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

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560-680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

Characterization of runs of homozygosity islands in American mink using whole-genome sequencing data

The genome-wide analysis of runs of homozygosity (ROH) islands can be an effective strategy for identifying shared variants within a population and uncovering important genomic regions related to complex traits. The current study performed ROH analysis to characterize the genome-wide patterns of homozygosity, identify ROH islands and annotated genes within these candidate regions using whole-genome sequencing data from 100 American mink (Neogale vison). After sequence processing, variants were called using GATK and Samtools pipelines. Subsequent to quality control, 8,373,854 bi-allelic variants identified by both pipelines remained for further analysis. A total of 34,652 ROH segments were identified in all individuals, among which shorter segments (0.3-1 Mb) were abundant throughout the genome, approximately accounting for 84.39% of all ROH. Within these segments, we identified 63 ROH islands housing 156 annotated genes. The genes located in ROH islands were associated with fur quality (EDNRA, FGF2, FOXA2 and SLC24A4), body size/weight (MYLK4, PRIM2, FABP2, EYS and PHF3), immune capacity (IL2, IL21, PTP4A1, SEMA4C, JAK2, CCNA2 and TNIP3) and reproduction (ADAD1, KHDRBS2, INSL6, PGRMC2 and HSPA4L). Furthermore, Gene Ontology and KEGG pathway enrichment analyses revealed 56 and 9 significant terms (FDR-corrected p-value < 0.05), respectively, among which cGMP-PKG signalling pathway, regulation of actin cytoskeleton, and calcium signalling pathway were highlighted due to their functional roles in growth and fur characteristics. This is the first study to present ROH islands in American mink. The candidate genes from ROH islands and functional enrichment analysis suggest possible signatures of selection in response to the mink breeding targets, such as increased body length, reproductive performance and fur quality. These findings contribute to our understanding of genetic characteristics, and provide complementary information to assist with implementation of breeding strategies for genetic improvement in American mink.

Identification of formononetin as the active compound of CR-SR in hepatocellular carcinoma treatment: An integrated approach combining network pharmacology and weighted gene co-expression networks

Hepatocellular carcinoma (HCC) is a life-threatening disease for which there is no cure. Traditional Chinese medicine is a treasure trove of Medicinals that has been used for thousands of years. In China, the traditional herb pair, Curcumae Rhizoma and Sparganii Rhizoma (CR-SR) represent a classic herbal combination used for the treatment of HCC. However, the drug targets and pharmacological mechanism of action of CR-SR in the treatment of HCC are unclear. To address this, we screened the active components and drug targets of CR-SR from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and a high-throughput experiment- and reference-guided database of traditional Chinese medicines (HERB database). Combined with the weighted co-expression network analysis of dataset GSE76427, we constructed an active component-target-disease regulatory network. It was found that CR-SR's active components for HCC treatment included trans-gondoic acid, beta-sitosterol, stigmasterol, hederagenin, and formononetin. These compounds specifically targeted the genes Estrogen Receptor 1 (ESR1), Cyclin A2 (CCNA2), Checkpoint Kinase 1 (CHEK1), and Nuclear Receptor Coactivator 2 (NCOA2). ESR1, CCNA2, and CHEK1 genes showed significant differences in survival prognosis, expression levels, and statistical significance during the pathological stage. Moreover, their high affinity for formononetin was determined through molecular docking analysis. Cell assays and high-throughput sequencing were performed to reveal that the inhibitory effect of formononetin on HepG2 cell proliferation was related to hepatocyte metabolism and cell cycle regulation-related pathways. This study provides insights into potential HCC treatments.

A Novel Mathematical Approach for Analysis of Integrated Cell-Patient Data Uncovers a 6-Gene Signature Linked to Endocrine Therapy Resistance

A significant number of breast cancers develop resistance to hormone therapy. This progression, while posing a major clinical challenge, is difficult to predict. Despite important contributions made by cell models and clinical studies to tackle this problem, both present limitations when taken individually. Experiments with cell models are highly reproducible but do not reflect the indubitable heterogenous landscape of breast cancer. On the other hand, clinical studies account for this complexity but introduce uncontrolled noise due to external factors. Here, we propose a new approach for biomarker discovery that is based on a combined analysis of sequencing data from controlled MCF7 cell experiments and heterogenous clinical samples that include clinical and sequencing information from The Cancer Genome Atlas. Using data from differential gene expression analysis and a Bayesian logistic regression model coupled with an original simulated annealing-type algorithm, we discovered a novel 6-gene signature for stratifying patient response to hormone therapy. The experimental observations and computational analysis built on independent cohorts indicated the superior predictive performance of this gene set over previously known signatures of similar scope. Together, these findings revealed a new gene signature to identify patients with breast cancer with an increased risk of developing resistance to endocrine therapy.

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.

Prognostic model based on telomere-related genes predicts the risk of oral squamous cell carcinoma

BACKGROUND

This study investigated a potential prognostic model based on telomere-related genes (TRGs) for the clinical prediction of oral squamous cell carcinoma (OSCC).

METHODS

Gene expression data and associated clinical phenotypes were obtained from online databases. Differentially expressed (DE)-TRGs were identified between OSCC and normal samples, followed by protein-protein interaction and enrichment analyses. Subsequently, the prognostic genes explored based on the DE-TRGs and survival data were applied in the establishment of the current prognostic model, and an integrated analysis was performed between high- and low-risk groups using a prognostic model. The expression of certain prognostic genes identified in the present study was validated using qPCR analysis and/or western blot in OSCC cell lines and clinical samples.

RESULTS

169 DE-TRGs were identified between the OSCC samples and controls. DE-TRGs are mainly involved in functions such as hypoxia response and pathways such as the cell cycle. Eight TRGs (CCNB1, PDK4, PLOD2, RACGAP1, MET, PLK1, KPNA2, and CCNA2) associated with OSCC survival and prognosis were used to construct a prognostic model. qPCR analysis and western blot showed that most of the eight prognostic genes were consistent with the current bioinformatics results. Analysis of the high- and low-risk groups for OSCC determined by the prognostic model showed that the current prognostic model was reliable.

CONCLUSIONS

A novel prognostic model for OSCC was constructed by TRGs. PLOD2 and APLK1 may participate in the progression of OSCC via responses to hypoxia and cell cycle pathways, respectively. TRGs, including KPNA2 and CCNA2, may serve as novel prognostic biomarkers for OSCC.

Network and functional analyses of differentially expressed genes in gastric cancer provide new biomarkers associated with disease pathogenesis

BACKGROUND

Gastric cancer is a dominant source of cancer-related death around the globe and a serious threat to human health. However, there are very few practical diagnostic approaches and biomarkers for the treatment of this complex disease.

METHODS

This study aimed to evaluate the association between differentially expressed genes (DEGs), which may function as potential biomarkers, and the diagnosis and treatment of gastric cancer (GC). We constructed a protein-protein interaction network from DEGs followed by network clustering. Members of the two most extensive modules went under the enrichment analysis. We introduced a number of hub genes and gene families playing essential roles in oncogenic pathways and the pathogenesis of gastric cancer. Enriched terms for Biological Process were obtained from the "GO" repository.

