Knockdown of CENPF inhibits the progression of lung adenocarcinoma mediated by ERβ2/5 pathway

Molecular mechanisms underlying the regulation of tumour suppressor genes in lung cancer

Tumour suppressor genes play a cardinal role in the development of a large array of human cancers, including lung cancer, which is one of the most frequently diagnosed cancers worldwide. Therefore, extensive studies have been committed to deciphering the underlying mechanisms of alterations of tumour suppressor genes in governing tumourigenesis, as well as resistance to cancer therapies. In spite of the encouraging clinical outcomes demonstrated by lung cancer patients on initial treatment, the subsequent unresponsiveness to first-line treatments manifested by virtually all the patients is inherently a contentious issue. In light of the aforementioned concerns, this review compiles the current knowledge on the molecular mechanisms of some of the tumour suppressor genes implicated in lung cancer that are either frequently mutated and/or are located on the chromosomal arms having high LOH rates (1p, 3p, 9p, 10q, 13q, and 17p). Our study identifies specific genomic loci prone to LOH, revealing a recurrent pattern in lung cancer cases. These loci, including 3p14.2 (FHIT), 9p21.3 (p16INK4a), 10q23 (PTEN), 17p13 (TP53), exhibit a higher susceptibility to LOH due to environmental factors such as exposure to DNA-damaging agents (carcinogens in cigarette smoke) and genetic factors such as chromosomal instability, genetic mutations, DNA replication errors, and genetic predisposition. Furthermore, this review summarizes the current treatment landscape and advancements for lung cancers, including the challenges and endeavours to overcome it. This review envisages inspired researchers to embark on a journey of discovery to add to the list of what was known in hopes of prompting the development of effective therapeutic strategies for lung cancer.

Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients

Background

Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined.

Methods

We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD.

Results

Out of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion.

Conclusion

Our research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis, mirrors the tumor's local immune condition, and supports clinical decision-making in tumor therapies.

Role of CCNB1, CENPF, and neutrophils in lung cancer diagnosis and prognosis

This study aimed to investigate CCNB1, CENPF, and Neutrophils as diagnostic predictors of lung cancer and to explore their association with clinical prognosis. Clinical data were obtained for a total of 52 patients. In addition, we downloaded 555 lung cancer-related samples from the cancer genome atlas (TCGA) database. Differentially expressed genes were further screened. Immune cell infiltration and survival analysis were performed. Immunohistochemistry was used to confirm gene expression. Peripheral blood analysis showed that neutrophil percentages were significantly reduced in patients with lung cancer. The least absolute shrinkage and selection operator and multivariate regression analysis revealed that CCNB1 and CENPF were lung cancer risk factors. Both CCNB1 and CENPF are overexpressed in lung cancer. The clinical diagnostic model constructed using CCNB1, CENPF, and neutrophils had a C-index of 0.994. This model area under the curve (AUC) and internal validation C-index values were 0.994 and 0.993, respectively. The elevated expression of CCNB1 and CENPF showed that the survival rate of lung cancer patients was reduced. CCNB1 and CENPF expression was positively correlated with the clinical stage of lung cancer. Further studies confirmed that CCNB1 and CENPF are overexpressed in lung cancer tissues. The clinically constructed model with high accuracy based on CCNB1, CENPF, and neutrophils demonstrated that these are crucial indicators for lung cancer diagnosis. High expression of CCNB1 and CENPF indicates a poor prognosis in patients with lung cancer.

