Virtual Screening of Small Molecules Targeting BCL2 with Machine Learning, Molecular Docking, and MD Simulation

This study aimed to identify potential BCL-2 small molecule inhibitors using deep neural networks (DNN) and random forest (RF), algorithms as well as molecular docking and molecular dynamics (MD) simulations to screen a library of small molecules. The RF model classified 61% (2355/3867) of molecules as 'Active'. Further analysis through molecular docking with Vina identified CHEMBL3940231, CHEMBL3938023, and CHEMBL3947358 as top-scored small molecules with docking scores of -11, -10.9, and 10.8 kcal/mol, respectively. MD simulations validated these compounds' stability and binding affinity to the BCL2 protein.

Three candidate anticancer drugs were repositioned by integrative analysis of the transcriptomes of species with different regenerative abilities after injury

Regeneration is a homeostatic process that involves the restoration of cells and body parts. Most of the molecular mechanisms and signalling pathways involved in wound healing, such as proliferation, have also been associated with cancer cell growth, suggesting that cancer is an over/unhealed wound. In this study, we examined differentially expressed genes in spinal cord samples from regenerative organisms (axolotl and zebrafish) and nonregenerative organisms (mouse and rat) compared to intact control spinal cord samples using publicly available transcriptomics data and bioinformatics analyses. Based on these gene signatures, we investigated 3 small compounds, namely cucurbitacin I, BMS-754807, and PHA-793887 as potential candidates for the treatment of cancer. The predicted target genes of the repositioned compounds were mainly enriched with the greatest number of genes in cancer pathways. The molecular docking results on the binding affinity between the repositioned compounds and their target genes are also reported. The repositioned 3 small compounds showed anticancer effect both in 2D and 3D cell cultures using the prostate cancer cell line as a model. We propose cucurbitacin I, BMS-754807, and PHA-793887 as potential anticancer drug candidates. Future studies on the mechanisms associated with the revealed gene signatures and anticancer effects of these three small compunds would allow scientists to develop therapeutic approaches to combat cancer. This research contributes to the evaluation of mechanisms and gene signatures that either limit or cause cancer, and to the development of new cancer therapies by establishing a link between regeneration and carcinogenesis.

TRAIP serves as a potential prognostic biomarker and correlates with immune infiltrates in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the major types of lung cancer with high morbidity and mortality. The TRAF-interacting protein (TRAIP) is a ring-type E3 ubiquitin ligase which has been recently identified to play pivotal roles in various cancers. However, the expression and function of TRAIP in LUAD remain elusive.

METHODS

In this study, we used bioinformatic tools as well as molecular experiments to explore the exact role of TRAIP and the underlying mechanism.

RESULTS

Data mining across the UALCAN, GEPIA and GTEx, GEO and HPA databases revealed that TRAIP was significantly overexpressed in LUAD tissues than that in adjacent normal tissues. Kaplan-Meier curve showed that high TRAIP expression was associated with poor overall survival (OS) and relapse-free survival (RFS). Univariate and multivariate cox regression analysis revealed that TRAIP was an independent risk factor in LUAD. And the TRAIP-based nomogram further supported the prognostic role of TRAIP in LUAD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that TRAIP-associated genes were mainly involved in DNA replication, cell cycle and other processes. The immune infiltration analysis indicated that TRAIP expression was tightly correlated with the infiltration of diverse immune cell types, including B cell, CD8 + T cell, neutrophil and dendritic cell. Moreover, TRAIP expression was observed to be significantly associated with tumor infiltrating lymphocytes (TILs) and immune checkpoint molecules. In vitro experiments further confirmed knockdown of TRAIP inhibited cell migration and invasion, as well as decreasing chemokine production and inhibiting M2-like macrophage recruitment. Lastly, CMap analysis identified 10 small molecule compounds that may target TRAIP, providing potential therapies for LUAD.

CONCLUSIONS

Collectively, our study found that TRAIP is an oncogenic gene in LUAD, which may be a potential prognostic biomarker and promising therapeutic target for LUAD.

Screening of Key Prognosis Genes of Lung Adenocarcinoma Based on Expression Analysis on TCGA Database

Objective

The data of lung adenocarcinoma- (LUAD-) related gene expression profiles were mined from the Cancer Genome Atlas (TCGA) database using bioinformatics methods and potential biomarkers related to the occurrence, development, and prognosis of LUAD were screened out to explore the key prognostic genes and clinical significance.

Methods

Following the LUAD gene expression profile data that were initially exported from the TCGA database, R software DESeq2 was employed to analyze the difference between the expression profiles of LUAD and normal tissues. The R package "clusterProfiler" was subsequently utilized to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the differential genes. A protein-protein interaction (PPI) network was constructed via the String database, and cytohubba, a plugin of Cytoscape, was applied to screen hub genes using the MCC algorithm. The Gene Expression Profile Data Interactive Analysis (GEPIA) was used to analyze expressions of 10 candidate genes in LUAD samples and healthy lung samples, and the selected genes were employed for survival analysis.

