A novel strategy of integrated microarray analysis identifies CENPA, CDK1 and CDC20 as a cluster of diagnostic biomarkers in lung adenocarcinoma

LncRNA RGMB-AS1 inhibits HMOX1 ubiquitination and NAA10 activation to induce ferroptosis in non-small cell lung cancer

Ferroptosis, an iron-dependent regulated cell death caused by excessive lipid peroxide accumulation, has emerged as a promising therapeutic target in various cancers, including non-small cell lung cancer (NSCLC). In this study, we identified the long non-coding RNA RGMB-AS1 as a key regulator of ferroptosis in NSCLC. Mechanistically, RGMB-AS1 interacted with heme oxygenase 1 (HMOX1) and prevented its ubiquitination by the E3 ligase TRC8, leading to increased HMOX1 stability and enhanced ferroptosis. Additionally, RGMB-AS1 bound to the 82-87 amino acid region of N-alpha-acetyltransferase 10 (NAA10), stimulating its acetyltransferase activity and promoting the conversion of acetyl-CoA to HMG-CoA, further contributing to ferroptosis. The RGMB-AS1-HMOX1 and RGMB-AS1-NAA10 axes synergistically inhibited NSCLC growth both in vitro and in vivo. Clinically, low RGMB-AS1 expression was associated with advanced tumor stage and poor overall survival in NSCLC patients. Furthermore, adeno-associated virus-mediated RGMB-AS1 overexpression significantly suppressed tumor growth in mouse xenograft models. Our findings uncover a novel lncRNA-mediated regulatory mechanism of ferroptosis and highlight the potential of RGMB-AS1 as a prognostic biomarker and therapeutic target in NSCLC.

Unraveling pancreatic ductal adenocarcinoma immune prognostic signature through a naive B cell gene set

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC), a leading cause of cancer mortality, has a complex pathogenesis involving various immune cells, including B cells and their subpopulations. Despite emerging research on the role of these cells within the tumor microenvironment (TME), the detailed molecular interactions with tumor-infiltrating immune cells (TIICs) are not fully understood.

METHODS

We applied CIBERSORT to quantify TIICs and naive B cells, which are prognostic for PDAC. Marker genes from scRNA-seq and modular genes from weighted gene co-expression network analysis (WGCNA) were integrated to identify naive B cell-related genes. A prognostic signature was constructed utilizing ten machine-learning algorithms, with validation in external cohorts. We further assessed the immune cell diversity, ESTIMATE scores, and immune checkpoint genes (ICGs) between patient groups stratified by risk to clarify the immune landscape in PDAC.

RESULTS

Our analysis identified 994 naive B cell-related genes across single-cell and bulk transcriptomes, with 247 linked to overall survival. We developed a 12-gene prognostic signature using Lasso and plsRcox algorithms, which was confirmed by 10-fold cross-validation and showed robust predictive power in training and real-world cohorts. Notably, we observed substantial differences in immune infiltration between patients with high and low risk.

CONCLUSION

Our study presents a robust prognostic signature that effectively maps the complex immune interactions in PDAC, emphasizing the critical function of naive B cells and suggesting new avenues for immunotherapeutic interventions. This signature has potential clinical applications in personalizing PDAC treatment, enhancing the understanding of immune dynamics, and guiding immunotherapy strategies.

The potential role of CDC20 in tumorigenesis, cancer progression and therapy: A narrative review

The cell division cycle 20 homologue (CDC20) is known to regulate the cell cycle. Many studies have suggested that dysregulation of CDC20 is associated with various pathological processes in malignant solid tumors, including tumorigenesis, progression, chemoradiotherapy resistance, and poor prognosis, providing a biomarker for cancer diagnosis and prognosis. Some researchers have demonstrated that CDC20 also regulates apoptosis, immune microenvironment, and tumor angiogenesis. In this review, we have systematically summarized the biological functions of CDC20 in solid cancers. Furthermore, we briefly synthesized multiple medicines that inhibited CDC20. We anticipate that CDC20 will be a promising and effective biomarker and therapeutic target for the treatment of human cancer.

The hypoxia-associated genes in immune infiltration and treatment options of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a common lung cancer with a poor prognosis under standard chemotherapy. Hypoxia is a crucial factor in the development of solid tumors, and hypoxia-related genes (HRGs) are closely associated with the proliferation of LUAD cells.

Methods

In this study, LUAD HRGs were screened, and bioinformatics analysis and experimental validation were conducted. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to gather LUAD RNA-seq data and accompanying clinical information. LUAD subtypes were identified by unsupervised cluster analysis, and immune infiltration analysis of subtypes was conducted by GSVA and ssGSEA. Cox regression and LASSO regression analyses were used to obtain prognosis-related HRGs. Prognostic analysis was used to evaluate HRGs. Differences in enrichment pathways and immunotherapy were observed between risk groups based on GSEA and the TIDE method. Finally, RT-PCR and in vitro experiments were used to confirm prognosis-related HRG expression in LUAD cells.

Results

Two hypoxia-associated subtypes of LUAD were distinguished, demonstrating significant differences in prognostic analysis and immunological characteristics between subtypes. A prognostic model based on six HRGs (HK1, PDK3, PFKL, SLC2A1, STC1, and XPNPEP1) was developed for LUAD. HK1, SLC2A1, STC1, and XPNPEP1 were found to be risk factors for LUAD. PDK3 and PFKL were protective factors in LUAD patients.

Conclusion

This study demonstrates the effect of hypoxia-associated genes on immune infiltration in LUAD and provides options for immunotherapy and therapeutic strategies in LUAD.

Key genes involved with prognosis were identified in lung adenocarcinoma by integrated bioinformatics analysis

Objective

By screening the core genes in lung adenocarcinoma (LUAD) with bioinformatics, our study evaluated its prognosis value and role in infiltration process of immune cells.

Methods

Using GEO database, we screened 5 gene chips, including GSE11072, GSE32863, GSE43458, GSE115002, and GSE116959. Then, we obtained the corresponding differentially expressed genes by analyzed 5 gene chips online by GEO2R (P < 0.05, |logFC| > 1). Then, through DAVID online platform, Cytoscape 3.6.1 software and PPI network analysis, the network was visualized and obtain the final core genes. Next, we plan to use the GEPIA, UALCAN, Kaplan-Meier plotter and Time 2.0 database for corresponding analysis. The GEPIA database was used to verify the expression of core genes in LUAD and normal lung tissues, and survival analysis was used to evaluate the value of core genes in the prognosis of LUAD patients. UALCAN was used to verify the expression of the LUAD core gene and promoter methylation status, and the predictive value of core genes was evaluated in LUAD patients by the Kaplan-Meier plotter online tool. Then, we used the Time 2.0 database to identify the relationship to immune infiltration in LUAD. Finally, we used the human protein atlas (HPA) database for online immunohistochemical analysis of the expressed proteins.

Results

The expression of CCNB2 and CDC20 in LUAD were higher than those in normal lung tissues, their increased expression was negatively correlated with the overall survival rate of LUAD, and they were involved in cell cycle signal transduction, oocyte meiosis signal transduction as well as the infiltration process of immune cells in LUAD. The expression proteins of CCNB2 and CDC20 were also different in lung cancer tissue and normal lung tissue. Therefore, CCNB2 and CDC20 were identified as the vital core genes.

Conclusion

CCNB2 and CDC20 are essential genes that may constitute prognostic biomarkers in LUAD, they also participate the immune infiltration process and protein expression process of LUAD, and might provides basis for clinical anti-tumor drug research.

PIN1 and CDK1 cooperatively govern pVHL stability and suppressive functions

The VHL protein (pVHL) functions as a tumor suppressor by regulating the degradation or activation of protein substrates such as HIF1α and Akt. In human cancers harboring wild-type VHL, the aberrant downregulation of pVHL is frequently detected and critically contributes to tumor progression. However, the underlying mechanism by which the stability of pVHL is deregulated in these cancers remains elusive. Here, we identify cyclin-dependent kinase 1 (CDK1) and peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1) as two previously uncharacterized regulators of pVHL in multiple types of human cancers harboring wild-type VHL including triple-negative breast cancer (TNBC). PIN1 and CDK1 cooperatively modulate the protein turnover of pVHL, thereby conferring tumor growth, chemotherapeutic resistance and metastasis both in vitro and in vivo. Mechanistically, CDK1 directly phosphorylates pVHL at Ser80, which primes the recognition of pVHL by PIN1. PIN1 then binds to phosphorylated pVHL and facilitates the recruitment of the E3 ligase WSB1, therefore targeting pVHL for ubiquitination and degradation. Furthermore, the genetic ablation or pharmacological inhibition of CDK1 by RO-3306 and PIN1 by all-trans retinoic acid (ATRA), the standard care for Acute Promyelocytic Leukemia could markedly suppress tumor growth, metastasis and sensitize cancer cells to chemotherapeutic drugs in a pVHL dependent manner. The histological analyses show that PIN1 and CDK1 are highly expressed in TNBC samples, which negatively correlate with the expression of pVHL. Taken together, our findings reveal the previous unrecognized tumor-promoting function of CDK1/PIN1 axis through destabilizing pVHL and provide the preclinical evidence that targeting CDK1/PIN1 is an appealing strategy in the treatment of multiple cancers with wild-type VHL.

Upregulation of CENPM is associated with poor clinical outcome and suppression of immune profile in clear cell renal cell carcinoma

BACKGROUND

The response of advanced clear cell renal cell carcinoma (ccRCC) to immunotherapy is still not durable, suggesting that the immune landscape of ccRCC still needs to be refined, especially as some molecules that have synergistic effects with immune checkpoint genes need to be explored.