RESULTS

A total of 307 DEGs were identified between GC and their corresponding normal adjacent tissue samples in GSE63089 datasets, including 261 upregulated and 261 downregulated genes. The top five hub genes in the PPI network were CDK1, CCNB1, CCNA2, CDC20, and PBK. They are involved in focal adhesion formation, extracellular matrix remodeling, cell migration, survival signals, and cell proliferation. No significant survival result was found for these hub genes.

CONCLUSIONS

Using comprehensive analysis and bioinformatics methods, important key pathways and pivotal genes related to GC progression were identified, potentially informing further studies and new therapeutic targets for GC treatment.

Comprehensive analysis of the expression and prognosis for RAI2: A promising biomarker in breast cancer

Introduction

Retinoic acid-induced 2 (RAI2) was initially related to cell differentiation and induced by retinoic acid. RAI2 has been identified as an emerging tumor suppressor in breast cancer and colorectal cancer.

Methods

In this study, we performed systematic analyses of RAI2 in breast cancer. Meta-analysis and Kaplan-Meier survival curves were applied to identify the survival prediction potential of RAI2. Moreover, the association between RAI2 expression and the abundance of six tumor-infiltrating immune cells was investigated by TIMER, including B cells, CD8+ T cells, CD4+ T cells, B cells, dendritic cells, neutrophils, and macrophages. The expression profiles of high and low RAI2 mRNA levels in GSE7390 were compared to identify differentially expressed genes (DEGs) and the biological function of these DEGs was analyzed by R software, which was further proved in GSE7390.

Results

Our results showed that the normal tissues had more RAI2 expression than breast cancer tissues. Patients with high RAI2 expression were related to a favorable prognosis and more immune infiltrates. A total of 209 DEGs and 182 DEGs were identified between the expression profiles of high and low RAI2 mRNA levels in the GSE7390 and GSE21653 databases, respectively. Furthermore, Gene Ontology (GO) enrichment indicated that these DEGs from two datasets were both mainly distributed in “biological processes” (BP), including “organelle fission” and “nuclear division”. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis demonstrated that these DEGs from two datasets were both significantly enriched in the “cell cycle”. Common hub genes between the DEGs in GSE7390 and GSE21653 were negatively associated with RAI2 expression, including CCNA2, MAD2L1, MELK, CDC20, and CCNB2.

Discussions

These results above suggested that RAI2 might play a pivotal role in preventing the initiation and progression of breast cancer. The present study may contribute to understanding the molecular mechanisms of RAI2 and enriching biomarkers to predict patient prognosis in breast cancer.

Identification of Prognostic Biomarkers for Suppressing Tumorigenesis and Metastasis of Hepatocellular Carcinoma through Transcriptome Analysis

Cancer is one of the deadliest diseases developed through tumorigenesis and could be fatal if it reaches the metastatic phase. The novelty of the present investigation is to explore the prognostic biomarkers in hepatocellular carcinoma (HCC) that could develop glioblastoma multiforme (GBM) due to metastasis. The analysis was conducted using RNA-seq datasets for both HCC (PRJNA494560 and PRJNA347513) and GBM (PRJNA494560 and PRJNA414787) from Gene Expression Omnibus (GEO). This study identified 13 hub genes found to be overexpressed in both GBM and HCC. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation, causing aneuploidy. A 13-gene predictive model was obtained and validated using a KM plot. These hub genes could be prognostic biomarkers and potential therapeutic targets, inhibition of which could suppress tumorigenesis and metastasis.

Phytocompounds From Edible Oil Seeds Target Hub Genes To Control Breast Cancer

Breast cancer is one of the most commonly diagnosed cancers in woman which accounts for more than 1 in 10 new cancers in the entire world. The recently found four new potential hub genes that show a strong expression in breast cancer are CCNA2, CCNB1, MAD2L1, and RAD51. Nowadays, food habits and lifestyle of an individual are one of the factors for causing cancers. Consumption of seeds on a regular basis is the key factor for leading a good health. Sesame seeds and Sunflower seeds are few examples of cancer fighting seeds. Sesame (Sesamum indicum) is one of the earliest oil seed plant with various phytocompounds present which include lignans, tocopherols, phenolics, polyunsaturated fatty acids, and phytosterols. Sunflower (Helianthus annuus L.) is primarily harvested as an oil seed plant with various phytocompounds present which include flavonoids, phenolic acids, tocopherols, and vitamin B3. These are the few seeds that help women to prevent and also to fight against Breast cancer with its potential anti-cancer activity. The main objective of the current study is to identify the potential phytocompounds present in the cancer fighting seeds using molecular docking and dynamic simulation approach which can further help pharmaceuticals industries in producing targeted drugs against breast cancer hub genes as well as food industries in producing products combining the potential phytocompounds present in the seeds.

Eight Aging-Related Genes Prognostic Signature for Cervical Cancer

This study searched for aging-related genes (ARGs) to predict the prognosis of patients with cervical cancer (CC). All data were obtained from Molecular Signatures Database, Cancer Genome Atlas, Gene Expression Integration, and Genotype Organization Expression. The R software was used to screen out the differentially expressed ARGs (DE-ARGs) between CC and normal tissues. A protein-protein interaction network was established by the DE-ARGs. The univariate and multivariate Cox regression analyses were conducted on the first extracted Molecular Complex Detection component, and a prognostic model was constructed. The prognostic model was further validated in the testing set and GSE44001 dataset. Prognosis was analyzed by Kaplan-Meier curves, and accuracy of the prognostic model was assessed by receiver operating characteristic area under the curve analysis. An independent prognostic analysis of risk score and some clinicopathological factors of CC was also performed. The copy-number variant (CNV) and single-nucleotide variant (SNV) of prognostic ARGs were analyzed by the BioPortal database. A clinical practical nomogram was established to predict individual survival probability. Finally, we carried out cell experiment to further verify the prognostic model. An eight-ARG prognostic signature for CC was constructed. High-risk CC patients had significantly shorter overall survival than low-risk patients. The receiver operating characteristic (ROC) curve validated the good performance of the signature in survival prediction. The Figo_stage and risk score served as independent prognostic factors. The eight ARGs mainly enriched in growth factor regulation and cell cycle pathway, and the deep deletion of FN1 was the most common CNV. An eight-ARG prognostic signature for CC was successfully constructed.

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.

A network-based dynamic criterion for identifying prediction and early diagnosis biomarkers of complex diseases

Lung adenocarcinoma (LUAD) seriously threatens human health and generally results from dysfunction of relevant module molecules, which dynamically change with time and conditions, rather than that of an individual molecule. In this study, a novel network construction algorithm for identifying early warning network signals (IEWNS) is proposed for improving the performance of LUAD early diagnosis. To this end, we theoretically derived a dynamic criterion, namely, the relationship of variation (RV), to construct dynamic networks. RV infers correlation [Formula: see text] statistics to measure dynamic changes in molecular relationships during the process of disease development. Based on the dynamic networks constructed by IEWNS, network warning signals used to represent the occurrence of LUAD deterioration can be defined without human intervention. IEWNS was employed to perform a comprehensive analysis of gene expression profiles of LUAD from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The experimental results suggest that the potential biomarkers selected by IEWNS can facilitate a better understanding of pathogenetic mechanisms and help to achieve effective early diagnosis of LUAD. In conclusion, IEWNS provides novel insight into the initiation and progression of LUAD and helps to define prospective biomarkers for assessing disease deterioration.