Results

A total of 1,598 differential genes were identified through differential analyses and data mining, with 1,394 genes upregulated and 204 downregulated. A total of 10 hub genes CCNA2, CDC20, CCNB2, KIF11, TOP2A, BUB1, BUB1B, CENPF, TPX2, and KIF2C were obtained using the cytohubba plugin. The results of the GEPIA analysis indicated that compared with normal lung tissue, the mRNA expression level of the described hub genes in LUAD tissue was significantly increased (P < 0.05). Survival analysis revealed that these genes had a significant impact on the overall survival time of LUAD patients (P < 0.05).

Conclusion

The previously described key genes related to LUAD identified in the TCGA database may be used as potential prognostic biomarkers, which will contribute to further comprehension of the occurrence and development of LUAD and provide references for its diagnosis and treatment.

Identification of a transcription factor‑cyclin family genes network in lung adenocarcinoma through bioinformatics analysis and validation through RT‑qPCR

Lung adenocarcinoma (LUAD) is the predominant pathological subtype of lung cancer, which is the most prevalent and lethal malignancy worldwide. Cyclins have been reported to regulate the physiology of various types of tumors by controlling cell cycle progression. However, the key roles and regulatory networks associated with the majority of the cyclin family members in LUAD remain unclear. In total, 556 differentially expressed genes were screened from the GSE33532, GSE40791 and GSE19188 mRNA microarray datasets by R software. Subsequently, protein-protein interaction network containing 499 nodes and 4,311 edges, in addition to a significant module containing 76 nodes and 2,631 edges, were extracted through the MCODE plug-in of Cytoscape. A total of four cyclin family genes [cyclin (CCNA2, CCNB1, CCNB2 and CCNE2] were then found in this module. Further co-expression analysis and associated gene prediction revealed forkhead box M1 (FOXM1), the common transcription factor of CCNB2, CCNB1 and CCNA2. In addition, using GEPIA database, it was found that the high expression of these four genes were simultaneously associated with poorer prognosis in patients with LUAD. Experimentally, it was proved that these four hub genes were highly expressed in LUAD cell lines (Beas-2B and H1299) and LUAD tissues through qPCR, western blot analysis and immunohistochemical studies. The diagnostic value of these 4 hub genes in LUAD was analyzed by logistic regression, CCNA2 was deleted, following which a nomogram diagnostic model was constructed accordingly. The area under the curve values of CCNB1, CCNB2 and FOXM1 diagnostic models were calculated to be 0.92, 0.91 and 0.96 in the training set (Combined dataset of GSE33532, GSE40791 and GSE19188) and two validation sets (GSE10072 and GSE75037), respectively. To conclude, data from the present study suggested that the FOXM1/cyclin (CCNA2, CCNB1 and/or CCNB2) axis may serve a regulatory role in the development and prognosis of LUAD. Specifically, CCNB1, CCNB2 and FOXM1 have potential as diagnostic markers and/or therapeutic targets for LUAD treatment.

Role of ferroptosis-related genes in coronary atherosclerosis and identification of key genes: integration of bioinformatics analysis and experimental validation

Background

Coronary atherosclerosis (CA) is the most common type of atherosclerosis. However, the inherent pathogenesis and mechanisms of CA are unclear, and the relationship with ferroptosis-related genes (FRGs) has not been reported. The purpose of this study was to use bioinformatics techniques to evaluate potential therapeutic targets for CA.Please provide the given name for author “Dingshun”.Please provide the given name for author “Dingshun”.

Methods

First, the GSE132651 dataset was acquired from the Gene Expression Omnibus database. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and Protein–Protein interaction network were successively conducted. Next, overlapping genes between hub genes and CA genes were found. FRGs were found when comparing the CA group with the normal group. The correlation between overlapping genes and FRGs was further analyzed. At last, we performed Elisa to validate the expression of these genes in human blood specimens. Mice aortic tissues were used for western blot to detect the expression of proteins.

Results

Based on the GSE132651 dataset, 102 differentially expressed genes were identified. Five overlapping genes between hub genes and CA genes were found (CCNA2, RRM2, PBK, PCNA, CDK1). TFRC and GPX4 were found to be FRGs. TFRC was positively correlated with CCNA2, PBK, PCNA, CDK1, RRM2, with CDK1 being the strongest correlation. GPX4 was negatively correlated with these genes, among which CCNA2 was the strongest correlation. The ELISA results showed that CCNA2, CDK1, and TFRC expression were markedly increased in serum of the CA samples compared with controls, while GPX4 expression was markedly decreased in the CA samples. The western blot results show that GPX4 expression was lower in the model group, TFRC, CDK1, and CCNA2 protein expression were high in the model group.

Conclusions

Ferroptosis-related genes GPX4 and TFRC were closely correlated with the identified overlapping genes CCNA2 and CDK1, which may serve as targeted therapies for the treatment of CA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02747-x.

Using Weighted Gene Co-Expression Network Analysis to Identify Increased MND1 Expression as a Predictor of Poor Breast Cancer Survival

Objective

We used bioinformatics analysis to identify potential biomarker genes and their relationship with breast cancer (BC).