METHODS

The expression levels of CENPM and its relationship with clinicopathological features were explored using the ccRCC dataset from TCGA and GEO databases. Quantitative polymerase chain reaction (qPCR) analysis was performed to validate the expression of CENPM in renal cancer cell lines. Kaplan-Meier analysis, COX regression analysis and Nomogram construction were used to systematically evaluate the prognostic potential of CENPM in ccRCC. Besides, single gene correlation analysis, protein-protein interaction (PPI) network, genetic ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) were used to predict the biological behaviour of CENPM and the possible signalling pathways involved. Finally, a comprehensive analysis of the crosstalk between CENPM and immune features in the tumor microenvironment was performed based on the ssGSEA algorithm, the tumor immune dysfunction and exclusion (TIDE) algorithm, the TIMER2.0 database and the TISIDB database.

RESULTS

CENPM was significantly upregulated in ccRCC tissues and renal cancer cell lines and was closely associated with poor clinicopathological features and prognosis. Pathway enrichment analysis revealed that CENPM may be involved in the regulation of the cell cycle in ccRCC and may have some crosstalk with the immune microenvironment in tumors. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. The ssGSEA algorithm, CIBERSOPT algorithm suggests that CENPM is associated with suppressor immune cells in ccRCC such as regulatory T cells. Furthermore, the TISIDB database provides evidence that not only CENPM is positively associated with immune checkpoint genes such as CTLA4, PDCD1, LAG3, TIGIT, but also chemokines and receptors (such as CCL5, CXCL13, CXCR3, CXCR5) may be responsible for the malignant phenotype of CENPM in ccRCC. Meanwhile, predictions based on the TIDE algorithm support that patients with high CENPM expression have a worse response to immunotherapy.

CONCLUSIONS

The upregulation of CENPM in ccRCC predicts a poor clinical outcome, and this malignant phenotype may be associated with its exacerbation of the immunosuppressive state in the tumor microenvironment.

Prognostic and immunological characteristics of CDK1 in lung adenocarcinoma: A systematic analysis

Background

Cyclin-dependent kinases (CDKs) play a key role in cell proliferation in lung adenocarcinoma (LUAD). Comprehensive analysis of CDKs to elucidate their clinical significance and interactions with the tumor immune microenvironment is needed.

Methods

RNA expression, somatic mutation, copy number variation, and single-cell RNA sequencing data were downloaded from public datasets. First, we comprehensively evaluated the expression profile and prognostic characteristics of 26 CDKs in LUAD, and CDK1 was selected as a candidate for further analysis. Then, a systematic analysis was performed to explore the relationships of CDK1 with clinical characteristics and tumor immune microenvironment factors in LUAD.

Results

CDK1 was markedly upregulated at both the mRNA and protein level in LUAD. Moreover, overexpression of CDK1 was related to poor clinical outcomes. CDK1 coexpressed genes were mainly involved in the cell cycle, the DNA repair process, and the p53 signaling pathway. In addition, CDK1 expression was found to be correlated with the expression of multiple immunomodulators and chemokines, which participate in activating and suppressing the immune microenvironment. CDK1 expression was also correlated with increased infiltration of numerous immune cells, including CD4+ T cells and M1 macrophages. Patients with high CDK1 expression tended to have a poor response to immunotherapy but were sensitive to multiple chemotherapies and targeted drugs. The MDK-NCL and SPP1-CD44 ligand-receptor pairs were markedly activated in the intercellular communication network. CDK1 was an independent prognostic factor for LUAD and improved the ability to predict overall survival when combined with tumor stage.

Conclusion

CDK1 plays an essential role in reshaping the tumor immune microenvironment and might be a prognostic and treatment biomarker in LUAD.

A review on the role of cyclin dependent kinases in cancers

The Cyclin-dependent kinase (CDK) class of serine/threonine kinases has crucial roles in the regulation of cell cycle transition and is mainly involved in the pathogenesis of cancers. The expression of CDKs is controlled by a complex regulatory network comprised of genetic and epigenetic mechanisms, which are dysregulated during the progression of cancer. The abnormal activation of CDKs results in uncontrolled cancer cell proliferation and the induction of cancer stem cell characteristics. The levels of CDKs can be utilized to predict the prognosis and treatment response of cancer patients, and further understanding of the function and underlying mechanisms of CDKs in human tumors would pave the way for future cancer therapies that effectively target CDKs. Defects in the regulation of cell cycle and mutations in the genes coding cell-cycle regulatory proteins lead to unrestrained proliferation of cells leading to formation of tumors. A number of treatment modalities have been designed to combat dysregulation of cell cycle through affecting expression or activity of CDKs. However, effective application of these methods in the clinical settings requires recognition of the role of CDKs in the progression of each type of cancer, their partners, their interactions with signaling pathways and the effects of suppression of these kinases on malignant features. Thus, we designed this literature search to summarize these findings at cellular level, as well as in vivo and clinical levels.

Phosphoproteomics of extracellular vesicles integrated with multiomics analysis reveals novel kinase networks for lung cancer

Phosphorylation regulates the functions of proteins and aberrant phosphorylation often leads to a variety of diseases, including cancers. Extracellular vesicles (EVs) are important messengers in the microenvironment and their proteome contributes to cancer genesis and metastasis, while the kinases that driving EVs proteins' phosphorylation are less known. Clinical tissue samples from 13 patients with non-small-cell lung cancer (NSCLC) were utilized to isolate cancer EVs and adjacent normal EVs. Through quantitative phosphoproteomics analysis, 2473 phosphorylation sites on 1567 proteins were successfully identified and quantified. Accordingly, 152 kinases were identified, and 25 of them were differentially expressed. Based on Tied Diffusion through Interacting Events (TieDIE) algorithm, we integrated genomic and transcriptomic data sets of NSCLC from TCGA with our phosphoproteome data set to construct signaling networks. Through database integration and multiomics enrichment analysis, a compact network of 234 nodes with 1599 edges was constructed, which consisted of 34 transcription factors, 33 kinases, 63 aberrant genes, and 172 linking proteins. Rarely studied phosphorylation sites were specifically enriched. Key phosphoproteins of network nodes were validated in patients' EVs, including MAPK6^S189 , IKBKE^S172 , SRC^Y530 , CDK7^S164 , and CDK1^T14 . These networks depict intrinsic signal-regulation derived from EVs' phosphoproteins, providing a comprehensive and pathway-based strategy for in-depth lung cancer research.

Establishing a Novel Gene Signature Related to Histone Modifications for Predicting Prognosis in Lung Adenocarcinoma

Background

Epigenetic modifications have been revealed to play an important role in tumorigenesis and tumor development. This study aims to analyze the role of histone modifications and the prognostic values of histone modifications in lung adenocarcinoma (LUAD). The promoters and enhancers of protein encoding genes (PCGs) were the regions of enriched histone modifications.

Methods

Expression profiles and clinical information of LUAD samples were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Histone modification data of LUAD cell lines were downloaded from Encyclopedia of DNA Elements (ENCODE) database. Limma R package was used to identify differentially expressed PCGs. To identify molecular subtypes, consensus clustering was conducted based on the expression of dysregulated PCGs with abnormal histone modifications. Univariate Cox regression analysis and stepwise Akaike information criterion (stepAIC) were utilized to establish a prognostic model.

Results

We identified a total of 699 epigenetic dysregulated genes with 122 of them significantly correlating with LUAD prognosis. We constructed three molecular subtypes (C1, C2, and C3) based on the 122 prognostic genes. C2 had the longest overall survival while C1 had the worst prognosis. In addition, three subtypes had differential immune infiltration and the response to immune checkpoint inhibitors. Moreover, we identified a risk model containing 5 epi-PCGs that had favorable performance to predict prognosis in different datasets.

Conclusions

This study further supported the critical histone modifications in LUAD development. Three subtypes may provide guidance for the immunotherapy of LUAD patients. Importantly, the prognostic model had great potential to predict LUAD prognosis.

CENP-A is a potential prognostic biomarker and correlated with immune infiltration levels in glioma patients

Background: Centromeric protein A (CENP-A), an essential protein involved in chromosomal segregation during cell division, is associated with several cancer types. However, its role in gliomas remains unclear. This study examined the clinical and prognostic significance of CENP-A in gliomas.

Methods: Data of patients with glioma were collected from the Cancer Genome Atlas. Logistic regression, the Kruskal–Wallis test, and the Wilcoxon signed-rank test were performed to assess the relationship between CENP-A expression and clinicopathological parameters. The Cox regression model and Kaplan–Meier curve were used to analyze the association between CENP-A and survival outcomes. A prognostic nomogram was constructed based on Cox multivariate analysis. Gene set enrichment analysis (GSEA) was conducted to identify key CENP-A-related pathways and biological processes.

Results:CENP-A was upregulated in glioma samples. Increased CENP-A levels were significantly associated with the world health organization (WHO) grade [Odds ratio (OR) = 49.88 (23.52–129.06) for grade 4 vs. grades 2 and 3], primary therapy outcome [OR = 2.44 (1.64–3.68) for progressive disease (PD) and stable disease (SD) vs. partial response (PR) and complete response (CR)], isocitrate dehydrogenase (IDH) status [OR = 13.76 (9.25–20.96) for wild-type vs. mutant], 1p/19q co-deletion [OR = 5.91 (3.95–9.06) for no codeletion vs. co-deletion], and age [OR = 4.02 (2.68–6.18) for > 60 vs. ≤ 60]. Elevated CENP-A expression was correlated with shorter overall survival in both univariate [hazard ratio (HR): 5.422; 95% confidence interval (CI): 4.044–7.271; p < 0.001] and multivariate analyses (HR: 1.967; 95% CI: 1.280–3.025; p < 0.002). GSEA showed enrichment of numerous cell cycle-and tumor-related pathways in the CENP-A high expression phenotype. The calibration plot and C-index indicated the favorable performance of our nomogram for prognostic prediction in patients with glioma.