Long Non-Coding RNA Small Nucleolar RNA Host Gene 4 Induced by Transcription Factor SP1 Promoted the Progression of Nasopharyngeal Carcinoma Through Modulating microRNA-510-5p/Centromere Protein F Axis

Long non-coding RNAs (LncRNAs) are implicated with tumorigenesis and the development of nasopharyngeal carcinoma (NPC). Previous studies suggested that long non-coding RNA small nucleolar RNA host gene 4 (SNHG4) exerted oncogenic roles in various cancers. However, the function and molecular mechanism of SNHG4 in NPC have not been investigated. In our study, it was confirmed that the SNHG4 level was enriched in NPC tissues and cells. Functional assays indicated that SNHG4 depletion inhibited the proliferation and metastasis but promoted apoptosis of NPC cells. Furthermore, we identified miR-510-5p as a downstream gene of SNHG4 in NPC cells and SNHG4 upregulated CENPF expression by binding to miR-510-5p. Moreover, there was a positive (or negative) association between CENPF and SNHG4 (or miR-510-5p) expression in NPC. In addition, rescue experiments verified that CENPF overexpression or miR-510-5p silencing abrogated inhibitory effects on NPC tumorigenesis caused by SNHG4 deficiency. The study demonstrated that SNHG4 promoted NPC progression via miR-510-5p/CENPF axis, providing a novel potential therapeutic target for NPC treatments.

N6-methyladenosine modification of CENPF mRNA facilitates gastric cancer metastasis via regulating FAK nuclear export

BACKGROUND

N6-methyladenosine (m⁶ A) modification is the most common modification that occurs in eukaryotes. Although substantial effort has been made in the prevention and treatment of gastric cancer (GC) in recent years, the prognosis of GC patients remains unsatisfactory. The regulatory mechanism between m⁶ A modification and GC development needs to be elucidated. In this study, we examined m⁶ A modification and the downstream mechanism in GC.

METHODS

Dot blotting assays, The Cancer Genome Atlas analysis, and quantitative real-time PCR (qRT-PCR) were used to measure the m⁶ A levels in GC tissues. Methylated RNA-immunoprecipitation sequencing and RNA sequencing were performed to identify the targets of m⁶ A modification. Western blotting, Transwell, wound healing, and angiogenesis assays were conducted to examine the role of centromere protein F (CENPF) in GC in vitro. Xenograft, immunohistochemistry, and in vivo metastasis experiments were conducted to examine the role of CENPF in GC in vivo. Methylated RNA-immunoprecipitation-qPCR, RNA immunoprecipitation-qPCR and RNA pulldown assays were used to verify the m⁶ A modification sites of CENPF. Gain/loss-of-function and rescue experiments were conducted to determine the relationship between CENPF and the mitogen-activated protein kinase (MAPK) signaling pathway in GC cells. Coimmunoprecipitation, mass spectrometry, qRT-PCR, and immunofluorescence assays were performed to explore the proteins that interact with CENPF and elucidate the regulatory mechanisms between them.

RESULTS

CENPF was upregulated in GC and facilitated the metastasis of GC both in vitro and in vivo. Mechanistically, increased m⁶ A modification of CENPF was mediated by methyltransferase 3, and this modified molecule could be recognized by heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1), thereby promoting its mRNA stability. In addition, the metastatic phenotype of CENPF was dependent on the MAPK signaling pathway. Furthermore, CENPF could bind to FAK and promote its localization in the cytoplasm. Moreover, we discovered that high expression of CENPF was related to lymphatic invasion and overall survival in GC patients.

CONCLUSIONS

Our findings revealed that increased m⁶ A modification of CENPF facilitates the metastasis and angiogenesis of GC through the CENPF/FAK/MAPK and epithelial-mesenchymal transition axis. CENPF expression was correlated with the clinical features of GC patients; therefore, CENPF may serve as a prognostic marker of GC.

CircDLG1 promotes malignant development of non-small cell lung cancer through regulation of the miR-630/CENPF axis

BACKGROUND

Circular RNAs (circRNAs) have been reported to be crucial modulatory molecules in the etiology of non-small cell lung cancer (NSCLC). This study aimed to probe the precise role and mechanism of circRNA discs large MAGUK scaffold protein 1 (circDLG1) in the malignant progression of NSCLC.