Materials and Methods

We used a weighted gene co-expression network analysis (WGCNA) to create a co-expression network based on the top 25% genes in the GSE24124, GSE33926, and GSE86166 datasets obtained from the Gene Expression Omnibus. We used the DAVID online platform to perform GO and KEGG pathway enrichment analyses and the Cytoscape CytoHubba plug-in to screen the potential genes. Then, we related the genes to prognostic values in BC using the Oncomine, GEPIA, and Kaplan–Meier Plotter databases. Findings were validated by immunohistochemical (IHC) staining in the Human Protein Atlas and the TCGA-BRCA cohort. LinkedOmics identified the interactive expressions of hub genes. We used UALCAN to evaluate the methylation levels of these hub genes. MethSurv and SurvivalMeth were used to assess the multilevel prognostic value. Finally, we assessed hub gene association with immune cell infiltration using TIMER.

Results

The mRNA levels of MKI67, UBE2C, GTSE1, CCNA2, and MND1 were significantly upregulated in BC, whereas ESR1, THSD4, TFF1, AGR2, and FOXA1 were significantly downregulated. The DNA methylation signature analysis showed a better prognosis in the low-risk group. Further subgroup analyses revealed that MND1 might serve as an independent risk factor for unfavorable BC prognosis. Additionally, MND1 expression levels positively correlate with the immune infiltration statuses of CD4+ T cells, CD8+ T cells, B cells, neutrophils, dendritic cells, and macrophages.

Conclusion

Our results indicate that the ten hub genes may be involved in BC’s carcinogenesis, development, or metastasis, and MND1 may be a potential biomarker and therapeutic target for BC.

The Predictive Competing Endogenous RNA Regulatory Networks and Potential Prognostic and Immunological Roles of Cyclin A2 in Pan-Cancer Analysis

Although accumulating evidence has verified the relationship between CCNA2 and cancers, no pan-cancer analysis about the function and the upstream molecular mechanism of CCNA2 is available. For the first time, we analyzed potential oncogenic roles of CCNA2 in 33 cancer types via The Cancer Genome Atlas (TCGA) database. Overexpression of CCNA2 is widespread in almost all cancer types, and it is related to poor prognosis and advanced pathological stages in most cases. Moreover, we conducted upstream miRNAs and lncRNAs of CCNA2 to establish upstream regulatory networks in kidney renal clear cell carcinoma (LINC00997/miR-27b-3p/CCNA2), liver hepatocellular carcinoma (SNHG16, GUSBP11, FGD5-AS1, LINC00630, CD27-AS1, LINC00997/miR-22-3p/CCNA2, miR-29b-3p/CCNA2, miR-29c-3p/CCNA2, and miR-204-5p/CCNA2), and lung adenocarcinoma (miRNA-218-5p/CCNA2 and miR-204-5p/CCNA2) by expression analysis, survival analysis, and correlation analysis. The CCNA2 expression is positively correlated with Th2 cell infiltration and negatively correlated with CD4+ central memory and effector memory T-cell infiltration in different cancer types. Furthermore, CCNA2 is positively associated with expressions of immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT) in most cancer types. Our first CCNA2 pan-cancer study contributes to understanding the prognostic and immunological roles and potential upstream molecular mechanisms of CCNA2 in different cancers.

Identification and Validation of TYMS as a Potential Biomarker for Risk of Metastasis Development in Hepatocellular Carcinoma

Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. Unfortunately, there are few reports on effective biomarkers for HCC metastasis. This study aimed to discover potential key genes of HCC, which could provide new insights for HCC metastasis. GEO (Gene Expression Omnibus) microarray and TCGA (The Cancer Genome Atlas) datasets were integrated to screen for candidate genes involved in HCC metastasis. Differentially expressed genes (DEGs) were screened, and then we performed enrichment analysis of Gene Ontology (GO), together with Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction network was then built and analyzed utilizing STRING and Cytoscape, followed by the identification of 10 hub genes by cytoHubba. Four genes were associated with survival, their prognostic value was verified by prognostic signature analysis. Thymidylate synthase (TYMS) gene was identified as significant HCC metastasis-associated genes after mRNA expression validation and IHC analysis. TYMS silencing in HCC cells remarkedly inhibited growth and invasion. Finally, we found TYMS silencing dramatically decrease DNA synthesis and extracellular matrix (ECM) degradation, resulting in the inhibition of HCC metastasis, indicating TYMS had close associations with HCC development. These findings provided new insights into HCC metastasis and identified candidate gene prognosis signatures for HCC metastasis.

Bioinformatics study on genes related to a high-risk postoperative recurrence of lung adenocarcinoma