Identification of biomarkers and key pathways in synovial sarcoma cells exposed to anlotinib by integrating bioinformatics analysis and experimental validation

OBJECTIVE

To identify potential biomarkers, key pathways and modules following the exposure of synovial sarcoma (SS) cells to anlotinib.

METHODS

In the current study, we integrated multiple bioinformatics methods to identify the hub genes and key pathways associated with the effects of anlotinib treatment in SS cells. In addition, we used reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) to validate the expression levels of the identified hub genes in SS cells treated with anlotinib.

RESULTS

In total, 183 differentially expressed genes (DEGs) were identified, of which 47 were upregulated and 136 were downregulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that the DEGs were predominantly involved in cell division and cell cycle progression. A total of two modules were identified from the protein-protein interaction network using the MCODE plugin in Cytoscape, where module 1 was the most significant. By combining the results of CytoHubba analysis based on the module 1 and The Cancer Genome Atlas database, six real hub genes, cyclin (CCN) A2, kinesin family member 2C, cell division cycle 20, CCNB2, aurora kinase B and CCNB1, were identified. Subsequent GO and KEGG pathway analysis revealed that these six real hub genes were significantly associated with the cell cycle and mitosis. Finally, RT-qPCR verified that the mRNA expression levels of these six real hub genes were significantly decreased in SS cells treated with anlotinib compared with those in the control group. Altogether, our study identified biomarkers and key pathways associated with the effects of anlotinib treatment in SS cells, which may provide novel insights into the underlying mechanism of anlotinib treatment in SS.

Curcumin alters distinct molecular pathways in breast cancer subtypes revealed by integrated miRNA/mRNA expression analysis

BACKGROUND

Curcumin is well known for its anticancer properties. Its cytotoxic activity has been documented in several cancer cell lines, including breast cancer. The pleiotropic activity of curcumin as an antioxidant, an antiangiogenic, antiproliferative, and pro-apoptotic, is due to its diverse targets, such as signaling pathways, protein/enzyme, or noncoding gene.

AIM

This study aimed to identify key miRNAs and mRNAs induced by curcumin in breast cancer cells MCF7, T47D (hormone positive), versus MDA-MB231 (hormone negative) using comparative analysis of global gene expression profiles.

METHODS

RNA was isolated and subjected to mRNA and miRNA library sequencing to study the global gene expression profile of curcumin-treated breast cancer cells. The differential expression of gene and miRNA was performed using the DESeq R package. The enriched pathways were studied using cluster profileR, and integrated miRNA-mRNA analysis was carried out using miRtarvis and miRmapper tools.

RESULTS

Curcumin treatment led to upregulation of 59% TSGs in MCF7, 21% in MDA-MB-231 cells, and 36% TSGs in T47D, and downregulation of 57% oncogenes in MCF7, 76% in MDA-MB-231, and 91% in T47D. Similarly, curcumin treatment led to upregulation of 32% TSmiRs in MCF7, 37.5% in MDA-MB231, and 62.5% in T47D, and downregulation of 77% oncomiRs in MCF7, 50% in MDA-MB231 and 28.6% in T47D. Integrated analysis of miRNA-mRNA led to the identification of a common NFKB pathway altered by curcumin in all three cell lines. Analysis of uniquely enriched pathway revealed non-integrin membrane-ECM interactions and laminin interactions in MCF7; extracellular matrix organization and degradation in MDA-MB-231 and cell cycle arrest and G2/M transition in T47D.

CONCLUSION

Curcumin regulates miRNA and mRNA in a cell type-specific manner. The integrative analysis led to the detection of miRNAs and mRNAs pairs, which can be used as biomarkers associated with carcinogenesis, diagnostic, and treatment response in breast cancer.

Identification of therapeutically potential targets and their ligands for the treatment of OSCC

Recent advancements in cancer biology have revealed molecular changes associated with carcinogenesis and chemotherapeutic exposure. The available information is being gainfully utilized to develop therapies targeting specific molecules involved in cancer cell growth, survival, and chemoresistance. Targeted therapies have dramatically increased overall survival (OS) in many cancers. Therefore, developing such targeted therapies against oral squamous cell carcinoma (OSCC) is anticipated to have significant clinical implications. In the current work, we have identified drug-specific sensitivity-related prognostic biomarkers (BOP1, CCNA2, CKS2, PLAU, and SERPINE1) using gene expression, Cox proportional hazards regression, and machine learning in OSCC. Dysregulation of these markers is significantly associated with OS in many cancers. Their elevated expression is related to cellular proliferation and aggressive malignancy in various cancers. Mechanistically, inhibition of these biomarkers should significantly reduce cellular proliferation and metastasis in OSCC and should result in better OS. It is pertinent to note that no effective small-molecule candidate has been identified against these biomarkers to date. Therefore, a comprehensive in silico drug design strategy assimilating homology modeling, extensive molecular dynamics (MD) simulation, and ensemble molecular docking has been applied to identify potential compounds against identified targets, and potential molecules have been identified. We hope that this study will help in deciphering potential genes having roles in chemoresistance and a significant impact on OS. It will also result in the identification of new targeted therapeutics against OSCC.

Construction of a Novel Oxidative Stress Response-Related Gene Signature for Predicting the Prognosis and Therapeutic Responses in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor outcomes, and the assessment of its prognosis as well as its response to therapy is still challenging. In this study, we aimed to construct an oxidative stress response-related genes-(OSRGs-) based gene signature for predicting prognosis and estimating treatment response in patients with HCC. We integrated the transcriptomic data and clinicopathological information of HCC patients from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. LASSO Cox regression analysis was utilized to establish an integrated multigene signature in the TCGA cohort, and its prediction performance was validated in the ICGC cohort. The CIBERSORT algorithm was employed to evaluate immune cell infiltration. The response rate to immune checkpoint inhibition (ICI) therapy was assessed using a TIDE platform. Drug activity data from the Cancer Genome Project and NCI-60 human cancer cell lines were used to predict sensitivity to chemotherapy. We successfully established a gene signature comprising G6PD, MT3, CBX2, CDKN2B, CCNA2, MAPT, EZH2, and SLC7A11. The risk score of each patient, which was determined by the multigene signature, was identified as an independent prognostic marker. The immune cell infiltration patterns, response rates to ICI therapy, and the estimated sensitivity of 89 chemotherapeutic drugs were associated with risk scores. Individual prognostic genes were also associated with susceptibility to various FDA-approved drugs. Our study indicates that a comprehensive transcriptomic analysis of OSRGs can provide a reliable molecular model to predict prognosis and therapeutic response in patients with HCC.