Conclusion: We propose a role for CENP-A in glioma progression and its potential as a biomarker for glioma diagnosis and prognosis.

Bioinformatics analysis reveals three key genes and four survival genes associated with youth-onset NSCLC

Youth-onset non-small cell lung cancer (NSCLC) is a heterogeneous disease. It has a unique clinicopathology and special genetic background. In this study, three key genes, CDC20, CCNB2, and BUB1, have been identified in youth-onset NSCLC tumor tissues based on the TCGA and GEO cohorts. Functional enrichment analysis reveals that the “oocyte meiosis,” “cell cycle,” and the “P53 signaling pathway” are significantly enriched. Additionally, four survival genes, including AKAP12, CRIM1, FEN1, and SLC7A11, that affect the prognosis of youth-onset NSCLC patients are identified in this study. Finally, we construct a risk model to predict the overall survival of youth-onset NSCLC patients, the AUC of the risk model in 1, 3, and 5 years of overall survival is 0.808, 0.844, and 0.728. This study aims to provide a novel idea to explore the pathogenic genes of youth-onset NSCLC.

CENP-A Regulation and Cancer

In mammals, CENP-A, a histone H3 variant found in the centromeric chromatin, is critical for faithful chromosome segregation and genome integrity maintenance through cell divisions. Specifically, it has dual functions, enabling to define epigenetically the centromere position and providing the foundation for building up the kinetochore. Regulation of its dynamics of synthesis and deposition ensures to propagate proper centromeres on each chromosome across mitosis and meiosis. However, CENP-A overexpression is a feature identified in many cancers. Importantly, high levels of CENP-A lead to its mislocalization outside the centromere. Recent studies in mammals have begun to uncover how CENP-A overexpression can affect genome integrity, reprogram cell fate and impact 3D nuclear organization in cancer. Here, we summarize the mechanisms that orchestrate CENP-A regulation. Then we review how, beyond its centromeric function, CENP-A overexpression is linked to cancer state in mammalian cells, with a focus on the perturbations that ensue at the level of chromatin organization. Finally, we review the clinical interest for CENP-A in cancer treatment.

Loss of CHGA Protein as a Potential Biomarker for Colon Cancer Diagnosis: A Study on Biomarker Discovery by Machine Learning and Confirmation by Immunohistochemistry in Colorectal Cancer Tissue Microarrays

Simple Summary

The identification of effective novel biomarkers is emergently needed in colon cancer patients. In the present study, firstly we predicted that CHGA could be a biomarker for colon cancer based on the protein–protein interaction network of all the reported biomarkers that were collected from our colorectal cancer biomarker database (CBD). Then we verified our results using a diagnostic test in gene expression data and an immunohistochemistry test. The results of this study suggest that a loss of CHGA expression from the normal colon and adjacent mucosa to colon cancer may be used as a valuable biomarker for early diagnosis of colon cancer patients.

Abstract

Background. The incidence of colorectal cancers has been constantly increasing. Although the mortality has slightly decreased, it is far from satisfaction. Precise early diagnosis for colorectal cancer has been a great challenge in order to improve patient survival. Patients and Methods. We started with searching for protein biomarkers based on our colorectal cancer biomarker database (CBD), finding differential expressed genes (GEGs) and non-DEGs from RNA sequencing (RNA-seq) data, and further predicted new biomarkers of protein–protein interaction (PPI) networks by machine learning (ML) methods. The best-selected biomarker was further verified by a receiver operating characteristic (ROC) test from microarray and RNA-seq data, biological network, and functional analysis, and immunohistochemistry in the tissue arrays from 198 specimens. Results. There were twelve proteins (MYO5A, CHGA, MAPK13, VDAC1, CCNA2, YWHAZ, CDK5, GNB3, CAMK2G, MAPK10, SDC2, and ADCY5) which were predicted by ML as colon cancer candidate diagnosis biomarkers. These predicted biomarkers showed close relationships with reported biomarkers of the PPI network and shared some pathways. An ROC test showed the CHGA protein with the best diagnostic accuracy (AUC = 0.9 in microarray data and 0.995 in RNA-seq data) among these candidate protein biomarkers. Furthermore, immunohistochemistry examination on our colon cancer tissue microarray samples further confirmed our bioinformatical prediction, indicating that CHGA may be used as a potential biomarker for early diagnosis of colon cancer patients. Conclusions. CHGA could be a potential candidate biomarker for diagnosing earlier colon cancer in the patients.

Development of a 5-Gene Signature to Evaluate Lung Adenocarcinoma Prognosis Based on the Features of Cancer Stem Cells

Cancer stem cells (CSCs) can induce recurrence and chemotherapy resistance of lung adenocarcinoma (LUAD). Reliable markers identified based on CSC characteristic of LUAD may improve patients' chemotherapy response and prognosis. OCLR was used to calculate mRNA expression-based stemness index (mRNAsi) of LUAD patients' data in TCGA. Association analysis of mRNAsi was performed with clinical features, somatic mutation, and tumor immunity. A prognostic prediction model was established with LASSO Cox regression. Kaplan-Meier Plotter (KM-plotter) and time-dependent ROC were applied to assess signature performance. For LUAD, univariate and multivariate Cox analysis was performed to identify independent prognostic factors. LUAD tissues showed a noticeably higher mRNAsi in than nontumor tissues, and it showed significant differences in T, N, M, AJCC stages, and smoking history. The most frequently mutated gene was TP53, with a higher mRNAsi relating to more frequent mutation of TP53. The mRNAsi was significantly negatively correlated with immune score, stromal score, and ESTIMATE score in LUAD. The blue module was associated with mRNAsi. The 5-gene signature was confirmed as an independent indicator of LUAD prognosis that could promote personalized treatment of LUAD and accurately predict overall survival (OS) of LUAD patients.

Analysis of the miRNA-mRNA Regulatory Network Reveals the Biomarker Genes in the Progression of Myocardial Ischemic Reperfusion

Objective

Cardiac injury induced by myocardial ischemic reperfusion (MI/R) is still an intractable question in clinical, and it has been confirmed as a major reason for the development of cardiovascular disease. Bioinformatics analysis has been widely used for revealing the pathogenic mechanism of diseases. This study attempted to identify the biomarkers and reveal the regulation mechanism of MI/R injury via bioinformatics analysis.

Methods

The GSE67308 and GSE74951 were obtained from the GEO database. The datasets were analyzed with GEO2R tool, and the genes with |logFC| > 2 and p value <0.05 were identified as the differentially expressed genes (DEGs). The enrichment analysis of the DEGs was performed with the DAVID database and R language. Moreover, the protein-protein interaction (PPI) network of DEGs was performed with the STRING database and then visualized with Cytoscape.

Result

The results showed that 195 downregulated mRNAs and 240 downregulated mRNAs were found in GSE67308, and 11 miRNAs were found in GSE7495. 152 common genes were screened in DEGs of GSE67308 and the targets of 11 miRNAs in GSE7495. Moreover, the enrichment analysis showed that the common genes were related with inflammatory response, immune response, PI3K/AKT, NF-κB, and TNF pathways. Besides, mmu-miR-92a-3p and mmu-miR-27b-3p were identified as the hubs miRNAs, and TNF, IL1B, and IFG1 were screened as the key nodes.

Conclusion

This study established a miRNA-mRNA network for cardiac injury induced by MI/R and provided the evidence concerning the molecular mechanism of MI/R injury, which provided some reference for MI/R treatment.

Current state and future potential of AI in occupational respiratory medicine

PURPOSE OF REVIEW

The COVID-19 pandemic has accelerated the pace of technological development relating to pulmonary diseases. The advent of newer technologies, such as Artificial Intelligence (AI), continues to be adapted for diagnostic purposes. AI offers comparable precision to trained physicians under certain circumstances, as well as the unique ability to process the information characteristic of Big Data. With respect to individual susceptibilities/pre-existing diseases, AI seems poised to integrate such individualized information and contribute to a greater implementation of precision medicine.

RECENT FINDINGS

AI can match trained clinicians in specific applications, but AI has limitations that require clearly defined questions and a high quality of data. Data collected for this purpose is predicted to increase both in quality and volume, as technology concerned with personal health (FitBit, Apple Watch) proliferates. However, the role of AI with respect to physicians in a clinical setting is still being debated. AI generally aims to increase objectivity through its correlational methodology.

SUMMARY

AI continues to be a proliferative field of study. It has defined strengths and weaknesses which, if accounted for, has the potential to increase healthcare access as well as the quality of care delivered.