In this study, we aimed to screen out genes associated with a high risk of postoperative recurrence of lung adenocarcinoma and investigate the possible mechanisms of the involvement of these genes in the recurrence of lung adenocarcinoma. We identify Hub genes and verify the expression levels and prognostic roles of these genes. Datasets of GSE40791, GSE31210, and GSE30219 were obtained from the Gene Expression Omnibus database. Enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the screened candidate genes using the DAVID database. Then, we performed protein-protein interaction (PPI) network analysis through the database STRING. Hub genes were screened out using Cytoscape software, and their expression levels were determined by the GEPIA database. Finally, we assessed the relationships of Hub genes expression levels and the time of survival. Forty-five candidate genes related to a high-risk of lung adenocarcinoma recurrence were screened out. Gene ontology analysis showed that these genes were enriched in the mitotic spindle assembly checkpoint, mitotic sister chromosome segregation, G2/M-phase transition of the mitotic cell cycle, and ATP binding, etc. KEGG analysis showed that these genes were involved predominantly in the cell cycle, p53 signaling pathway, and oocyte meiosis. We screened out the top ten Hub genes related to high expression of lung adenocarcinoma from the PPI network. The high expression levels of eight genes (TOP2A, HMMR, MELK, MAD2L1, BUB1B, BUB1, RRM2, and CCNA2) were related to short recurrence-free survival and they can be used as biomarkers for high risk of lung adenocarcinoma recurrence. This study screened out eight genes associated with a high risk of lung adenocarcinoma recurrence, which might provide novel insights into researching the recurrence mechanisms of lung adenocarcinoma as well as into the selection of targets in the treatment of the disease.

Identification of novel hub genes associated with gastric cancer using integrated bioinformatics analysis

Background

Gastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high-recurrence-rate. Identifying the molecular signatures and specific biomarkers of GC might provide novel clues for GC prognosis and targeted therapy.

Methods

Gene expression profiles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytohubba plugin. Their prognostic values were assessed by Kaplan–Meier survival analyses and the gene expression profiling interactive analysis (GEPIA). Finally, qRT-PCR in GC tissue samples was established to validate these DEGs.

Results

Total of 295 DEGs were identified between GC and their corresponding normal adjacent tissue samples in E-MTAB-1440, GSE79973, GSE19826, GSE13911, GSE27342, GSE33335 and GSE56807 datasets, including 117 up-regulated and 178 down-regulated genes. Among them, 7 vital upregulated genes (HMMR, SPP1, FN1, CCNB1, CXCL8, MAD2L1 and CCNA2) were selected. Most of them had a significantly worse prognosis except SPP1. Using qRT-PCR, we validated that their transcriptions in our GC tumor tissue were upregulated except SPP1 and FN1, which correlated with tumor relapse and predicts poorer prognosis in GC patients.

Conclusions

We have identified 5 upregulated DEGs (HMMR, CCNB1, CXCL8, MAD2L1, and CCNA2) in GC patients with poor prognosis using integrated bioinformatical methods, which could be potential biomarkers and therapeutic targets for GC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08358-7.

Evaluation of clinical value and potential mechanism of MTFR2 in lung adenocarcinoma via bioinformatics

Background

Mitochondrial fission regulator 2 (MTFR2) was involved in the progression and development of various cancers. However, the relationship between MTFR2 with lung adenocarcinoma (LUAD) had not been reported. Herein, this study analyzed the clinical significance and potential mechanisms of MTFR2 in LUAD via bioinformatics tools.

Results

We found that the level of MTFR2 was increased, and correlated with sex, age, smoking history, neoplasm staging, histological subtype and TP53 mutation status in LUAD patients. Kaplan-Meier survival analysis showed LUAD patients with increased MTFR2 had a poor prognosis. In addition, univariate COX regression analysis showed neoplasm staging, T stage, distant metastasis and MTFR2 level were risk factors for the prognosis of LUAD. A total of 1127 genes were coexpressed with MTFR2, including 840 positive and 208 negative related genes. KEGG and GSEA found that MTFR2 participated in the progression of LUAD by affecting cell cycle, DNA replication, homologous recombination, p53 signaling pathway and other mechanisms. The top 10 coexpressed genes, namely CDK1, CDC20, CCNB1, PLK1, CCNA2, AURKB, CCNB2, BUB1B, MAD2L1 and BUB1 were highly expressed, and were associated with poor prognosis in LUAD.

Conclusions

Consequently, we elucidated MTFR2 was a biomarker for diagnosis and poor prognosis in LUAD, and might participate in the progression of LUAD via affecting cell cycle, DNA replication, homologous recombination and p53 signaling pathway.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08378-3.

Identifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction

Background

Although 1000s of immune-related and platelet receptor-related genes have been identified in lung adenocarcinoma, their role in prognosis prediction remains unclear.

Methods

We downloaded mRNA data from the Cancer Genome Atlas Dataset (TCGA), and GSE68465 or GSE14814 data sets from the Gene Expression Omnibus (GEO) database.

Results

The high-risk group’s overall survival (OS) time was lower than that of the low-risk group’s in TCGA (p = 1.15e-03). Additionally, the risk score was an independent prognostic survival factor for lung adenocarcinoma patients in TCGA (HR = 2.136, 95%CI = 1.553–2.937, p < 0.001). The model’s prognostic performance was verified with two independent GEO cohorts (GSE68465 and GSE14814). We also developed a nomogram and provided free webpage prediction tools.¹ The mechanism of the high-risk group in this risk score may be have been related to somatic mutations and copy number changes. In addition, this risk score can distinguish the prognosis of the other two cancers (ACC, p < 0.001 and KIRP, p < 0.001). Also, among the other seven cancers, the OS prognosis for high and low risk groups show wide variation (p < 0.05).

Conclusion

Our research demonstrates that CCNA2 and TGFB2 are potential diagnostic and prognostic biomarkers, as well as therapeutic targets in lung adenocarcinoma (LUAD). We also determined a novel and reliable prognostic score for lung adenocarcinoma prognosis. The online nomogram prediction tool that contains this risk score may also help clinical medical staff.