Improved prediction of gene expression through integrating cell signalling models with machine learning

Background

A key problem in bioinformatics is that of predicting gene expression levels. There are two broad approaches: use of mechanistic models that aim to directly simulate the underlying biology, and use of machine learning (ML) to empirically predict expression levels from descriptors of the experiments. There are advantages and disadvantages to both approaches: mechanistic models more directly reflect the underlying biological causation, but do not directly utilize the available empirical data; while ML methods do not fully utilize existing biological knowledge.

Results

Here, we investigate overcoming these disadvantages by integrating mechanistic cell signalling models with ML. Our approach to integration is to augment ML with similarity features (attributes) computed from cell signalling models. Seven sets of different similarity feature were generated using graph theory. Each set of features was in turn used to learn multi-target regression models. All the features have significantly improved accuracy over the baseline model - without the similarity features. Finally, the seven multi-target regression models were stacked together to form an overall prediction model that was significantly better than the baseline on 95% of genes on an independent test set. The similarity features enable this stacking model to provide interpretable knowledge about cancer, e.g. the role of ERBB3 in the MCF7 breast cancer cell line.

Conclusion

Integrating mechanistic models as graphs helps to both improve the predictive results of machine learning models, and to provide biological knowledge about genes that can help in building state-of-the-art mechanistic models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04787-8.

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.

CCNA2 as an Immunological Biomarker Encompassing Tumor Microenvironment and Therapeutic Response in Multiple Cancer Types

Background

Cancer is a major threat to human health worldwide. Although recent innovations and advances in early detection and effective therapies such as targeted drugs and immune checkpoint inhibitors have saved more lives of cancer patients and improved their quality of life, our knowledge about cancer remains largely unknown. CCNA2 belongs to the cell cyclin family and has been demonstrated to be a tumorigenic gene in multiple solid tumor types. The aim of the present study was to make a comprehensive analysis on the role of CCNA2 at a pancancer level.

Methods

Multidatabases were collected to evaluate the different expression, prognostic value, DNA methylation, tumor mutation burden, microsatellite instability, mismatch repair, tumor immune microenvironment, and drug sensitivity of CCNA2 across pancancer. IHC was utilized to validate the expression and prognostic value of CCNA2 in ccRCC patients from SMMU cohort.

Results

CCNA2 was differentially expressed in most cancer types vs. normal tissues. CCNA2 may significantly influence the prognosis of multiple cancer types, especially clear cell renal cell carcinoma (ccRCC). CCNA2 was also frequently mutated in most cancer types. Notably, CCNA2 was significantly correlated with immune cell infiltration and immune checkpoint inhibitory genes. In addition, CCNA2 was also strongly related to drug resistance.

Conclusion

CCNA2 may prove to be a new biomarker for prognostic prediction, tumor immunity assessment, and drug susceptibility evaluation in pancancer level, especially in ccRCC.

Biological response of adrenal carcinoma and melanoma cells to mitotane treatment

A previous case report described an adrenal incidentaloma initially misdiagnosed as adrenocortical carcinoma (ACC), which was treated with mitotane. The final diagnosis was metastatic melanoma of unknown primary origin. However, the patient developed rapid disease progression after mitotane withdrawal, suggesting a protective role for mitotane in a non-adrenal-derived tumor. The aim of the present study was to determine the biological response of primary melanoma cells obtained from that patient, and that of other established melanoma and ACC cell lines, to mitotane treatment using a proliferation assay, flow cytometry, quantitative PCR and microarrays. Although mitotane inhibited the proliferation of both ACC and melanoma cells, its role in melanoma treatment appears to be limited. Flow cytometry analysis and transcriptomic studies indicated that the ACC cell line was highly responsive to mitotane treatment, while the primary melanoma cells showed a moderate response in vitro. Mitotane modified the activity of several key biological processes, including ‘mitotic nuclear division’, ‘DNA repair’, ‘angiogenesis’ and ‘negative regulation of ERK1 and ERK2 cascade’. Mitotane administration led to elevated levels of DNA double-strand breaks, necrosis and apoptosis. The present study provides a comprehensive insight into the biological response of mitotane-treated cells at the molecular level. Notably, the present findings offer new knowledge on the effects of mitotane on ACC and melanoma cells.

Characterization of Cell Cycle-Related Competing Endogenous RNAs Using Robust Rank Aggregation as Prognostic Biomarker in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD), one of the most common pathological subtypes in lung cancer, has been of concern because it is the leading cause of cancer-related deaths. Due to its poor prognosis, to identify a prognostic biomarker, this study performed an integrative analysis to screen curial RNAs and discuss their cross-talks. The messenger RNA (mRNA) profiles were primarily screened using robust rank aggregation (RRA) through several datasets, and these deregulated genes showed important roles in multiple biological pathways, especially for cell cycle and oocyte meiosis. Then, 31 candidate genes were obtained via integrating 12 algorithms, and 16 hub genes (containing homologous genes) were further screened according to the potential prognostic values. These hub genes were used to search their regulators and biological-related microRNAs (miRNAs). In this way, 10 miRNAs were identified as candidate small RNAs associated with LUAD, and then miRNA-related long non-coding RNAs (lncRNAs) were further obtained. In-depth analysis showed that 4 hub mRNAs, 2 miRNAs, and 2 lncRNAs were potential crucial RNAs in the occurrence and development of cancer, and a competing endogenous RNA (ceRNA) network was then constructed. Finally, we identified CCNA2/MKI67/KIF11:miR-30a-5p:VPS9D1-AS1 axis-related cell cycle as a prognostic biomarker, which provided RNA cross-talks among mRNAs and non-coding RNAs (ncRNAs), especially at the multiple isomiR levels that further complicated the coding–non-coding RNA regulatory network. Our findings provide insight into complex cross-talks among diverse RNAs particularly involved in isomiRs, which will enrich our understanding of mRNA–ncRNA interactions in coding–non-coding RNA regulatory networks and their roles in tumorigenesis.

Protein disulfide isomerase A1‑associated pathways in the development of stratified breast cancer therapies

The oxidoreductase protein disulfide isomerase A1 (PDIA1) functions as a cofactor for many transcription factors including estrogen receptor α (ERα), nuclear factor (NF)-κB, nuclear factor erythroid 2-like 2 (NRF2) and regulates the protein stability of the tumor suppressor p53. Taking this into account we hypothesized that PDIA1, by differentially modulating the gene expression of a diverse subset of genes in the ERα-positive vs. the ERα-negative breast cancer cells, might modify dissimilar pathways in the two types of breast cancer. This hypothesis was investigated using RNA-seq data from PDIA1-silenced MCF-7 (ERα-positive) and MDA-MB-231 (ERα-negative) breast cancer cells treated with either interferon γ (IFN-γ) or etoposide (ETO), and the obtained data were further analyzed using a variety of bioinformatic tools alongside clinical relevance assessment via Kaplan-Meier patient survival curves. The results highlighted the dual role of PDIA1 in suppressing carcinogenesis in the ERα(+) breast cancer patients by negatively regulating the response to reactive oxygen species (ROS) and promoting carcinogenesis by inducing cell cycle progression. In the ERα(−) breast cancer patients, PDIA1 prevented tumor development by modulating NF-κB and p53 activity and cell migration and induced breast cancer progression through control of cytokine signaling and the immune response. The findings reported in this study shed light on the differential pathways regulating carcinogenesis in ERα(+) and ERα(−) breast cancer patients and could help identify therapeutic targets selectively effective in ERα(+) vs. ERα(−) patients.