Comprehensive Analysis of the Glycolysis-Related Gene Prognostic Signature and Immune Infiltration in Endometrial Cancer

Glucose metabolic reprogramming and immune imbalance play important roles in the progression of cancers. The purpose of this study is to develop a glycolysis-related prognostic signature for endometrial cancer (EC) and analyze its relationship with immune function. The mRNA expression profiling of the glycolysis-related genes and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA). We identified a glycolysis-related gene prognostic signature for predicting the prognosis of EC by using The Least Absolute Shrinkage and Selection Operator (LASSO) regression and found the patients in the high-risk group had worse survival prognosis. Multivariate Cox regression analysis showed that the gene signature was an independent prognostic factor for EC. The ROC curve confirmed the accuracy of the prognostic signature (AUC = 0.730). Then, we constructed a nomogram to predict the 1–5 years survival rate of EC patients. The association between the gene signature and immune function was analyzed based on the “ESTIMATE” and “CIBERSORT” algorithm, which showed the immune and ESTIMATE scores of patients in the high-risk group were lower, while the low immune and ESTIMATE scores were associated with a worse prognosis of patients. The imbalance of immune cells was also found in the high-risk group. Further, the protein of CDK1, a gene in the signature, was found to be closely related to prognosis of EC and inhibition of CDK1 could inhibit migration and promote apoptosis of EC cells. This study reveals a link between glycolysis-related gene signature and immunity, and provides personalized therapeutic targets for EC.

A Lipid Metabolism-Based Seven-Gene Signature Correlates with the Clinical Outcome of Lung Adenocarcinoma

Background. Herein, we tried to develop a prognostic prediction model for patients with LUAD based on the expression profiles of lipid metabolism-related genes (LMRGs). Methods. Molecular subtypes were identified by non-negative matrix factorization (NMF) clustering. The overall survival (OS) predictive gene signature was developed and validated internally and externally based on online data sets. Time-dependent receiver operating characteristic (ROC) curve, Kaplan–Meier curve, nomogram, restricted mean survival time (EMST), and decision curve analysis (DCA) were used to assess the performance of the gene signature. Results. We identified three molecular subtypes in LUAD with distinct characteristics on immune cells infiltration and clinical outcomes. Moreover, we confirmed a seven-gene signature as an independent prognostic factor for patients with LUAD. Calibration and DCA analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the gene signature. In addition, the nomogram showed higher robustness and clinical usability compared with four previously reported prognostic gene signatures. Conclusions. Findings in the present study shed new light on the characteristics of lipid metabolism within LUAD, and the established seven-gene signature can be utilized as a new prognostic marker for predicting survival in patients with LUAD.

CENPA is one of the potential key genes associated with the proliferation and prognosis of ovarian cancer based on integrated bioinformatics analysis and regulated by MYBL2

Background

Ovarian cancer (OV) is a highly lethal disease, and the fifth leading cause of all cancer-related deaths in women. The study aimed to identify potential key genes associated with the proliferation and prognosis of OV.

Methods

Differentially expressed genes (DEGs) between ovarian cancer and normal tissues were screened by the robust rank aggregation (RRA) method. The expression of CENPA and MYBL2 were examined in SKOV3 and A2780 ovarian cancer cell lines and tumor tissues by qRT-PCR and western blot. Small RNA interference assays, plasmid overexpression assays and EdU assays were used to validate the proliferative effect of the MYBL2-CENPA axis in ovarian cancer cell lines. The ChIP assay was used to verify the direct regulation of MYBL2 on CENPA.

Results

133 up-regulated genes and 158 down-regulated genes were identified, and the up-regulated genes mainly enrichment in cell cycle. The three up-regulated gene with DNA separation (CENPA, CENPF and CEP55) might be tightly correlated with proliferation and prognosis of OV. Knockdown CENPA expression inhibited the proliferation of A2780 and SKOV3 cells After the knockout of MYBL2, the expression of CENPA significantly decreased. MYBL2 directly binds to the promoter region of CENPA.

Conclusions

The MYBL2-CENPA pathway plays an important role in the proliferation of ovarian cancer cells, suggesting that this pathway may be a potential target for the treatment of ovarian cancer.

Stemness-associated senescence genes as potential novel risk factors for papillary renal cell carcinoma

Background

Papillary renal cell carcinoma (PRCC) is the 2nd most common type of renal carcinoma; however, there is limited data about PRCC, and strategies for the diagnosis and treatment of PRCC need to be identified.

Methods

In this study, the stemness-associated senescence (SAS) phenotype of PRCC was obtained by a bioinformatics analysis. We acquired the gene expression profiles of patients with PRCC and calculated the PRCC messenger ribonucleic acid stemness index (mRNAsi). We then screened the SAS genes from the GenAge database. A least absolute shrinkage and selection operator-Cox regression was conducted to examine correlations between risk signatures and the abundance of the SAS genes in the PRCC samples. Functional enrichment analyses were then performed via molecular co-expression studies of mRNAsi, and the risk scores of PRCC patients were calculated.

Results

We identified the following 8 SAS signatures that were strongly associated with prognosis in PRCC patients: cyclin-dependent kinase 1, heat shock protein family D member 1, platelet-derived growth factor receptor A, cyclin-dependent kinase inhibitor 2B, pyrroline-5-carboxylate reductase 1, sequestosome-1, sirtuin-3, and cyclin-dependent kinase inhibitor 1A. The SAS signatures were significantly associated with the stage and type of PRCC. The calculated risk scores can be used to divide PRCC patients into low- and high-risk groups, and provide guidance in determining treatment plans.

Conclusions

We have developed a reliable prognostic tool to predict the clinical outcomes of PRCC patients. This tool could improve treatment decisions regarding drug therapy, surgery, and conservative options.

Bioinformatics prediction and experimental verification identify MAD2L1 and CCNB2 as diagnostic biomarkers of rhabdomyosarcoma

Background

Rhabdomyosarcoma (RMS) is a malignant soft-tissue tumour. In recent years, the tumour microenvironment (TME) has been reported to be associated with the development of tumours. However, the relationship between the occurrence and development of RMS and TME is unclear. The purpose of this study is to identify potential tumor microenvironment-related biomarkers in rhabdomyosarcoma and analyze their molecular mechanisms, diagnostic and prognostic significance.

Methods

We first applied bioinformatics method to analyse the tumour samples of 125 patients with rhabdomyosarcoma (RMS) from the Gene Expression Omnibus database (GEO). Differential genes (DEGs) that significantly correlate with TME and the clinical staging of tumors were extracted. Immunohistochemistry (IHC) was applied to validate the expression of mitotic arrest deficient 2 like 1 (MAD2L1) and cyclin B2 (CCNB2) in RMS tissue. Then, we used cell function and molecular biology techniques to study the influence of MAD2L1 and CCNB2 expression levels on the progression of RMS.

Results

Bioinformatics results show that the RMS TME key genes were screened, and a TME-related tumour clinical staging model was constructed. The top 10 hub genes were screened through the establishment of a protein–protein interaction (PPI) network, and then Gene Expression Profiling Interactive Analysis (GEPIA) was conducted to measure the overall survival (OS) of the 10 hub genes in the sarcoma cases in The Cancer Genome Atlas (TCGA). Six DEGs of statistical significance were acquired. The relationship between these six differential genes and the clinical stage of RMS was analysed. Further analysis revealed that the OS of RMS patients with high expression of MAD2L1 and CCNB2 was worse and the expression of MAD2L1 and CCNB2 was related to the clinical stage of RMS patients. Gene set enrichment analysis (GSEA) revealed that the genes in MAD2L1 and CCNB2 groups with high expression were mainly related to the mechanism of tumour metastasis and recurrence. In the low-expression MAD2L1 and CCNB2 groups, the genes were enriched in the metabolic and immune pathways. Immunohistochemical results also confirmed that the expression levels of MAD2L1 (30/33, 87.5%) and CCNB2 (33/33, 100%) were remarkably higher in RMS group than in normal control group (0/11, 0%). Moreover, the expression of CCNB2 was related to tumour size. Downregulation of MAD2L1 and CCNB2 suppressed the growth, invasion, migration, and cell cycling of RMS cells and promoted their apoptosis. The CIBERSORT immune cell fraction analysis indicated that the expression levels of MAD2L1 and CCNB2 affected the immune status in the TME.

Conclusions

The expression levels of MAD2L1 and CCNB2 are potential indicators of TME status changes in RMS, which may help guide the prognosis of patients with RMS and the clinical staging of tumours.

A Systematic Analysis Identifies Key Regulators Involved in Cell Proliferation and Potential Drugs for the Treatment of Human Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common and malignant cancer types. Abnormal cell proliferation, exemplified by cell cycle and cell division dysregulation, is one of the most prominent hallmarks of cancer and is responsible for recurrence, metastasis, and resistance to cancer therapy. However, LUAD-specific gene regulation and clinical significance remain obscure. Here, by using both tissues and cells from LUAD and normal lung samples, 434 increased and 828 decreased genes of biological significance were detected, including 127 cell cycle-associated genes (95 increased and 32 decreased), 66 cell division-associated genes (56 increased and 10 decreased), and 81 cell proliferation-associated genes (34 increased and 47 decreased). Among them, 12 increased genes (TPX2, CENPF, BUB1, PLK1, KIF2C, AURKB, CDKN3, BUB1B, HMGA2, CDK1, ASPM, and CKS1B) and 2 decreased genes (TACC1 and MYH10) were associated with all the three above processes. Importantly, 2 (CDKN3 and CKS1B) out of the 11 increased genes (except HMGA2) are previously uncharacterized ones in LUAD and can potentially be prognostic markers. Moreover, PLK1 could be a promising therapeutic target for LUAD. Besides, protein–protein interaction network analysis showed that CDK1 and CDC20 were the hub genes, which might play crucial roles in cell proliferation of LUAD. Furthermore, transcriptional regulatory network analysis suggested that the transcription factor E2F1 could be a key regulator in controlling cell proliferation of LUAD via expression modulation of most cell cycle-, cell division-, and cell proliferation-related DEGs. Finally, trichostatin A, hycanthone, vorinostat, and mebeverine were identified as four potential therapeutic agents for LUAD. This work revealed key regulators contributing to cell proliferation in human LUAD and identified four potential therapeutic agents for treatment strategy.