Identification of Core Genes and Pathways in Medulloblastoma by Integrated Bioinformatics Analysis

Medulloblastoma (MB) is one of the most common intracranial malignancies in children. The present study applied integrated bioinformatics to identify potential core genes associated with the pathogenesis of MB and reveal potential molecular mechanisms. Through the integrated analysis of multiple data sets from the Gene Expression Omnibus (GEO), 414 differentially expressed genes (DEGs) were identified. Combining the protein-protein interaction (PPI) network analysis with gene set enrichment analysis (GSEA), eight core genes, including CCNA2, CCNB1, CCNB2, AURKA, CDK1, MAD2L1, BUB1B, and RRM2, as well as four core pathways, including "cell cycle", "oocyte meiosis", "p53 pathway" and "DNA replication" were selected. In independent data sets, the core genes showed superior diagnostic values and significant prognostic correlations. Moreover, in the pan-caner data of the cancer genome atlas (TCGA), the core genes were also widely abnormally expressed. In conclusion, this study identified core genes and pathways of MB through integrated analysis to deepen the understanding of the molecular mechanisms underlying the MB and provide potential targets and pathways for diagnosis and treatment of MB.

Role of Sentinel Lymph Node Biopsy in Microinvasive Breast Cancer

BACKGROUND

Microinvasive ductal carcinoma (DCISM), defined as DCIS with a focus of invasive carcinoma ≤ 1 mm, can be managed similarly to pure DCIS; however, management of the axilla in DCISM has been a subject of debate. Reports in the literature differ on the utility and necessity of sentinel lymph node biopsy (SLNB) for DCISM. The aim of the present study was to identify risk factors for nodal disease in patients with DCISM, which can help develop a selective approach to SLNB in this patient population.

METHODS

The National Cancer Database was used to select patients with DCISM (pT1mi), diagnosed from 2012 to 2015, who underwent SLNB. Multivariable regression analysis was performed to determine associations between sentinel lymph node metastasis and relevant clinical variables.

RESULTS

Our cohort comprised of 2609 patients with pT1mi who underwent SLNB. Of these, 76 (2.9%) were found to have sentinel lymph node metastases on final pathology. Low/intermediate grade tumors were associated with decreased SLN metastasis (OR 0.50, CI 0.28-0.92). Age and receptor status of the tumor did not have a clear association in predicting SLN metastases.

CONCLUSIONS

The rate of sentinel node metastases in DCISM is low at only 2.9% in this national study. Tumor grade was identified as influencing the risk of SLN metastases. This information can factor into shared decision-making for SLNB in patients with DCISM.

Identification of key genes associated with progression and prognosis for lung squamous cell carcinoma

Background

Lung squamous cell carcinoma (LUSC) is a major subtype of lung cancer with limited therapeutic options and poor clinical prognosis.

Methods

Three datasets (GSE19188, GSE33532 and GSE33479) were obtained from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between LUSC and normal tissues were identified by GEO2R, and functional analysis was employed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. Protein-protein interaction (PPI) and hub genes were identified via the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Hub genes were further validated in The Cancer Genome Atlas (TCGA) database. Subsequently, survival analysis was performed using the Kapla-Meier curve and Cox progression analysis. Based on univariate and multivariate Cox progression analysis, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic value of the model.

Results

A total of 116 up-regulated genes and 84 down-regulated genes were identified. These DEGs were mainly enriched in the two pathways: cell cycle and p53 signaling way. According to the degree of protein nodes in the PPI network, 10 hub genes were identified. The mRNA expression levels of the 10 hub genes in LUSC were also significantly up-regulated in the TCGA database. Furthermore, a novel seven-gene signature (FLRT3, PPP2R2C, MMP3, MMP12, CAPN8, FILIP1 and SPP1) from the DEGs was constructed and acted as a significant and independent prognostic signature for LUSC.

Conclusions

The 10 hub genes might be tightly correlated with LUSC progression. The seven-gene signature might be an independent biomarker with a significant predictive value in LUSC overall survival.

Evaluation of the Prognostic Value of STEAP1 in Lung Adenocarcinoma and Insights Into Its Potential Molecular Pathways via Bioinformatic Analysis

Background

Upregulation of the six-transmembrane epithelial antigen of prostate-1 (STEAP1) is closely associated with prognosis of numerous malignant cancers. However, its role in lung adenocarcinoma (LUAD), the most common type of lung cancer, remains unknown. This study aimed to investigate the role of STEAP1 in the occurrence and progression of LUAD and the potential mechanisms underlying its regulatory effects.

Methods

STEAP1 mRNA and protein expression were analyzed in 40 LUAD patients via real-time PCR and western blotting, respectively. We accessed the clinical data of 522 LUAD patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to investigate the expression and prognostic role of STEAP1 in LUAD. Further, we performed gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) to elucidate the potential mechanism underlying the role of STEAP1 in LUAD. The protein-protein interaction (PPI) network of STEAP1 was analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and hub genes with significant positive and negative associations with STEAP1 were identified and their role in LUAD prognosis was predicted.