PKMYT1 regulates the proliferation and epithelial‑mesenchymal transition of oral squamous cell carcinoma cells by targeting CCNA2

Oral squamous cell carcinoma (OSCC) has gradually become a global public health issue in recent years. Therefore, the current study aimed to explore the mechanism of OSCC development and to identify a potential target that may be used in its treatment. The expression of protein kinase, membrane-associated tyrosine/threonine 1 (PKMYT1) and cyclin A2 (CCNA2) in SCC-9 cells was determined prior to and following transfection with short hairpin RNA targeting PKMYT1. Cell proliferation, colony-forming ability, migration and invasion were determined using Cell Counting Kit-8, colony formation, wound healing and Transwell assays, respectively. Furthermore, the expression of epithelial-mesenchymal transition (EMT)- and migration-related proteins were evaluated using western blot analysis. Additionally, co-immunoprecipitation was used to verify the binding of PKMYT1 and CCNA2. The results revealed that PKMYT1 was highly expressed in OSCC cells and that PKMYT1 knockdown could inhibit proliferation, colony formation, migration, invasion, EMT and CCNA2 expression in SCC-9 cells. In addition, PKMYT1 was demonstrated to bind to CCNA2, and knocking down PKMYT1 resulted in inhibitory effects on cell proliferation, colony formation ability, migration, invasion and EMT by downregulating CCNA2 expression. PKMYT1 was observed to regulate the proliferation, migration and EMT of OSCC cells by targeting CCNA2, which may be used in the future to improve OSCC treatment.

Silencing of LncRNA AFAP1-AS1 Inhibits Cell Proliferation in Oral Squamous Cancer by Suppressing CCNA2

Background

Evidence has indicated that dysregulation of long noncoding RNAs (lncRNA) is a critical factor in the occurrence of many diseases, including cancer. The lncRNA AFAP1-AS1 has been shown to participate in oncogenesis, metastasis, or drug resistance in many types of cancer. However, the potential role of AFAP1-AS1 in oral squamous cell carcinoma (OSCC) has not been fully elucidated.

Methods

Bioinformatics analysis was performed to compare AFAP1-AS1 expression levels in OSCC cancer samples and in normal controls. The biological function of AFAP1-AS1 was studied through loss-of-function assays. To study the potential mechanisms, high-throughput sequencing was applied to OSCC cancer samples and a series of bioinformatics analyses were performed. The effects of AFAP1-AS1 on OSCC tumor growth was evaluated by in vivo xenograft tumor formation assays.

Results

Bioinformatics analyses indicated that AFAP1-AS1 was upregulated in OSCC. Overexpression of AFAP1-AS1 was positively correlated with lymph node metastasis, tumor stage, and pathological grade. Down-regulation of AFAP1-AS1 in OSCC led to decreased proliferation in vitro and, notably, inhibition of tumor growth in vivo. Further research indicated that AFAP1-AS1 regulated OSCC cell proliferation by targeting CCNA2.

Conclusion

AFAP1-AS1 promotes tumor proliferation and indicates a poor prognosis in OSCC, providing a potential therapeutic strategy.

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses

Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment. Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses. Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature's ability to detect the disease in its early stages in patients' biological fluids instead of invasive procedures and its prognostic potential. Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.

A Hypoxia Signature for Predicting Prognosis and Tumor Immune Microenvironment in Adrenocortical Carcinoma

Adrenocortical carcinoma (ACC) is a rare malignancy with dismal prognosis. Hypoxia is one of characteristics of cancer leading to tumor progression. For ACC, however, no reliable prognostic signature on the basis of hypoxia genes has been built. Our study aimed to develop a hypoxia-associated gene signature in ACC. Data of ACC patients were obtained from TCGA and GEO databases. The genes included in hypoxia risk signature were identified using the Cox regression analysis as well as LASSO regression analysis. GSEA was applied to discover the enriched gene sets. To detect a possible connection between the gene signature and immune cells, the CIBERSORT technique was applied. In ACC, the hypoxia signature including three genes (CCNA2, COL5A1, and EFNA3) was built to predict prognosis and reflect the immune microenvironment. Patients with high-risk scores tended to have a poor prognosis. According to the multivariate regression analysis, the hypoxia signature could be served as an independent indicator in ACC patients. GSEA demonstrated that gene sets linked to cancer proliferation and cell cycle were differentially enriched in high-risk classes. Additionally, we found that PDL1 and CTLA4 expression were significantly lower in the high-risk group than in the low-risk group, and resting NK cells displayed a significant increase in the high-risk group. In summary, the hypoxia risk signature created in our study might predict prognosis and evaluate the tumor immune microenvironment for ACC.

E2F1 transcriptionally regulates CCNA2 expression to promote triple negative breast cancer tumorigenicity

BACKGROUND

Triple-negative breast cancer (TNBC) is a highly malignant breast cancer subtype with a poor prognosis. The cell cycle regulator cyclin A2 (CCNA2) plays a role in tumor development. Herein, we explored the role of CCNA2 in TNBC.

METHODS

We analyzed CCNA2 expression in 15 pairs of TNBC and adjacent tissues and assessed the relationship between CCNA2 expression using the tissue microarray cohort. Furthermore, we used two TNBC cohort datasets to analyze the correlation between CCNA2 and E2F transcription factor 1 (E2F1) and a luciferase reporter to explore their association. Through rescue experiments, we analyzed the effects of E2F1 knockdown on CCNA2 expression and cellular behavior.

RESULTS

We found that CCNA2 expression in TNBC was significantly higher than that in adjacent tissues with similar observations in MDA-MB-231 and MDA-MB-468 cells. E2F1 was highly correlated with CCNA2 as observed through bioinformatics analysis (R= 0.80, P< 0.001) and through TNBC tissue verification analysis (R= 0.53, P< 0.001). We determined that E2F1 binds the +677 position within the CCNA2 promoter. Moreover, CCNA2 overexpression increased cell proliferation, invasion, and migration owing to E2F1 upregulation in TNBC.

CONCLUSION

Our data indicate that E2F1 promotes TNBC proliferation and invasion by upregulating CCNA2 expression. E2F1 and CCNA2 are potential candidates that may be targeted for effective TNBC treatment.

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.

Integrated analysis of ceRNA network in hepatocellular carcinoma using bioinformatics analysis

BACKGROUND

Long noncoding RNAs (lncRNAs) can work as microRNA (miRNA) sponges through a competitive endogenous RNA (ceRNA) mechanism. LncRNAs and miRNAs are important components of competitive endogenous binding, and their expression imbalance in hepatocellular carcinoma (HCC) is closely related to tumor development, diagnosis, and prognosis. This study explored the potential impact of the ceRNA regulatory network in HCC on the prognosis of HCC patients.