Microdissection of the Bulk Transcriptome at Single-Cell Resolution Reveals Clinical Significance and Myeloid Cells Heterogeneity in Lung Adenocarcinoma

Background

Tumor infiltrating myeloid (TIM) cells constitute a vital element of the tumor microenvironment. The cell-type heterogeneity of TIM has yet to be fully investigated.

Methods

We used a time saving approach to generate a single-cell reference matrix, allowing quantification of cell-type proportions and cell-type-specific gene abundances in bulk RNA-seq data.

Results

Two distinct clusters, MSC1 and MSC2 (MSC subtype) were newly identified in lung adenocarcinoma (LUAD) patients, both significantly associated with overall survival and immune blockade therapy responses. Twenty myeloid cell types were detected. Thirteen of these had distinct enrichment patterns between MSC1 and MSC2. LAMP3+ dendritic cells, being a mature and transportable subtype of dendritic cell that may migrate to lymph nodes, were noted as associated with non-responsiveness to immunotargeted therapy. High infiltration level of IFIT3+ neutrophils was strongly related to the response to immune-targeted therapy and was seen to activate CD8+ T cells, partly through inflammasome activation. The infiltration levels of TIMP1+ macrophages and S100A8+ neutrophils were both significantly associated with poor prognosis. TIMP1+ macrophages were noted to recruit S100A8+ neutrophils via the CXCL5-CXCR2 axes and promote LUAD progression.

Conclusion

Altogether, we performed virtual microdissection of the bulk transcriptome at single-cell resolution and provided a promising TIM infiltration landscape that may shed new light on the development of immune therapy.

MEST promotes lung cancer invasion and metastasis by interacting with VCP to activate NF-κB signaling

Background

Cell invasion is a hallmark of metastatic cancer, leading to unfavorable clinical outcomes. In this study, we established two highly invasive lung cancer cell models (A549-i8 and H1299-i8) and identified mesoderm-specific transcript (MEST) as a novel invasive regulator of lung cancer. We aim to characterize its biological function and clinical significance in lung cancer metastasis.

Methods

Transwell invasion assay was performed to establish high-invasive lung cancer cell model. Immunohistochemistry (IHC) was used to detect MEST expression in tumor tissues. Mass spectrometry and bioinformatic analyses were used to identify MEST-regulated proteins and binding partners. Co-immunoprecipitation assay was performed to detect the interaction of MEST and VCP. The biological functions of MEST were investigated in vitro and in vivo. Immunofluorescence staining was conducted to explore the colocalization of MEST and VCP.

Results

MEST overexpression promoted metastasis of lung cancer cells in vivo and in vitro by activating NF-κB signaling. MEST increased the interaction between VCP and IκBα, which accelerated IκBα degradation and NF-κB activation. Such acceleration was abrogated by VCP silencing, indicating that MEST is an upstream activator of the VCP/IκBα/NF-κB signaling pathway. Furthermore, high expressions of MEST and VCP were associated with poor survival of lung cancer patients.

Conclusion

Collectively, these results demonstrate that MEST plays an important role in driving invasion and metastasis of lung cancer by interacting with VCP to coordinate the IκBα/NF-κB pathway. Targeting the MEST/VCP/IκBα/NF-κB signaling pathway may be a promising strategy to treat lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-02107-1.

Glycolysis-related gene expression profiling serves as a novel prognosis risk predictor for human hepatocellular carcinoma

Metabolic pattern reconstruction is an important factor in tumor progression. Metabolism of tumor cells is characterized by abnormal increase in anaerobic glycolysis, regardless of high oxygen concentration, resulting in a significant accumulation of energy from glucose sources. These changes promotes rapid cell proliferation and tumor growth, which is further referenced a process known as the Warburg effect. The current study reconstructed the metabolic pattern in progression of cancer to identify genetic changes specific in cancer cells. A total of 12 common types of solid tumors were included in the current study. Gene set enrichment analysis (GSEA) was performed to analyze 9 glycolysis-related gene sets, which are implicated in the glycolysis process. Univariate and multivariate analyses were used to identify independent prognostic variables for construction of a nomogram based on clinicopathological characteristics and a glycolysis-related gene prognostic index (GRGPI). The prognostic model based on glycolysis genes showed high area under the curve (AUC) in LIHC (Liver hepatocellular carcinoma). The findings of the current study showed that 8 genes (AURKA, CDK1, CENPA, DEPDC1, HMMR, KIF20A, PFKFB4, STMN1) were correlated with overall survival (OS) and recurrence-free survival (RFS). Further analysis showed that the prediction model accurately distinguished between high- and low-risk cancer patients among patients in different clusters in LIHC. A nomogram with a well-fitted calibration curve based on gene expression profiles and clinical characteristics showed good discrimination based on internal and external cohorts. These findings indicate that changes in expression level of metabolic genes implicated in glycolysis can contribute to reconstruction of tumor-related microenvironment.

Centromere Protein A Goes Far Beyond the Centromere in Cancers

Centromere dysfunctions leading to numerical chromosome alterations are believed to be closely related to human cancers. As a centromere-specific protein, centromere protein A (CENP-A) replaces the histone H3 in centromeres and is therefore considered a key factor of centromere identity. Researches have shown that CENP-A is overexpressed in many types of human cancers. However, the behavior and function of CENP-A in tumorigenesis have not yet been systematically summarized. In this article, we describe the pleiotropic roles of CENP-A in human cells. Moreover, we provide a comprehensive review of the current knowledge on the relationship between aberrant expression and ectopic localization of CENP-A and tumorigenesis, and the mechanism of the ectopic deposition of CENP-A in cancers. Furthermore, we note that some oncogenic viruses can modulate the expression and localization of this centromere protein along with its chaperone. At last, we also discuss the therapeutic potential of targeting CENP-A for cancer therapy.

Integrated bioinformatics analysis reveals CDK1 and PLK1 as potential therapeutic targets of lung adenocarcinoma

ABSTRACT

This study is to identify potential biomarkers and therapeutic targets for lung adenocarcinoma (LUAD).GSE6044 and GSE118370 raw data from the Gene Expression Omnibus database were normalized with Robust Multichip Average. After merging these two datasets, the combat function of sva packages was used to eliminate batch effects. Then, limma packages were used to filtrate differentially expressed genes. We constructed protein-protein interaction relationships using STRING database and hub genes were identified based on connectivity degrees. The cBioportal database was used to explore the alterations of the hub genes. The promoter methylation of cyclin dependent kinase 1 (CDK1) and polo-like Kinase 1 (PLK1) and their association with tumor immune infiltration in patients with LUAD were investigated using DiseaseMeth version 2.0 and TIMER databases. The Cancer Genome Atlas-LUAD dataset was used to perform gene set enrichment analysis.We identified 10 hub genes, which were upregulated in LUAD, among which 8 were successfully verified in the Cancer Genome Atlas and Oncomine databases. Kaplan-Meier analysis indicated that the expressions of CDK1 and PLK1 in LUAD patients were associated with overall survival and disease-free survival. The methylation levels in the promoter regions of these 2 genes in LUAD patients were lower than those in normal lung tissues. Their expressions in LUAD were associated with tumor stages and relative abundance of tumor infiltrating immune cells, such as B cells, CD4+ T cells, and macrophages. Moreover, cell cycle, DNA replication, homologous recombination, mismatch repair, P53 signaling pathway, and small cell lung cancer signaling were significantly enriched in CDK1 and PLK1 high expression phenotype.CDK1 and PLK1 may be used as potential biomarkers and therapeutic targets for LUAD.

Development and validation of a LRP1B mutation-associated prognostic model for hepatocellular carcinoma

PURPOSE

 To develop a LRP1B gene mutation based prognostic model for hepatocellular carcinoma (HCC) patients risk prediction. Methods: The LRP1B gene mutation rate was calculated from HCC patient samples. Meanwhile, differentially expressed genes according to LRP1B mutant were screened out for prognostic model establishment. Based on this innovative model, HCC patients were categoried into high and low-risk group. The immune status including immune cell infiltration ratio and checkpoints have been explored in two groups. The functions of LRP1B and risk factors in the model were verified using both in vivo and in vitro experiments.

RESULTS

It could be demonstrated that LRP1B was a potential negative predictor for HCC patients prognosis with high mutation frequency. The functions of LRP1B was verified with ELISA assay and Quantitative Real-time PCR method based on clinical recruited HCC participants. 11 genes displayed significant differences according to LRP1B status, which could better predict HCC patient prognosis. The functions of these genes were examined using HCC cell line HCCLM3, suggesting they played a pivotal role in determining HCC cell proliferation and apoptosis. From the immune cell infiltration ratio analysis, there was a significant difference in the infiltration degree of 7 types of immune cells and 2 immune checkpoints between high and low-risk HCC patients.

CONCLUSION

 This study hypothesized a potential prognostic biomarker and developed a novel LRP1B mutation-associated prognostic model for hepatocellular carcinoma, which provided a systematic reference for future understanding of clinical research.