Results

STEAP1 was significantly upregulated in LUAD patients and associated with LUAD prognosis. Further, TCGA data indicated that STEAP1 upregulation is correlated with the clinical prognosis of LUAD. GO and KEGG analysis revealed that the genes co-expressed with STEAP1 were primarily involved in cell division, DNA replication, cell cycle, apoptosis, cytokine signaling, NF-kB signaling, and TNF signaling. GSEA revealed that homologous recombination, p53 signaling pathway, cell cycle, DNA replication, apoptosis, and toll-like receptor signaling were highly enriched upon STEAP1 upregulation. Gene Expression Profiling Interactive Analysis (GEPIA) analysis revealed that the top 10 hub genes associated with STEAP1 expression were also associated with the LUAD prognosis.

Conclusion

STEAP1 upregulation potentially influences the occurrence and progression of LUAD and its co-expressed genes via regulation of homologous recombination, p53 signaling, cell cycle, DNA replication, and apoptosis. STEAP1 is a potential prognostic biomarker for LUAD.

Integrated bioinformatics analysis of expression, related signal pathways, and prognostic significance of CCNA2 in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most diagnosed malignant carcinomas of the digestive system with a poor prognosis. In this study, the expression of CCNA2 gene expression in HCC was investigated by bioinformatics analysis and the feasibility of CCNA2 gene as a molecular marker for prognosis in HCC was assessed. The results of bioinformatics analysis were verified by immunohistochemistry assay.

AIM

To investigate the expression of CCNA2 in HCC, related signal pathways, and its relationship with prognosis.

METHODS

The expression levels of CCNA2 mRNA in HCC and paracancerous tissues were compared based on the TCCA database. CCNA2 protein interaction network was constructed based on the String database, and the related protein function and KEGG signal pathways were enriched. According to the expression level of CCNA2, the patients were divided into high and low expression groups, and the difference in overall survival (OS) and disease free survival (DFS) was compared between the two groups. The relationship between the expression of CCNA2 protein analyzed by immunohistochemistry and the clinicopathological features of 72 patients with HCC who underwent surgical treatment was analyzed.

RESULTS

The expression level of CCNA2 mRNA was significantly higher in HCC than in normal liver tissues. There are ten proteins that have a close interaction with CCNA2 protein, with edge = 50, and the regional clustering index is 0.931. The interaction network of the ten proteins that have a close interaction with CCNA2 protein was significantly enriched (P < 0.05). The expression of TOP2 mRNA was positively correlated with CCNA2 expression (r = 0.85, P < 0.05), while CCL14 expression was negatively correlated with CCNA2 expression (r = -0.54, P < 0.05). CCNA2 gene related signaling pathways are mainly enriched in cell cycle, viral carcinogenesis, hepatitis B, p53 signaling pathway, and PI3K-Akt signaling pathway. The prognosis analysis indicated that the OS (HR = 1.7, P = 0.0037) and DFS rates were significantly lower in the high expression group than in the low expression group (HR = 1.6, P = 0.0037). Immunohistochemistry showed that the high expression rate of CCNA2 in HCC was 34.7% (25/72). High expression of CCNA2 protein was significantly associated with tumor diameter (P < 0.05), DC infiltration (P < 0.05), and recurrence/metastasis 2 years after operation (P < 0.05).

CONCLUSION

CCNA2 gene is up-regulated in HCC, which can be used as a molecular marker for poor prognosis in HCC.

Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer

Background

Non-small cell lung cancer (NSCLC) is the main histologic form of lung cancer that affects human health, but biomarkers for therapeutic diagnosis and prognosis of the disease are currently lacking.

Material/Methods

The gene expression profile GSE18842 was downloaded from the Gene Expression Omnibus database in this prospective study, which consisted of 46 tumors and 45 controls. After screening differentially expressed genes (DEGs), we conducted functional enrichment analysis and KEGG analysis with upregulated differentially expressed genes (uDEGs) and downregulated differentially expressed genes (dDEGs), respectively. Protein–protein interaction (PPI) networks among DEGs and corresponding coding protein complexes, constructed using the STRING database, were analyzed using Cytoscape. Kaplan-Meier method was used to verify survival associated with hub genes. The GEPIA webserver was used to plot the gene expression level heat map of hub genes between NSCLC and adjacent lung tissues in the TCGA database.

Results

We identified 368 DEGs (168 uDEGs and 200 dDEGs) in NSCLC samples relative to control samples after gene integration. We established a PPI network for the DEGs, which had 249 nodes and 1472 edges protein pairs. Ten undefined hub genes with the highest connectivity degree (CDK1, UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11) were verified by survival analysis, and 9 of them were associated with poorer overall survival in NSCLC. The expression reliability of hub genes was verified by use of the GEPIA web tool.

Conclusions

The results suggested that UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11 are inherent key biomarkers for diagnosis and prognosis, while KEGG analysis results showed the mitotic cell cycle pathway is a probable signaling pathway contributing to NSCLC progression. These genes could be promising biomarkers for diagnosis and provide a new approach for developing targeted therapeutic NSCLC drugs.