METHODS

We thoroughly researched the differential expression profiles of lncRNAs, miRNAs, and mRNAs from 2 HCC Gene Expression Omnibus datasets (GSE98269 and GSE60502). Then, a dysregulated ceRNA network was constructed by bioinformatics. In addition, hub genes in the ceRNA network were screened by Cytoscape, these hub genes functional analysis was performed by gene set enrichment analysis, and the expression of these hub genes in tumors and their correlation with patient prognosis were verified with Gene Expression Profiling Interactive Analysis.

RESULTS

A ceRNA network was successfully constructed in this study including 4 differentially expressed (DE) lncRNAs, 7 DEmiRNAs, and 166 DEmRNAs. Importantly, 4 core genes (CCNA2, CHEK1, FOXM1, and MCM2) that were significantly associated with HCC prognosis were identified.

CONCLUSIONS

Our study provides comprehensive and meaningful insights into HCC tumorigenesis and the underlying molecular mechanisms of ceRNA. Furthermore, the specific ceRNAs can be further used as potential therapeutic targets and prognostic biomarkers for HCC.

Expression and prognostic value of CDK1, CCNA2, and CCNB1 gene clusters in human breast cancer

Objective

Cell cycle-associated proteins play important roles in breast cancer (BRCA), based on evidence from cell lines, preclinical murine models, and human tissue samples.

Methods

Herein, we used the Onomine, GEPIA, Kaplan–Meier Plotter, and cBioPortal databases to examine transcriptional and survival data pertaining to cyclin-associated gene clusters (CDK1, CCNA2, and CCNB1) in BRCA patients.

Results

CDK1, CCNA2, and CCNB1 gene expression levels were higher in BRCA compared with control tissue samples and were correlated with more-advanced tumor stage. Kaplan–Meier survival analyses confirmed that elevated CDK1, CCNA2, and CCNB1 expression levels were associated with overall and post-progression survival and recurrence-free probability rates in patients with BRCA.

Conclusion

The results of this study implied that CDK1, CCNA2, and CCNB1 gene clusters may provide potential therapeutic targets and prognostic biomarkers in patients with BRCA.

Expression patterns and prognostic values of the cyclin-dependent kinase 1 and cyclin A2 gene cluster in pancreatic adenocarcinoma

Objective

Pancreatic adenocarcinoma (PAAD) is one of the most lethal malignant tumors worldwide. Various studies based on cell lines, preclinical mouse models, and human tissue samples have shown that cell cycle-associated proteins are involved in the tumorigenesis and progression of PAAD.

Methods

Herein, we analyzed the relationships between CDK1 and CCNA2 gene expression and prognosis in patients with pancreatic cancer, using information from the Oncomine, cBioportal, Kaplan–Meier Plotter, and GEPIA databases.

Results

Expression levels of CDK1 and CCNA2 were significantly higher in PAAD compared with control tissues, and were associated with more advanced tumor stage. Survival analyses using the Kaplan–Meier Plotter database further confirmed that increased expression levels of CDK1 and CCNA2 were associated with a poor prognosis in patients with pancreatic cancer.

Conclusions

The results of this study suggest that CDK1 and CCNA2 may be potential therapeutic targets and prognostic biomarkers in patients with PAAD.

Integrated Profiling Identifies CCNA2 as a Potential Biomarker of Immunotherapy in Breast Cancer

Introduction

Breast cancer is the main reason for cancer-related deaths in women and the most common malignant cancer among women. In recent years, immunosuppressive factors have become a new type of treatment for cancer. However, there are no effective biomarkers for breast cancer immunotherapy. Therefore, exploring immune-related biomarkers is presently an important topic in breast cancer.

Methods

Gene expression profile data of breast cancer from The Cancer Genome Atlas (TCGA) was downloaded. Scale-free gene co-expression networks were built with weighted gene co-expression network analysis. The correlation of genes was performed with Pearson’s correlation values. The potential associations between clinical features and gene sets were studied, and the hub genes were screened out. Gene Ontology and gene set enrichment analysis were used to reveal the function of hub gene in breast cancer. The gene expression profiles of GSE15852, downloaded from the Gene Expression Omnibus database, were used for hub gene verification. In addition, candidate biomarkers expression in breast cancer was studied. Survival analysis was performed using Log rank test and Kaplan–Meier. Immunohistochemistry was used to analyze the expression of CCNA2.

Results

A total of 6 modules related to immune cell infiltration were identified via the average linkage hierarchical clustering. According to the threshold criteria (module membership >0.9 and gene significance >0.35), a significant module consisting of 13 genes associated with immune cells infiltration were identified as candidate hub genes after performed with the human protein interaction network. And 3 genes with high correlation to clinical traits were identified as hub genes, which were negatively associated with the overall survival. Among them, the expression of CCNA2 was increased in metastatic breast cancer compare with non-metastatic breast cancer, who underwent immunotherapy. Immunohistochemistry results showed that CCNA2 expression in carcinoma tissues was elevated compared with normal control.

Discussion

CCNA2 identified as a potential immune therapy marker in breast cancer, which were first reported here and deserved further research.

Development and Validation of a Prognostic Gene Signature in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.

Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis

Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e-26) and tumor stage (r = 0.38, p = 2e-17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.

Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer

Genetic co-expression network (GCN) analysis augments the understanding of breast cancer (BC). We aimed to propose GCN-based modeling for BC relapse-free survival (RFS) prediction and to discover novel biomarkers. We used GCN and Cox proportional hazard regression to create various prediction models using mRNA microarray of 920 tumors and conduct external validation using independent data of 1056 tumors. GCNs of 34 identified candidate genes were plotted in various sizes. Compared to the reference model, the genetic predictors selected from bigger GCNs composed better prediction models. The prediction accuracy and AUC of 3 ~ 15-year RFS are 71.0-81.4% and 74.6-78% respectively (rfm, ACC 63.2-65.5%, AUC 61.9-74.9%). The hazard ratios of risk scores of developing relapse ranged from 1.89 ~ 3.32 (p < 10^-8) over all models under the control of the node status. External validation showed the consistent finding. We found top 12 co-expressed genes are relative new or novel biomarkers that have not been explored in BC prognosis or other cancers until this decade. GCN-based modeling creates better prediction models and facilitates novel genes exploration on BC prognosis.

The overexpression of DNA repair genes in invasive ductal and lobular breast carcinomas: Insights on individual variations and precision medicine

In the era of precision medicine, analyzing the transcriptomic profile of patients is essential to tailor the appropriate therapy. In this study, we explored transcriptional differences between two invasive breast cancer subtypes; infiltrating ductal carcinoma (IDC) and lobular carcinoma (LC) using RNA-Seq data deposited in the TCGA-BRCA project. We revealed 3854 differentially expressed genes between normal ductal tissues and IDC. In addition, IDC to LC comparison resulted in 663 differentially expressed genes. We then focused on DNA repair genes because of their known effects on patients' response to therapy and resistance. We here report that 36 DNA repair genes are overexpressed in a significant number of both IDC and LC patients' samples. Despite the upregulation in a significant number of samples, we observed a noticeable variation in the expression levels of the repair genes across patients of the same cancer subtype. The same trend is valid for the expression of miRNAs, where remarkable variations between patients' samples of the same cancer subtype are also observed. These individual variations could lie behind the differential response of patients to treatment. The future of cancer diagnostics and therapy will inevitably depend on high-throughput genomic and transcriptomic data analysis. However, we propose that performing analysis on individual patients rather than a big set of patients' samples will be necessary to ensure that the best treatment is determined, and therapy resistance is reduced.