High Expression Levels of CDK1 and CDC20 in Patients With Lung Squamous Cell Carcinoma are Associated With Worse Prognosis

Purpose: Progress related to the early detection and molecular targeted therapy of lung squamous cell carcinoma (LUSC) remains limited. The goal of our study was to identify key candidate indicators of LUSC. Methods: Three microarray datasets (GSE33532, GSE30219 and GSE19188) were applied to find differentially expressed genes (DEGs). Functional enrichment analyses of DEGs were carried out, and their protein-protein interaction (PPI) network was established. Hub genes were chosen from the PPI network according to their degree scores. Then, overall survival (OS) analyses of hub genes were carried out using Kaplan-Meier plotter, and their GSEA analyses were performed. Public databases were used to verify the expression patterns of CDK1 and CDC20. Furthermore, basic experiments were performed to verify our findings. Results: A total of 1,366 DEGs were identified, containing 669 downregulated and 697 upregulated DEGs. These DEGs were primarily enriched in cell cycle, chromosome centromeric region and nuclear division. Seventeen hub genes were selected from PPI network. Survival analyses demonstrated that CDK1 and CDC20 were closely associated with OS. GSEA analyses revealed that cell cycle, DNA replication, and mismatch repair were associated with CDK1 expression, while spliceosome, RNA degradation and cell cycle were correlated with CDC20 expression. Based on The Cancer Genome Atlas (TCGA) and The Human Protein Atlas (THPA) databases, CDK1 and CDC20 were upregulated in LUSC at the mRNA and protein levels. Moreover, basic experiments also supported the obvious upregulation of CDK1 and CDC20 in LUSC. Conclusion: Our study suggests and validates that CDK1 and CDC20 are potential therapeutic targets and prognostic biomarkers of LUSC.

Systematic profiling of invasion-related gene signature predicts prognostic features of lung adenocarcinoma

Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis and prognosis prediction in patients with LUAD. Invasion-related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion-related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi-gene risk model was constructed by Lasso-Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features. 3 subtypes (C1, C2 and C3) based on the expression of 97 invasion-related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signalling pathway and other tumour-related pathways. A 5-gene signature (KRT6A, MELTF, IRX5, MS4A1 and CRTAC1) was identified by using Lasso-Cox analysis. The training, validation and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumour tissues were higher than in normal tissues, while CRTAC1 expression in tumour tissues was lower than in normal tissues. The 5-gene signature prognostic stratification system based on invasion-related genes could be used to assess prognostic risk in patients with LUAD.

Bioinformatics Analysis Reveals Centromere Protein K Can Serve as Potential Prognostic Biomarker and Therapeutic Target for Non-small Cell Lung Cancer

Background:

Non-small cell lung carcinoma (NSCLC) accounts for 80% of all lung cancer cases, which have been a leading cause of morbidity and mortality worldwide. Previous studies demonstrated that centromere proteins were dysregulated and involved in regulating the tumorigenesis and development of human cancers. However, the roles of centromere protein family members in NSCLC remained to be further elucidated.

Objective:

The present study aimed to explore the roles of centromere protein family members in NSCLC.

Method:

GEPIA (http://gepia.cancer-pku.cn/) was used to analyze the target’s expression between normal and human cancers. We explored the prognostic value of centromere proteins in NSCLC using the Kaplan–Meier plotter (http://kmplot.com). The protein-protein interaction among centromere proteins were determined using GeneMANIA (http://www.genemania.org). TISIDB (http://cis.hku.hk/TISIDB) database was used to detect the relationship between centromere proteins expression and clinical stages, lymphocytes, immunomodulators and chemokines in NSCLC. The DAVID database (https://david.ncifcrf.gov) was used to detect potential roles of CENPK using its co-expressing genes

Results:

The present study for the first time showed that centromere protein family members including CENPA, CENPF, CENPH, CENPI, CENPK, CENPM, CENPN, CENPO, CENPQ, CENPU were dysregulated and correlated to the poor prognosis of patients with LUAD. CENPK showed the greatest correlation with the prognosis of patients with NSCLC. We found that CENPK was significantly highly expressed in LUAD samples and overexpression of CENPK was remarkably correlated to the shorter OS and DFS on patients with different stage NSCLC. Of note, this study for the first time showed that CENPK was significantly correlated to the lymphocytes and immunomodulators using the TISIDB database

Conclusion:

In summary, CENPK can serve as a novel biomarker for the diagnosis of patients with NSCLC.

High expression levels of centromere protein A plus upregulation of the phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin signaling pathway affect chemotherapy response and prognosis in patients with breast cancer

Centromere proteins (CENPs) are involved in mitosis, and CENP gene expression levels are associated with chemotherapy responses in patients with breast cancer. The present study aimed to examine the roles and underlying mechanisms of the effects of CENP genes on chemotherapy responses and breast cancer prognosis. Using data obtained from the Gene Expression Omnibus (GEO) database, correlation and Cox multivariate regression analyses were used to determine the CENP genes associated with chemotherapy responses and survival in patients with breast cancer. Weighted gene co-expression network and correlation analyses were used to determine the gene modules co-expressed with the identified genes and the differential expression of gene modules associated with the pathological complete response (PCR) and residual disease (RD) subgroups. CENPA, CENPE, CENPF, CENPI, CENPJ and CENPN were associated with a high nuclear grade and low estrogen and progesterone receptor expression levels. In addition, CENPA, CENPB, CENPC and CENPO were independent factors affecting the distant relapse-free survival (DRFS) rates in patients with breast cancer. Patients with high expression levels of CENPA or CENPO exhibited poor prognoses, whereas those with high expression levels of CENPB or CENPC presented with favorable prognoses. For validation between databases, the Cancer Genome Atlas (TCGA) database analysis also revealed that CENPA, CENPB and CENPO exerted similar effects on overall survival. However, according to the multivariate analyses, only CENPA was an independent risk factor associated with DRFS in GEO database. In addition, in the RD subgroup, patients with higher CENPA expression levels had a worse prognosis compared with those with lower CENPA expression levels. Among patients with high expression levels of CENPA, the PI3K/Akt/mTOR pathway was more likely to be activated in the RD compared with the PCR subgroup. The same trend was observed in TCGA data. These results suggested that high CENPA expression levels plus upregulation of the PI3K/Akt/mTOR signaling pathway may affect DRFS in patients with breast cancer.

TSPAN1, TMPRSS4, SDR16C5, and CTSE as Novel Panel for Pancreatic Cancer: A Bioinformatics Analysis and Experiments Validation

Pancreatic cancer is a lethal malignancy with a poor prognosis. This study aims to identify pancreatic cancer-related genes and develop a robust diagnostic model to detect this disease. Weighted gene co-expression network analysis (WGCNA) was used to determine potential hub genes for pancreatic cancer. Their mRNA and protein expression levels were validated through reverse transcription PCR (RT-PCR) and immunohistochemical (IHC). Diagnostic models were developed by eight machine learning algorithms and ten-fold cross-validation. Four hub genes (TSPAN1, TMPRSS4, SDR16C5, and CTSE) were identified based on bioinformatics. RT-PCR showed that the four hub genes were expressed at medium to high levels, IHC revealed that their protein expression levels were higher in pancreatic cancer tissues. For the panel of these four genes, eight models performed with 0.87-0.92 area under the curve value (AUC), 0.91-0.94 sensitivity, and 0.84-0.86 specificity in the validation cohort. In the external validation set, these models also showed good performance (0.86-0.98 AUC, 0.84-1.00 sensitivity, and 0.86-1.00 specificity). In conclusion, this study has identified four hub genes that might be closely related to pancreatic cancer: TSPAN1, TMPRSS4, SDR16C5, and CTSE. Four-gene panels might provide a theoretical basis for the diagnosis of pancreatic cancer.

MicroRNA-497-5p Is Downregulated in Hepatocellular Carcinoma and Associated with Tumorigenesis and Poor Prognosis in Patients

Background

MicroRNAs (miRNAs) have been demonstrated to exhibit important regulatory roles in multiple malignancies, including hepatocellular carcinoma (HCC). hsa-miR-497-5p was reported to involve in cancer progression and poor prognosis in many kinds of tumors. However, the expression and its clinical significance of hsa-miR-497-5p in HCC remain unclear.

Methods

In the present study, we investigated the expression of hsa-miR-497-5p in HCC and analyzed the correction of clinical features with prognosis. The expression levels of hsa-miR-497-5p and potential target genes were analyzed in HCC and adjacent noncancerous tissues using The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to analyze hsa-miR-497-5p levels in 328 HCC tissues and 30 paired adjacent noncancer tissues. Overall survival (OS) and progression-free survival (PFS) of patients with HCC were assessed using the Kaplan-Meier method and the log-rank test.

Results

The hsa-miR-497-5p expression levels were decreased, and its target genes ACTG1, CSNK1D, PPP1CC, and BIRC5 were upregulated in HCC tissues compared with normal tissues. Lower levels of hsa-miR-497-5p expression and higher levels of the four target genes were significantly associated with higher tumor diameter. Moreover, patients with lower hsa-miR-497-5p expression and higher target genes levels had shorter OS.

Conclusion

The expression levels of hsa-miR-497-5p may play an important regulatory role in HCC and are closely correlated with HCC progression and poor prognosis in patients. The hsa-miR-497-5p may be a specific therapeutic target for the treatment of HCC.