Clinical significance of PD-L1-positive cancer-associated fibroblasts in pN0M0 non-small cell lung cancer

OBJECTIVES

Cancer-associated fibroblasts (CAFs) are a dominant cell type in tumor stroma and support the generation of pro-tumorigenic microenvironment. CAFs have frequent opportunities to interact with immune cells infiltrating the tumor stroma, but the process remains to be determined. In this study, we focused on immune checkpoint mechanism. We also examined the induction of programmed cell death-ligand 1 (PD-L1) on CAFs by immune cell, and the clinical significance of PD-L1-expressed CAFs in non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

CAFs were isolated from human NSCLC tissues, and PD-L1 expression levels in CAFs were analyzed by real-time polymerase chain reaction and flow-cytometry. Following immunohistochemical analysis of PD-L1 in surgically resected pN0M0 NSCLC (n = 125, including 88 invasive adenocarcinomas and 37 squamous cell carcinomas), the correlation of PD-L1-positive CAFs with clinicopathological features was investigated.

RESULTS

PD-L1 mRNA and protein expression on CAFs was upregulated by exogenously supplemented interferon-gamma (IFN-γ) and downregulated through the depletion of IFN-γ. PD-L1 expression on CAFs was upregulated by co-culture with activated lymphocytes releasing IFN-γ. Immunohistochemistry revealed that PD-L1-positive CAFs were observed in 31 cases (24.8%). Postoperative relapse-free survival was significantly prolonged in patients with PD-L1-positive CAFs as compared with those with PD-L1-negative CAFs, with 5-year relapse-free probabilities of 84.5% and 66.3%, respectively (P = 0.031). Multivariate analysis revealed that PD-L1 expression on CAFs was an independent prognostic factor of longer relapse-free survival after surgery (hazard ratio: 3.225, P = 0.027).

CONCLUSION

PD-L1 expression on CAFs is reversibly regulated by environmental stimuli including IFN-γ from activated lymphocytes. In the non-metastatic NSCLC, PD-L1 expression on CAFs suggests the induction of anti-tumor immune responses, contributing to better prognosis after surgery.

Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis

Background and Objective: Non-small cell lung cancer (NSCLC) accounts for 80-85% of all patients with lung cancer and 5-year relative overall survival (OS) rate is less than 20%, so that identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study attempted to identify potential key genes associated with the pathogenesis and prognosis of NSCLC. Methods: Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NSCLC samples and normal ones were analyzed using limma package, and RobustRankAggreg (RRA) package was used to conduct gene integration. Moreover, Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE) were utilized to establish protein-protein interaction (PPI) network of these DEGs. Furthermore, functional enrichment and pathway enrichment analyses for DEGs were performed by Funrich and OmicShare. While the expressions and prognostic values of top genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online dataset. Results: A total of 249 DEGs (113 upregulated and 136 downregulated) were identified after gene integration. Moreover, the PPI network was established with 166 nodes and 1784 protein pairs. Topoisomerase II alpha (TOP2A), a top gene and hub node with higher node degrees in module 1, was significantly enriched in mitotic cell cycle pathway. In addition, Interleukin-6 (IL-6) was enriched in amb2 integrin signaling pathway. The mitotic cell cycle was the most significant pathway in module 1 with the highest P-value. Besides, five hub genes with high degree of connectivity were selected, including TOP2A, CCNB1, CCNA2, UBE2C, and KIF20A, and they were all correlated with worse OS in NSCLC. Conclusion: The results showed that TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 may be potential key genes, while the mitotic cell cycle pathway may be a potential pathway contribute to progression in NSCLC. Further, it could be used as a new biomarker for diagnosis and to direct the synthesis medicine of NSCLC.

Clinical significance and biological roles of cyclins in gastric cancer

Background and aim

Cyclins have been reported to be overexpressed with poor prognosis in several human cancers. However, limited numbers of studies evaluated the expressions and prognostic roles of cyclins in gastric cancer (GC). We aim to evaluate the expressions and prognostic roles of cyclins. Also, further efforts were made to explore biological function of the differentially expressed cyclins.

Methods

Cyclins expressions were analyzed by Oncomine and The Cancer Genome Atlas datasets, and the prognostic roles of cyclins in GC patients were investigated by the Kaplan–Meier Plotter database. Then, a comprehensive PubMed literature search was performed to identify expression and prognosis of cyclins in GC. Biological functions of the differentially expressed cyclins were explored through Enrich R platform, and KEGG and transcription factor were analyzed.

Results

The expression levels of CCNA2 (cyclin A2), CCNB1 (cyclin B1), CCNB2 (cyclin B2), and CCNE1 (cyclin E1) mRNAs were identified to be significantly higher in GC tissues than in normal tissues in both Oncomine and The Cancer Genome Atlas datasets. High expressions of CCNA2, CCNB1, and CCNB2 mRNAs were identified to be related with poor overall survival in Kaplan–Meier Plotter dataset. Evidence from clinical studies showed that CCNB1 was related with overall survival in GC patients. Cyclins were associated with several biological pathways, including cell cycle, p53 signaling pathway, FoxO signaling pathway, viral carcinogenesis, and AMPK signaling pathway. Enrichment analysis also showed that cyclins interacted with some certain transcription factors, such as FOXM1, SIN3A, NFYA, and E2F4.

