GSE42127, HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004). Multivariate Cox regression analysis proved that the three-gene signature was an independent prognostic factor. Combining the three-gene signature with clinical characters, a nomogram was constructed. The primary and external verification C-indexes were 0.717 and 0.716, respectively. The calibration curves for the probability of 3- and 5-year OS showed significant agreement between nomogram predictions and actual observations. Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and further constructed a useful prognostic signature to guide personalized treatment of LUAD patients.

The High Expression of PTPRH Is Associated with Poor Prognosis of Human Lung Adenocarcinoma

Objective

The aim of the study is to explore the prognosis value of PTPRH in patients with lung adenocarcinoma (LUAD).

Methods

Oncomine, UALCAN, and GEPIA databases were employed to examine the differential expression of PTPRH between LUAD and adjacent tissues. 100 pairs of LUAD and adjacent tissue samples were involved in this study. qRT-PCR and immunohistochemical staining were performed. Meanwhile, we analyzed The Cancer Genome Atlas (TCGA) data to investigate the correlation between PTPRH gene expression and clinicopathological characteristics. Kaplan-Meier analysis and univariate and multivariate Cox analyses were performed to estimate the relationship between PTPRH expression and LUAD prognosis. The evaluation performance was verified by drawing a ROC curve. In addition, through GSEA, the changes of PTPRH expression were analyzed by GSEA to screen out primarily affected signaling pathway.

Results

Oncomine, UALCAN, and GEPIA databases showed that the mRNA expression of PTPRH in LUAD tissues was significantly higher than that in adjacent tissues. qRT-PCR and immunohistochemical staining indicated the mRNA and protein levels of PTPRH in LUAD tissues were markedly upregulated. TCGA data showed that the expression of PTPRH was significantly correlated with T stage and disease stage. Kaplan-Meier analysis showed that the patients with high PTPRH expression had a poor prognosis. Univariate and multivariate Cox analyses exhibited that PTPRH expression could act as an independent prognostic factor for LUAD. The ROC curve showed that PTPRH combined with various clinicopathological features could effectively predict the prognosis of LUAD. Finally, GSEA indicated that changes in PTPRH expression level may affect p53, VEGF, Notch, and mTOR cancer-related signaling pathways.

Conclusion

Our results demonstrated that PTPRH was highly expressed in LUAD and may be closely correlated with the poor prognosis of LUAD patients.

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

Background

Clear cell renal cell carcinoma (ccRCC) is a common type of fatal malignancy in the urinary system. As the therapeutic strategies of ccRCC are severely limited at present, the prognosis of patients with metastatic carcinoma is usually not promising. Revealing the pathogenesis and identifying hub candidate genes for prognosis prediction and precise treatment are urgently needed in metastatic ccRCC.

Methods

In the present study, we conducted a series of bioinformatics studies with the gene expression profiles of ccRCC samples from Gene Expression Omnibus (GEO) and the cancer genome atlas (TCGA) database for identifying and validating the hub gene of metastatic ccRCC. We constructed a co-expression network, divided genes into co-expression modules, and identified ccRCC-related modules by weighted gene co-expression network analysis (WGCNA) with data from GEO. Then, we investigated the functions of genes in the ccRCC-related modules by enrichment analyses and built a sub-network accordingly. A hub candidate gene of the metastatic ccRCC was identified by maximal clique centrality (MCC) method. We validate the hub gene by differentially expressed gene analysis, overall survival analysis, and correlation analysis with clinical traits with the external dataset (TCGA). Finally, we explored the function of the hub gene by correlation analysis with targets of precise therapies and single-gene gene set enrichment analysis.

Results

We conducted WGCNA with the expression profiles of GSE73731 from GEO and divided all genes into 8 meaningful co-expression modules. One module is proved to be positively correlated with pathological stage and tumor grade of ccRCC. Genes in the ccRCC-related module were mainly enriched in functions of mitotic cell division and several proverbial tumor related signal pathways. We then identified KIF18B as a hub gene of the metastasis of ccRCC. Validating analyses in external dataset observed the up-regulation of KIF18B in ccRCC and its correlation with worse outcomes. Further analyses found that the expression of KIF18B is related to that of targets of precise therapies.

Conclusion

Our study proposed KIF18B as a hub candidate gene of ccRCC for the first time. Our conclusion may provide a brand-new clue for prognosis evaluating and precise treatment for ccRCC in the future.

Weighted Gene Coexpression Network Analysis of Features That Control Cancer Stem Cells Reveals Prognostic Biomarkers in Lung Adenocarcinoma

Purpose We aimed to identify new prognostic biomarkers of lung adenocarcinoma (LUAD) based on cancer stem cell theory.

Materials and Methods: RNA-seq and microarray data were obtained with clinical information downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Weighted gene coexpression network analysis (WGCNA) was applied to identify significant module and hub genes. The hub genes were validated via microarray data from GEO, and a prognostic signature with prognostic hub genes was constructed.

Results LUAD patients enrolled from TCGA had a higher mRNA expression-based stemness index (mRNAsi) in tumor tissue than in adjacent normal tissue. Some clinical features and prognoses were found to be highly correlated with mRNAsi. WGCNA found that the green module and blue module were the most significant modules related to mRNAsi; 50 key genes were identified in the green module and were enriched mostly in the cell cycle, chromosome segregation, chromosomal region and microtubule binding. Six hub genes were revealed through the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) plugin of Cytoscape software. Based on external verification with the GEO database, these six genes are not only expressed at different levels in LUAD and normal tissues but also associated with different clinical features. In addition, the construction of a prognostic signature with three hub genes showed high predictive value.

Conclusion mRNAsi-related biomarkers may suggest a new potential treatment strategy for LUAD.

Development and validation of a 4-gene combination for the prognostication in lung adenocarcinoma patients

Objective: To identify a multi-gene prognostic factor in patients with lung adenocarcinoma (LUAD).

Materials and methods Prognosis-related genes were screened in the TCGA-LUAD cohort. Then, patients in this cohort were randomly separated into training set and test set. Least absolute shrinkage and selection operator (LASSO) regression was performed to the penalized the Cox proportional hazards regression (CPH) model on the training set, and a prognostication combination based on the result of LASSO analysis was developed. By performing Kaplan-Meier curve analysis, univariate and multivariable CPH model on the overall survival (OS) as well as recurrence free survival (RFS), the prognostication performance of the multigene combination were evaluated. Moreover, we constructed a nomogram and performed decision curve analysis to evaluate the clinical application of the multigene combination.

Results We obtained 99 prognosis-related genes and screened out a 4-gene combination (including CIDEC, ZFP3, DKK1, and USP4) according to the LASSO analysis. The results of survival analyses suggested that patients in the 4-gene combination low-risk group had better OS and RFS than those in the 4-gene combination high-risk group. The 4-gene mentioned was demonstrated to be independent prognostic factor of patients with LUAD in the training set(OS, HR=11.962, P<0.001; RFS, HR=9.281, P<0.001) and test set (OS, HR=5.377, P=0.003; RFS, HR=2.949, P=0.104). More importantly, its prognosis performance was well in the validation set (OS, HR=0.955, P=0.002; RFS, HR=1.042, P<0.001).

Conclusions We introduced a 4-gene combination which could predict the survival of LUAD patients and might be an independent prognostic factor in LUAD.