Potential role of glucosamine-phosphate N-acetyltransferase 1 in the development of lung adenocarcinoma

Glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1) is a key enzyme associated with glucose metabolism and uridine diphosphate-N-acetylglucosamine biosynthesis. Abnormal GNPNAT1 expression might be associated with carcinogenesis. We analyzed multiple lung adenocarcinoma (LUAD) gene expression databases and verified GNPNAT1 higher expression in LUAD tumor tissues than in normal tissues. Moreover, we analyzed the survival relationship between LUAD patients’ clinical status and GNPNAT1 expression, and found higher GNPNAT1 expression in LUAD patients with unfavorable prognosis. We built GNPNAT1 gene co-expression networks and further annotated the co-expressed genes’ Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and various associated regulatory factors. These co-expression genes’ functional networks mainly participate in chromosome segregation, RNA metabolic process, and RNA transport. We analyzed GNPNAT1 genetic alterations and co-occurrence networks, and the functional networks of these genes showed that GNPNAT1 participates in multiple steps of cell cycle transition and in the development of some cancers. We assessed the correlation between GNPNAT1 expression and cancer immune infiltrates and showed that GNPNAT1 expression is correlated with several immune cells, chemokines, and immunomodulators in LUAD. We found that GNPNAT1 correlates with LUAD development and prognosis, laying a foundation for further research, especially in immunotherapy.

Integrated Protein-Protein Interaction and Weighted Gene Co-expression Network Analysis Uncover Three Key Genes in Hepatoblastoma

Hepatoblastoma (HB) is the most common liver tumor in the pediatric population, with typically poor outcomes for advanced-stage or chemotherapy-refractory HB patients. The objective of this study was to identify genes involved in HB pathogenesis via microarray analysis and subsequent experimental validation. We identified 856 differentially expressed genes (DEGs) between HB and normal liver tissue based on two publicly available microarray datasets (GSE131329 and GSE75271) after data merging and batch effect correction. Protein–protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were conducted to explore HB-related critical modules and hub genes. Subsequently, Gene Ontology (GO) analysis was used to reveal critical biological functions in the initiation and progression of HB. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that genes involved in cell cycle phase transition and the PI3K/AKT signaling were associated with HB. The intersection of hub genes identified by both PPI and WGCNA analyses revealed five potential candidate genes. Based on receiver operating characteristic (ROC) curve analysis and reports in the literature, we selected CCNA2, CDK1, and CDC20 as key genes of interest to validate experimentally. CCNA2, CDK1, or CDC20 small interfering RNA (siRNA) knockdown inhibited aggressive biological properties of both HepG2 and HuH-6 cell lines in vitro. In conclusion, we identified CCNA2, CDK1, and CDC20 as new potential therapeutic biomarkers for HB, providing novel insights into important and viable targets in future HB treatment.

CDK1 serves as a novel therapeutic target for endometrioid endometrial cancer

Background: Endometrial cancer (EC) is one of the most common and prevalent gynecologic malignancies worldwide. The aim of this study was to identify a novel therapeutic target for endometrioid endometrial cancer.

Materials and Methods: Bioinformatic analysis was performed and CDK1 was screen out as one of the hub genes in the pathogenesis of EC. Immunohistochemistry was used to verify the expression of CDK1 in endometrial cancer tissue. Cell viability and colony formation were used to study the effects of CDK1 on the proliferation and colony formation of endometrial cancer cells in vitro. Apoptosis and cell cycle assays were used to elucidate the mechanism of CDK1 affecting cell proliferation. Tumor xenograft transplantation assay was performed to show the effects of CDK1 on the growth of endometrial cancer cells in vivo.

Results: CDK1 was over expressed in endometrioid endometrial cancer, and accumulation of cytoplasmic CDK1 was associated with histological grade of EC. CDK1 promoted endometrial cancer cell growth and colony formation in vitro. The inhibition of CDK1 activity induced cell apoptosis and caused G2/M phase arrest of cell cycle in endometrial cancer cells. The inhibition of CDK1 activity also inhibited endometrial cancer growth in xenograft models.

Conclusion: CDK1 was involved in the pathogenesis of endometrioid endometrial cancer and provided a novel therapeutic target for endometrioid endometrial cancer.

Identification of theranostic factors for patients developing metastasis after surgery for early-stage lung adenocarcinoma

Rationale: Lung adenocarcinoma (LUAD) is an aggressive disease with high propensity of metastasis. Among patients with early-stage disease, more than 30% of them may relapse or develop metastasis. There is an unmet medical need to stratify patients with early-stage LUAD according to their risk of relapse/metastasis to guide preventive or therapeutic approaches. In this study, we identified 4 genes that can serve both therapeutic and diagnostic (theranostic) purposes.

Methods: Three independent datasets (GEO, TCGA, and KMPlotter) were used to evaluate gene expression profile of patients with LUAD by unbiased screening approach. Upon significant genes uncovered, functional enrichment analysis was carried out. The predictive power of their expression on patient prognosis were evaluated. Once confirmed their theranostic roles by integrated bioinformatics, we further conducted in vitro and in vivo validation.

Results: We found that four genes (ADAM9, MTHFD2, RRM2, and SLC2A1) were associated with poor patient outcomes with an increased hazard ratio in LUAD. Knockdown of them, both separately and simultaneously, suppressed lung cancer cell proliferation and migration ability in vitro and prolonged survival time in metastatic tumor mouse models. Moreover, these four biomarkers were found to be overexpressed in tumor tissues from LUAD patients, and the total immunohistochemical staining scores correlated with poor prognosis.

Conclusions: These results suggest that these four identified genes could be theranostic biomarkers for stratifying high-risk patients who develop relapse/metastasis in early-stage LUAD. Developing therapeutic approaches for the four biomarkers may benefit early-stage LUAD patients after surgery.

Artificial Intelligence and its future potential in lung cancer screening

Artificial intelligence (AI) simulates intelligent behavior as well as critical thinking comparable to a human being and can be used to analyze and interpret complex medical data. The application of AI in imaging diagnostics reduces the burden of radiologists and increases the sensitivity of lung cancer screening so that the morbidity and mortality associated with lung cancer can be decreased. In this article, we have tried to evaluate the role of artificial intelligence in lung cancer screening, as well as the future potential and efficiency of AI in the classification of nodules. The relevant studies between 2010-2020 were selected from the PubMed database after excluding animal studies and were analyzed for the contribution of AI. Techniques such as deep learning and machine learning allow automatic characterization and classification of nodules with high precision and promise an advanced lung cancer screening method in the future. Even though several combination models with high performance have been proposed, an effectively validated model for routine use still needs to be improvised. Combining the performance of artificial intelligence with a radiologist's expertise offers a successful outcome with higher accuracy. Thus, we can conclude that higher sensitivity, specificity, and accuracy of lung cancer screening and classification of nodules is possible through the integration of artificial intelligence and radiology. The validation of models and further research is to be carried out to determine the feasibility of this integration.

Identification of a potentially functional circRNA-miRNA-mRNA ceRNA regulatory network in bladder cancer by analysis of microarray data

Background

Circular RNAs (circRNAs) have received increasing attention in cancer development. However, a substantial number of circRNAs still require characterization. The purpose of this study is to uncover novel circRNAs and their molecular mechanism in bladder cancer (BCa).

Methods

A combinative strategy of extensive data mining and computational biology was employed to identify BCa-related circRNAs and explore their potential mechanisms of action.

Results

Three differentially expressed circRNAs (has_circ_0023642, has_circ_0047322, has_circ_0041151) were obtained from the microarray dataset (GSE92675). Four miRNAs (miR-616, miR-515-5p, miR-647, miR-1178) with potential binding sites with these three circRNAs were identified. Pathway analysis demonstrated that all four miRNAs were closely associated with some cancer-related pathways. Survival analysis indicated that these miRNAs might potentially play a role in tumor-suppressive functions in BCa. Subsequently, 181 overlapping genes were identified from 472 up-regulated genes in BCa (TCGA database), and 10,017 predicted target genes of the four miRNAs obtained. A circRNA-miRNA-mRNA network was constructed on the identified three circRNAs, four miRNAs, and 181 overlapping genes. Besides, six hub genes (CENPA, HIST1H2BJ, HIST1H2BO, HIST1H3H, HIST1H3B, HIST1H3F) were identified from establishing a protein-protein interaction (PPI) network on the same overlapping genes. Furthermore, a circRNA-miRNA-hub gene sub-network was built to delineate the links among the differential circRNAs, miRNA, and hub genes.

Conclusions

Our study provided significant insights into the molecular mechanisms that regulate the progression of BCa from the circRNA-miRNA-mRNA network view.

High Centromere Protein-A (CENP-A) Expression Correlates with Progression and Prognosis in Gastric Cancer

Purpose

Recent studies have established the ability of centromere protein-A (CENP-A) to perform as an oncogene, regulating tumor progression. The aim of this research was to explore the relationship between CENP-A expression and clinical significance in gastric cancer (GC) patients.

Materials and Methods

Experiments with a microarray were conducted using the Affymetrix U133 plus 2.0 GeneChip Array. Upregulated differentially expressed genes (DEGs) were identified via the GEO2R and intersected using a Venn diagram. Bioinformatic databases Omcomine, GEPIA, and Ualcan were applied to investigate the expression level of CENP-A in GC. The real-time quantitative RT-PCR (qRT-PCR) was used to validate the level of CENP-A mRNA in GC. Immunohistochemistry (IHC) was employed to verify the protein levels of CENP-A, while the relationship between CENP-A expression and patients’ clinical parameters in GC was explored through the use of IHC. Kaplan-Meier analysis was conducted to evaluate the prognostic significance of CENP-A. Additionally, the Kaplan-Meier plotter database (KM plotter) was used to verify the prognostic function of CENP-A in GC patients.