METHODS

The abundances of circDLG1, miR-630, and centromere protein F (CENPF) mRNAs were gauged by quantitative real-time polymerase chain reaction (qRT-PCR). Cell proliferation was tested in 3‑(4, 5‑dimethylthiazol-2-yl)-2, 5‑diphenyltetrazolium bromide (MTT) assay and 5‑ethynyl-2'-deoxyuridine (EdU)-incorporation assay. Cell apoptosis was analyzed by flow cytometry. Cell migration and invasion were assessed by transwell assay. Western blot was exploited to examine the levels of all proteins. The interaction between miR-630 and circDLG1 or CENPF was verified by dual-luciferase reporter, RNA pull-down, and/or RNA immunoprecipitation assays. Tumor xenograft assay and immunohistochemistry (IHC) were executed for the role of circDLG1 in tumor growth in vivo.

RESULTS

CircDLG1 and CENPF were highly expressed in NSCLC, while miR-630 was downregulated. CircDLG1 silencing repressed proliferation, migration, and invasion, and expedited apoptosis of NSCLC cells in vitro. Mechanistically, circDLG1 deficiency modulated NSCLC cell malignant development through interacting with miR-630. Furthermore, CENPF was targeted by miR-630, and circDLG1 could positively control CENPF expression through acting as an miR-630 sponge. Furthermore, CENPF overexpression reversed the repressive impacts of circDLG1 inhibition in the malignant behaviors of NSCLC cells. Besides, circDLG1 interference hindered tumor growth in vivo.

CONCLUSION

CircDLG1 knockdown could impede NSCLC advancement through modulating the miR-630/CENPF axis, manifesting as a promising molecular target for NSCLC treatment.

Combined multiomics analysis reveals the mechanism of CENPF overexpression-mediated immune dysfunction in diffuse large B-cell lymphoma in vitro

Diffuse large B-cell lymphoma (DLBCL) is one of the most common aggressive B-cell lymphomas with significant heterogeneity. More than half of patients are cured, but 40%-45% still face relapse or develop drug resistance, and the mechanism is not yet known. In this study, Centrimeric protein F (CENPF) overexpression was found in several DLBCL patients with relapsed or refractory disease compared to patients with complete remission. Thus, the human DLBCL cell line SU-DHL-4 was chosen for this study, and CENPF was upregulated in that cell line by using an adenovirus in vitro. Mass spectrometry-based quantitative proteome analysis was first performed, and the results showed that the expression levels of various proteins were increased when CENPF was upregulated, and these proteins are mainly involved in cellular processes, biological regulation, immune system processes and transcriptional regulator activity. Bioinformatics data analysis revealed that the main enriched proteins, including UBE2A, UBE2C, UBE2S, TRIP12, HERC2, PIRH2, and PIAS, were involved in various ubiquitin-related kinase activities and ubiquitination processes. Thus, ubiquitinome analysis was further performed, and the results demonstrated that proteins in many immune-related cellular pathways, such as natural killer cell-mediated cytotoxicity, the T-cell receptor signaling pathway and the B-cell receptor signaling pathway, were significantly deubiquitinated after CENPF was upregulated in DLBCL cells. Furthermore, TIMER2.0 was also used to reveal the association between CENPF and immune infiltration in DLBCL. The results showed that CENPF expression was positively correlated with CD8⁺ T cells, NK cells and B lymphocytes in DLBCL samples but negatively correlated with regulatory T cells. Aberrant activation of CENPF may induce immune dysregulation in DLBCL cells by mediating protein deubiquitination in various immune signaling pathways, which leads to tumor escape of DLBCL, but further experimental validation is still needed.

Bioinformatics Analysis of mRNAs and miRNAs for Identifying Potential Biomarkers in Lung Adenosquamous Carcinoma

Background

Lung adenosquamous carcinoma (LASC) is a special type of lung cancer. LASC is a malignant tumor with strong aggressiveness and a poor prognosis. Previous studies have revealed that microRNAs (miRNAs) are widely involved in the development of tumors by targeting mRNA. This study is aimed at identifying the key mRNAs and miRNAs of LASC and constructing miRNA-mRNA networks for deeply comprehending the latent molecular mechanisms.