Conclusion

Based on our results, high expressions of cyclins were related with poor prognosis in GC patients. The above information might be useful for better understanding the clinical and biological roles of cyclins mRNA and guiding individualized treatments for GC patients.

Identification of key genes in glioblastoma-associated stromal cells using bioinformatics analysis

The aim of the present study was to identify key genes and pathways in glioblastoma-associated stromal cells (GASCs) using bioinformatics. The expression profile of microarray GSE24100 was obtained from the Gene Expression Omnibus database, which included the expression profile of 4 GASC samples and 3 control stromal cell samples. Differentially expressed genes (DEGs) were identified using limma software in R language, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery software. In addition, a protein-protein interaction (PPI) network was constructed. Subsequently, a sub-network was constructed to obtain additional information on genes identified in the PPI network using CFinder software. In total, 502 DEGs were identified in GASCs, including 331 upregulated genes and 171 downregulated genes. Cyclin-dependent kinase 1 (CDK1), cyclin A2, mitotic checkpoint serine/threonine kinase (BUB1), cell division cycle 20 (CDC20), polo-like kinase 1 (PLK1), and transcription factor breast cancer 1, early onset (BRCA1) were identified from the PPI network, and sub-networks revealed these genes as hub genes that were involved in significant pathways, including mitotic, cell cycle and p53 signaling pathways. In conclusion, CDK1, BUB1, CDC20, PLK1 and BRCA1 may be key genes that are involved in significant pathways associated with glioblastoma. This information may lead to the identification of the mechanism of glioblastoma tumorigenesis.

High expression of Bcl-2 protein predicts favorable outcome in non-small cell lung cancer: evidence from a systematic review and meta-analysis

BACKGROUND

The prognostic value of Bcl-2 protein expression in non-small cell lung cancer (NSCLC) is under debate. We therefore systematically reviewed the evidence for Bcl-2 protein effects on NSCLC survival to elucidate this issue.

MATERIALS AND METHODS

An electronic search in Pubmed and Embase complemented by manual searches in article references were conducted to identify eligible studies to evaluate the association between Bcl-2 protein expression and overall survival (OS) as well as disease free survival (DFS) of NSCLC patients. Combined hazard ratios (HRs) with corresponding 95% confidence intervals (95%CIs) were pooled using the random-effects model.

RESULTS

A total of 50 trials (including 52 cohorts) encompassing 7,765 patients were pooled in the meta-analysis regarding Bcl-2 expression and OS of NSCLC patients. High expression of Bcl-2 protein had a favorable impact (HR=0.76, 95%CI=0.67-0.86). In the group of Bcl-2 expression and DFS, 11 studies including 2,634 patients were included. The synthesized result indicated high expression of Bcl-2 protein might predict good DFS (HR=0.85, 95%CI=0.75-0.95).

CONCLUSIONS

Our present meta-analysis demonstrated favorable prognostic values of Bcl-2 expression in patients with NSCLC. Further prospective trails are welcomed to validate the utility of assessing Bcl-2 in NSCLC patient management.

CCNA2 is a prognostic biomarker for ER+ breast cancer and tamoxifen resistance

Identification of effective prognostic biomarkers and targets are of crucial importance to the management of estrogen receptor positive (ER+) breast cancer. CCNA2 (also known as CyclinA2) belongs to the highly conserved cyclin family and is significantly overexpressed in various cancer types. In this study, we demonstrated that CCNA2 had significant predictive power in distant metastasis free survival, disease free survival, recurrence free survival and overall survival of ER+ breast cancer patients. We also found that CCNA2 was closely associated with tamoxifen resistance. In addition, gene set enrichment analysis (GSEA) revealed that its expression was positively associated with genes overexpressed in endocrine therapy resistant samples. Finally, though CCNA2-Drug interaction network, we demonstrated the interactions between CCNA2 and several available cancer drugs. Overall, we suggest that CCNA2 is a biomarker for the prognosis of ER+ breast cancer and monitoring of tamoxifen efficacy. It's also a promising target for developing new strategies to prevent or even reverse tamoxifen resistance. Moreover, CCNA2 expression may help monitoring tamoxifen efficacy and directing personalized therapies. Nevertheless, in vivo and in vitro experiments and multi-center randomized controlled clinical trials are still needed before its application in clinical settings.

The antiproliferative effect of C2-ceramide on lung cancer cells through apoptosis by inhibiting Akt and NFκB

The anticancer effects of ceramide have been reported in many types of cancers but less in lung cancer. In this study, we used C₂-ceramide to further investigate its possible anticancer effects and mechanisms on non-small cell lung cancer (NSCLC) H1299 cells. The result of cell proliferation in terms of trypan blue assay showed high dose of C₂-ceramide inhibited cell survival after 24 h treatment. The flow cytometry-based assays indicated the effect of apoptosis, chromatin condensation, and G₁ arrest in terms of Annexin V/propidium iodide (PI), DAPI, and PI stainings, respectively. Moreover, the decreased protein level of p-Akt, p-NFκB, survivin and cyclin A2 were detected by Western blot assay. Taken together, these results indicated the antiproliferative effect of C₂-ceramide is majorly responsible for cell apoptosis in lung cancer H1299 cells.