Results

The results indicated that CENP-A was significantly overexpressed, both in protein and mRNA levels of GC tissues, compared to adjacent noncancerous tissues (P<0.05). Furthermore, we observed that CENP-A expression was positively associated with TNM stage, tumor classification, lymph node metastasis, distant metastasis, and Lauren type (P<0.05). Kaplan-Meier analysis showed that patients with an overexpression of CENP-A had significantly poorer overall survival (OS) times (P<0.05). Multivariate analysis suggested CENP-A may serve as an independent predicting factor for the poor outcome of GC patients.

Conclusion

Our results show that CENP-A upregulation is significantly correlated with advanced tumor progression and poor prognosis. CENP-A may function as a novel potential biomarker for predicting the clinical outcomes of GC patients.

Identification of Potential Hub Genes Related to Diagnosis and Prognosis of Hepatitis B Virus-Related Hepatocellular Carcinoma via Integrated Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is a common malignant cancer with poor survival outcomes, and hepatitis B virus (HBV) infection is most likely to contribute to HCC. But the molecular mechanism remains obscure. Our study intended to identify the candidate potential hub genes associated with the carcinogenesis of HBV-related HCC (HBV-HCC), which may be helpful in developing novel tumor biomarkers for potential targeted therapies. Four transcriptome datasets (GSE84402, GSE25097, GSE94660, and GSE121248) were used to screen the 309 overlapping differentially expressed genes (DEGs), including 100 upregulated genes and 209 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were used to explore the biological function of DEGs. A PPI network based on the STRING database was constructed and visualized by the Cytoscape software, consisting of 209 nodes and 1676 edges. Then, we recognized 17 hub genes by CytoHubba plugin, which were further validated on additional three datasets (GSE14520, TCGA-LIHC, and ICGC-LIRI-JP). The diagnostic effectiveness of hub genes was assessed with receiver operating characteristic (ROC) analysis, and all hub genes displayed good performance in discriminating TNM stage I patient samples and normal tissue ones. For prognostic analysis, two prognostic key genes (TOP2A and KIF11) out of the 17 hub genes were screened and used to develop a prognostic signature, which showed good potential for overall survival (OS) stratification of HBV-HCC patients. Gene Set Enrichment Analysis (GSEA) was performed in order to better understand the function of this prognostic gene signature. Finally, the miRNA-mRNA regulatory relationships of all hub genes in human liver were predicted using miRNet. In conclusion, the current study gives further insight on the pathogenesis and carcinogenesis of HBV-HCC, and the identified DEGs provide a promising direction for improving the diagnostic, prognostic, and therapeutic outcomes of HBV-HCC.

Curcumol Overcomes TRAIL Resistance of Non-Small Cell Lung Cancer by Targeting NRH:Quinone Oxidoreductase 2 (NQO2)

Resistance to tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL) of cancer cell remains a key obstacle for clinical cancer therapies. To overcome TRAIL resistance, this study identifies curcumol as a novel safe sensitizer from a food-source compound library, which exhibits synergistic lethal effects in combination with TRAIL on non-small cell lung cancer (NSCLC). SILAC-based cellular thermal shift profiling identifies NRH:quinone oxidoreductase 2 (NQO2) as the key target of curcumol. Mechanistically, curcumol directly targets NQO2 to cause reactive oxygen species (ROS) generation, which triggers endoplasmic reticulum (ER) stress-C/EBP homologous protein (CHOP) death receptor (DR5) signaling, sensitizing NSCLC cell to TRAIL-induced apoptosis. Molecular docking analysis and surface plasmon resonance assay demonstrate that Phe178 in NQO2 is a critical site for curcumol binding. Mutation of Phe178 completely abolishes the function of NQO2 and augments the TRAIL sensitization. This study characterizes the functional role of NQO2 in TRAIL resistance and the sensitizing function of curcumol by directly targeting NQO2, highlighting the potential of using curcumol as an NQO2 inhibitor for clinical treatment of TRAIL-resistant cancers.

Identification of crucial genes associated with lung adenocarcinoma by bioinformatic analysis

Lung cancer is the world's most common malignancies and ranks first among all cancer-related deaths. Lung adenocarcinoma (LUAD) is the most frequent histological type in lung cancer. Its pathogenesis has not yet been fully elucidated, so it is of great significance to explore related genes for elucidating the molecular mechanism involved in occurrence and development of LUAD.To explore the crucial genes associated with LUAD development and progression, microarray datasets GSE7670, GSE10072, and GSE31547 were acquired from the Gene Expression Omnibus (GEO) database. R language Limma package was adopted to screen the differentially expressed genes (DEGs). The clusterProfiler package was used for enrichment analysis and annotation of the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways for DEGs. The Search Tool for the Retrieval of Interacting Genes database (STRING) was used to construct the protein interaction network for DEGs, while Cytoscape was adopted to visualize it. The functional module was screened with Cytoscape's MCODE (The Molecular Complex Detection) plugin. The crucial genes associated with LUAD were identified by cytoHubba plugin. Kaplan-Meier plotter online tool was used to perform survival analysis of the hub gene.Three hundred twenty-one DEGs in total were screened, of which 105 were upregulated and 216 were downregulated. It was found that some GO terms and pathways (e.g., collagen trimer, extracellular structure organization, heparin binding, complement and coagulation cascades, malaria, protein digestion and absorption, and PPAR signaling pathway) were considerably enriched in DEGs. UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, PRC1, and CDK1 were identified as crucial genes. Survival analysis showed that the overexpression of UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, and PRC1 significantly reduced the overall survival of LUAD patients. One of the crucial genes: UBE2C was validated by immunohistochemistry to be upregulated in LUAD tissues.This study screened out potential biomarkers of LUAD, providing a theoretical basis for elucidating the pathogenesis and evaluating the prognosis of LUAD.

Identification of a Prognostic Model Based on Immune-Related Genes of Lung Squamous Cell Carcinoma

Immune-related genes (IRGs) play considerable roles in tumor immune microenvironment (IME). This research aimed to discover the differentially expressed immune-related genes (DEIRGs) based on the Cox predictive model to predict survival for lung squamous cell carcinoma (LUSC) through bioinformatics analysis. First of all, the differentially expressed genes (DEGs) were acquired based on The Cancer Genome Atlas (TCGA) using the limma R package, the DEIRGs were obtained from the ImmPort database, whereas the differentially expressed transcription factors (DETFs) were acquired from the Cistrome database. Thereafter, a TFs-mediated IRGs network was constructed to identify the candidate mechanisms for those DEIRGs in LUSC at molecular level. Moreover, Gene Ontology (GO), together with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, was conducted for exploring those functional enrichments for DEIRGs. Besides, univariate as well as multivariate Cox regression analysis was conducted for establishing a prediction model for DEIRGs biomarkers. In addition, the relationship between the prognostic model and immunocytes was further explored through immunocyte correlation analysis. In total, 3,599 DEGs, 223 DEIRGs, and 46 DETFs were obtained from LUSC tissues and adjacent non-carcinoma tissues. According to multivariate Cox regression analysis, 10 DEIRGs (including CALCB, GCGR, HTR3A, AMH, VGF, SEMA3B, NRTN, ENG, ACVRL1, and NR4A1) were retrieved to establish a prognostic model for LUSC. Immunocyte infiltration analysis showed that dendritic cells and neutrophils were positively correlated with IRGs, which possibly exerted an important part within the IME of LUSC. Our study identifies a prognostic model based on IRGs, which is then used to predict LUSC prognosis and analyze immunocyte infiltration. This may provide a novel insight for exploring the potential IRGs in the IME of LUSC.

Construction and Validation of Novel Diagnostic and Prognostic DNA Methylation Signatures for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent life-threatening human cancers and the leading cause of cancer-related mortality, with increased global incidence within the last decade. Identification of effective diagnostic and prognostic biomarkers would enable reliable risk stratification and efficient screening of high-risk patients, thereby facilitating clinical decision-making. Herein, we performed a comprehensive, robust DNA methylation analysis based on genome-wide DNA methylation profiling. We constructed a diagnostic signature with five DNA methylation markers, which precisely distinguished HCC patients from normal controls. Cox regression and LASSO analysis were applied to construct a prognostic signature with four DNA methylation markers. A one-to-one correlation analysis was carried out between genes of the whole genome and our prognostic signature. Exploration of the biological function and the role of the underlying significantly correlated genes was conducted. A mixed dataset of 463 HCC patients and 253 normal controls, derived from six independent datasets, was used to valid the diagnostic signature. Results showed a specificity of 96.84% and sensitivity of 96.77%. Class scores for the diagnostic signature were significantly different between normal controls, individuals with liver diseases, and HCC patients. The present signature has the potential to serve as a biomarker to monitor health in normal controls. Additionally, HCC patients were successfully separated into low-risk and high-risk groups by the prognostic signature, with a better prognosis for patients in the low-risk group. Kaplan-Meier and ROC analysis confirmed that the prognostic signature performed well. We found eight of the top ten genes to positively correlate with risk scores of the prognostic signature, and to be involved in cell cycle regulation. This eight-gene panel also served as a prognostic signature. The robust evidence presented in this study therefore demonstrates the effectiveness of the prognostic signature. In summary, we constructed diagnostic and prognostic signatures, which have potential for use in diagnosis, surveillance, and prognostic prediction for HCC patients. Eight genes that were significantly and positively correlated with the prognostic signature were strongly associated with cell cycle processes. Therefore, the prognostic signature can be used as a guide by which to measure responsiveness to cell-cycle-targeting agents.