Methods

mRNA dataset (GSE51852) and miRNA dataset (GSE51853) were extracted and downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were picked out by the GEO2R web tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted in the DAVID database. The protein-protein interaction (PPI) network was performed and analyzed by using the STRING database and Cytoscape software, respectively. TransmiR v2.0 was applied to predict potential transcription factors of miRNAs. The target genes of DEMs were predicted in the miRWalk database.

Results

In comparison to normal tissues, a total of 1458 DEGs (511 upregulated and 947 downregulated) and 13 DEMs (5 upregulated and 8 downregulated) were screened out in LASC tissues. The PPI network of the DEGs displayed five key modules and seventeen hub genes. Six target genes of the DEMs were predicted, and five essential miRNA-mRNA regulatory pairs were established. Ensuingly, CENPF, one of the target genes, was also the hub genes of GSE51852, which was obtained from MCODE and cytoHubba and regulated by hsa-miR-205.

Conclusions

We constructed the miRNA-mRNA regulatory pairs, which are helpful to study the potential regulatory mechanisms and find out promising diagnosis biomarkers and therapeutic targets for LASC.

A Novel Prognostic Signature for Survival Prediction and Immune Implication Based on SARS-CoV-2–Related Genes in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is a common aggressive malignancy of the urinary system. COVID-19, a highly infectious and severe disease caused by SARS-CoV-2, has become a significant challenge for global public health. Cancer patients have been reported to be more vulnerable to SARS-CoV-2 infection and have a higher risk for serious complications than the general population. However, the correlation between KIRC and COVID-19 remains incompletely elucidated. In this study, we comprehensively investigated the expression and prognostic significance of 333 SARS-CoV-2 infection–related genes in KIRC using the TCGA dataset and identified 31 SARS-CoV-2–related differently expressed genes between KIRC and normal renal tissues. Based on these genes, we constructed and validated a 5-gene prognostic signature (including ACADM, CENPF, KDELC1, PLOD2, and TRMT1) to distinguish low- and high-risk KIRC patients of poor survival in TCGA and E-MTAB-1980 cohorts. Gene set enrichment analysis (GSEA) showed that some inflammatory/immune-related pathways were significantly enriched in the high-risk group. The ESTIMATE analysis indicated that patients in the high-risk group had higher stromal and immune cell scores, therefore lower tumor purity. Moreover, they presented higher proportions of macrophages M0, regulatory T cells (Tregs), and T follicular helper cells and higher expression of immune checkpoints CTLA-4, LAG-3, TIGIT, and PDCD1 than low-risk patients. Besides, we also developed a nomogram to expand clinical applicability, which exhibits excellent predictive accuracy for survival. In conclusion, we identified a novel prognostic signature and nomogram based on SARS-CoV-2–related genes as reliable prognostic predictors for KIRC patients and provided potential therapeutic targets for KIRC and COVID-19.

Identification of a Prognostic Model Based on 2-Gene Signature and Analysis of Corresponding Tumor Microenvironment in Alcohol-Related Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors with the poor prognosis. Nowadays, alcohol is becoming a leading risk factor of HCC in many countries. In our study, we obtained the DEGs in alcohol-related HCC through two databases (TCGA and GEO). Subsequently, we performed enrichment analyses (GO and KEGG), constructed the PPI network and screened the 53 hub genes by Cytoscape. Two genes (BUB1B and CENPF) from hub genes was screened by LASSO and Cox regression analyses to construct the prognostic model. Then, we found that the high risk group had the worse prognosis and verified the clinical value of the risk score in alcohol-related HCC. Finally, we analyzed the tumor microenvironment between high and low risk groups through CIBERSORT and ESTIMATE. In summary, we constructed the two-gene prognostic model that could predict the poor prognosis in patients with alcohol-related HCC.