Identification of potential key genes and miRNAs involved in Hepatoblastoma pathogenesis and prognosis

Hepatoblastoma (HB) is one of the most common liver malignancies in children, while the molecular basis of the disease is largely unknown. Therefore, this study aims to explore the key genes and molecular mechanisms of the pathogenesis of HB using a bioinformatics approach. The gene expression dataset GSE131329 was used to find differentially expressed genes (DEGs). Functional and enrichment analyses of the DEGs were performed by the EnrichR. Then, the protein-protein interaction (PPI) network of the up-regulated genes was constructed and visualized using STRING database and Cytoscape software, respectively. MCODE was used to detect the significant modules of the PPI network, and cytoHubba was utilized to rank the important nodes (genes) of the PPI modules. Overall, six ranking methods were employed and the results were validated by the Oncopression database. Moreover, the upstream regulatory network and the miRNA-target interactions of the up-regulated DEGs were analyzed by the X2K web and the miRTarBase respectively. A total of 594 DEGs, including 221 up- and 373 down-regulated genes, were obtained, which were enriched in different cellular and metabolic processes, human diseases, and cancer. Furthermore, 15 hub genes were screened, out of which, 11 were validated. Top 10 transcription factors, kinases, and miRNAs were also determined. To the best of our knowledge, the association of RACGAP1, MKI67, FOXM1, SIN3A, miR-193b, and miR-760 with HB was reported for the first time. Our findings may be used to shed light on the underlying mechanisms of HB and provide new insights for better prognosis and therapeutic strategies.

A prognosis-related molecular subtype for early-stage non-small lung cell carcinoma by multi-omics integration analysis

Background

Early-stage non-small cell lung carcinoma (NSCLC) accounts for more than 80% of lung cancer, which is a kind of cancer with high heterogeneity, so the genetic heterogeneity and molecular subtype should be explored.

Methods

Partitioning Around Medoid algorithm was used to acquire the molecular subtype for early-stage NSCLC based on prognosis-related mRNAs and methylation sites. Random forest (RF) and support vector machine (SVM) were used to build prediction models for subtypes.

Results

Six prognosis-related subtypes for early-stage NSCLC, including 4 subtypes for lung squamous cell carcinoma (LUSC) and 2 subtypes for lung adenocarcinoma (LUAD), were identified. There were highly expressed and hypermethylated gene regions for LUSC-C1 and LUAD-C2, highly expressed region for LUAD-C1, and hypermethylated regions for LUSC-C3 and LUSC-C4. Molecular subtypes for LUSC were mainly determined by DNA methylation (14 mRNAs and 362 methylation sites). Molecular subtypes for LUAD were determined by both mRNA and DNA methylation information (143 mRNAs and 458 methylation sites). Ten methylation sites were selected as biomarkers for prediction of LUSC-C1 and LUSC-C3, respectively. Nine genes and 1 methylation site were selected as biomarkers for LUAD subtype prediction. These subtypes can be predicted by the selected biomarkers with RF and SVM models.

Conclusions

In conclusion, we proposed a prognosis-related molecular subtype for early-stage NSCLC, which can provide important information for personalized therapy of patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07846-0.

Key Genes and Prognostic Analysis in HER2+ Breast Cancer

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes (CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

Identification of Potential Diagnostic and Prognostic Biomarkers for Colorectal Cancer Based on GEO and TCGA Databases

Colorectal cancer (CRC) is one of the most common neoplastic diseases worldwide. With a high recurrence rate among all cancers, treatment of CRC only improved a little over the last two decades. The mortality and morbidity rates can be significantly lessened by earlier diagnosis and prompt treatment. Available biomarkers are not sensitive enough for the diagnosis of CRC, whereas the standard diagnostic method, endoscopy, is an invasive test and expensive. Hence, seeking the diagnostic and prognostic biomarkers of CRC is urgent and challenging. With that order, we screened the overlapped differentially expressed genes (DEGs) of GEO (GSE110223, GSE110224, GSE113513) and TCGA datasets. Subsequent protein-protein interaction network analysis recognized the hub genes among these DEGs. Further functional analyses including Gene Ontology and KEGG pathway analysis and gene set enrichment analysis were processed to investigate the role of these genes and potential underlying mechanisms in CRC. Kaplan-Meier analysis and Cox hazard ratio analysis were carried out to clarify the diagnostic and prognostic role of these genes. In conclusion, our present study demonstrated that CCNA2, MAD2L1, DLGAP5, AURKA, and RRM2 are all potential diagnostic biomarkers for CRC and may also be potential treatment targets for clinical implication in the future.

Ribosome assembly factor URB1 contributes to colorectal cancer proliferation through transcriptional activation of ATF4

Ribosome assembly factor URB1 is essential for ribosome biogenesis. However, its latent role in cancer remains unclear. Analysis of The Cancer Genome Atlas database and clinical tissue microarray staining showed that URB1 expression was upregulated in colorectal cancer (CRC) and prominently related to clinicopathological characteristics. Silencing of URB1 hampered human CRC cell proliferation and growth in vitro and in vivo. Microarray screening, ingenuity pathway analysis, and JASPAR assessment indicated that activating transcription factor 4 (ATF4) and X‐box binding protein 1 (XBP1) are potential downstream targets of URB1 and could transcriptionally interact through direct binding. Silencing of URB1 significantly decreased ATF4 and cyclin A2 (CCNA2) expression in vivo and in vitro. Restoration of ATF4 effectively reversed the malignant proliferation phenotype of URB1‐silenced CRC cells. Dual‐luciferase reporter and ChIP assays indicated that XBP1 transcriptionally activated ATF4 by binding with its promoter region. X‐box binding protein 1 colocalized with ATF4 in the nuclei of RKO cells, and ATF4 mRNA expression was positively regulated by XBP1. This study shows that URB1 contributes to oncogenesis and CRC growth through XBP1‐mediated transcriptional activation of ATF4. Therefore, URB1 could be a potential therapeutic target for CRC.

Identification of grade-related genes and construction of a robust genomic-clinicopathologic nomogram for predicting recurrence of bladder cancer

BACKGROUND

Bladder cancer (BC) is a common tumor in the urinary system with a high recurrence rate. The individualized treatment and follow-up after surgery is the key to a successful outcome. Currently, the surveillance strategies are mainly depending on tumor stage and grade. Previous evidence has proved that tumor grade was a significant and independent risk factor of BC recurrence. Exploring the grade-related genes may provide us a new approach to predict prognosis and guide the post-operative treatment in BC patients.

METHODS

In this study, the weighted gene co-expression network analysis was applied to identify the hub gene module correlated with BC grade using GSE71576. After constructing a protein-protein interaction (PPI) network with the hub genes inside the hub gene module, we identified some potential core genes. TCGA and another independent dataset were used for further validation.

RESULTS

The results revealed that the expression of AURKA, CCNA2, CCNB1, KIF11, TTK, BUB1B, BUB1, and CDK1 were significantly higher in high-grade BC, showing a strong ability to distinguish BC grade. The expression levels of the 8 genes in normal, paracancerous, tumorous, and recurrent bladder tissues were progressively increased. By conducting survival analysis, we proved their prognostic value in predicting the recurrence of BC. Eventually, we constructed a prognostic nomogram by combining the 8-core-gene panel with clinicopathologic features, which had shown great performance in predicting the recurrence of BC.

CONCLUSION

We identified 8 core genes that revealed a significant correlation with the tumor grade as well as the recurrence of BC. Finally, we proved the value of a novel prognostic nomogram for predicting the relapse-free survival of BC patients after surgery, which could guide their treatment and follow-up.

Identification of potential target genes and crucial pathways in small cell lung cancer based on bioinformatic strategy and human samples

Small cell lung cancer (SCLC) is a carcinoma of the lungs with strong invasion, poor prognosis and resistant to multiple chemotherapeutic drugs. It has posed severe challenges for the effective treatment of lung cancer. Therefore, searching for genes related to the development and prognosis of SCLC and uncovering their underlying molecular mechanisms are urgent problems to be resolved. This study is aimed at exploring the potential pathogenic and prognostic crucial genes and key pathways of SCLC via bioinformatic analysis of public datasets. Firstly, 117 SCLC samples and 51 normal lung samples were collected and analyzed from three gene expression datasets. Then, 102 up-regulated and 106 down-regulated differentially expressed genes (DEGs) were observed. And then, functional annotation and pathway enrichment analyzes of DEGs was performed utilizing the FunRich. The protein-protein interaction (PPI) network of the DEGs was constructed through the STRING website, visualized by Cytoscape. Finally, the expression levels of eight hub genes were confirmed in Oncomine database and human samples from SCLC patients. It showed that CDC20, BUB1, TOP2A, RRM2, CCNA2, UBE2C, MAD2L1, and BUB1B were upregulated in SCLC tissues compared to paired adjacent non-cancerous tissues. These suggested that eight hub genes might be viewed as new biomarkers for prognosis of SCLC or to guide individualized medication for the therapy of SCLC.

Independent prognostic implications of RRM2 in lung adenocarcinoma

Background: Ribonucleoside-diphosphate reductase subunit M2 (RRM2) is the catalytic subunit of ribonucleotide reductase and modulates the enzymatic activity, which is essential for DNA replication and repair. However, the role of RRM2 in lung adenocarcinoma (LUAD) remains unclear.

Methods: In this study, we explored the expression pattern and prognostic value of RRM2 in LUAD across TCGA, GEO, Oncomine, UALCAN, PrognoScan, and Kaplan-Meier Plotter, and confirmed its independent prognostic value via Cox analyses. LinkedOmics and GEPIA2 were applied to investigate co-expression and functional networks associated with RRM2. Besides, we used TIMER to assess the correlation between RRM2 and the main six types of tumor-infiltrating immune cells. Lastly, the correlations between immune signatures of immunomodulators, chemokines, and 28 tumor-infiltrating lymphocytes (TILs) and RRM2 were examined by tumor purity-corrected partial Spearman's rank correlation coefficient through TIMER portal.

Results:RRM2 was found upregulated in tumor tissues in TCGA-LUAD, and validated in multiple independent cohorts. Moreover, whether in TCGA or other cohorts, high RRM2 expression was found to be associated with poor survival. Cox analyses showed that high RRM2 expression was an independent risk factor for overall survival, disease-specific survival, and progression-free survival of LUAD. Functional network analysis suggested that RRM2 regulates RNA transport, oocyte meiosis, spliceosome, ribosome biogenesis in eukaryotes, and cellular senescence signaling through pathways involving multiple cancer-related kinases and E2F family. Also, RRM2 expression correlated with infiltrating levels of B cells, CD4+ T cells, and neutrophils. Subsequent analysis found that B cells and dendritic cells could predict the outcome of LUAD. B cells were identified as an independent risk factor among six types of immune cells through Cox analyses. At last, the correlation analysis showed RRM2 correlated with 67.68% (624/922) of the immune signatures we performed.

Conclusion: Our research showed that RRM2 could independently predict the prognosis of LUAD and was associated with immune infiltration. In particular, the tight relationship between RRM2 and B cell marker genes are the potential epicenter of the immune response and one of the critical factors affecting the prognosis. Our findings laid the foundation for further research on the immunomodulatory role of RRM2 in LUAD.

Independent Prognostic Potential of GNPNAT1 in Lung Adenocarcinoma

Background

Glucosamine-Phosphate N-Acetyltransferase 1 (GNPNAT1) is a critical enzyme in the biosynthesis of uridine diphosphate-N-acetylglucosamine. It has many important functions, such as protein binding, monosaccharide binding, and embryonic development and growth. However, the role of GNPNAT1 in lung adenocarcinoma (LUAD) remains unclear.

Methods

In this study, we explored the expression pattern and prognostic value of GNPNAT1 in LUAD across TCGA and GEO databases and assessed its independent prognostic value via Cox analysis. LinkedOmics and GEPIA2 were applied to investigate coexpression and functional networks associated with GNPNAT1. The TIMER web tool was deployed to assess the correlation between GNPNAT1 and the main six types of tumor-infiltrating immune cells. Besides, the correlations between GNPNAT1 and the LUAD common genetic mutations, TMB, and immune signatures were examined.

Results

GNPNAT1 was validated upregulated in tumor tissues in TCGA-LUAD and GEO cohorts. Moreover, in both TCGA and GEO cohorts, high GNPNAT1 expression was found to be associated with poor overall survival. Cox analysis showed that high GNPNAT1 expression was an independent risk factor for LUAD. Functional network analysis suggested that GNPNAT1 regulates cell cycle, ribosome, proteasome, RNA transport, and spliceosome signaling through pathways involving multiple cancer-related kinases and E2F family. In addition, GNPNAT1 correlated with infiltrating levels of B cells, CD4+ T cells, and dendritic cells. B cells and dendritic cells could predict the outcome of LUAD, and B cells and CD4+ T cells were significant independent risk factors. The TMB and mutations of KRAS, EGFR, STK11, and TP53 were correlated with GNPNAT1. At last, the correlation analysis showed GNPNAT1 correlated with most of the immune signatures we performed.

Conclusion

Our findings showed that GNPNAT1 was correlated to the prognosis and immune infiltration of LUAD. In particular, the tight relationship between GNPNAT1 and B cell marker genes may be the epicenter of the immune response and one of the key factors affecting the prognosis. Our findings laid the foundation for further research on the immunomodulatory role of GNPNAT1 in LUAD.