NCAPG upregulation mediated by four microRNAs combined with activation of the p53 signaling pathway is a predictor of poor prognosis in patients with breast cancer

Unveiling the Significance of NCAP Family Genes in Adrenocortical Carcinoma and Adenoma Pathogenesis: A Molecular Bioinformatics Exploration

Objectives

Adrenocortical carcinoma (ACC), a rare and aggressive adrenal cortex cancer, poses significant challenges due to high mortality, poor prognosis, and early post-surgery recurrence. Variability in survival across ACC stages emphasizes the need to uncover its molecular underpinnings. Adrenocortical adenoma, a benign tumor, adds to diagnostic challenges, highlighting the necessity for molecular insights. The Non-SMC Associated Condensin Complex (NCAP) gene family, recognized for roles in chromosomal structure and cell cycle control. This study focuses on evaluating NCAP gene functions and importance in ACC through gene expression profiling to identify diagnostic and therapeutic targets.

Methods

Microarray datasets from ACC patients, obtained from the Gene Expression Omnibus database, were normalized to eliminate batch effects. Differential expression analysis of NCAP family genes, facilitated by the GEPIA2 database, included survival and pathological stage evaluations. A Protein-Protein Interaction network was constructed using GeneMANIA, and additional insights were gained through Gene Ontology enrichment analysis, correlation analysis, and ROC curve analysis.

Results

ACC samples exhibited elevated levels of NCAPG, NCAPG2, and NCAPH compared to normal and adenoma samples. Increased expression of these genes correlated with poor overall survival, particularly in advanced disease stages. The Protein-Protein Interaction network highlighted interactions with related proteins, and Gene Ontology enrichment analysis demonstrated their involvement in chromosomal structure and control. Differentially expressed NCAP genes showed positive associations, and ROC curve analysis indicated their high discriminatory power in identifying ACC from adenoma and normal samples.

Conclusion

The study emphasizes the potential importance of NCAPG, NCAPG2, and NCAPH in ACC, suggesting roles in tumor aggressiveness and diagnostic relevance. These genes could serve as therapeutic targets and markers for ACC, but further exploration into their molecular activities and validation studies is imperative to fully harness their diagnostic and therapeutic potential, advancing precision medicine approaches against this rare but lethal malignancy.

Deciphering the miRNA-mRNA Interaction Landscape between Breast Cancer and Triple-Negative Breast Cancer: An Integrated Bioinformatics Approach

Breast cancer (BC) is globally recognized as the second most prevalent form of cancer. It predominantly affects women and can be categorized into distinct types based on the overexpression of specific cancer receptors.The key receptors implicated in this context are the human epidermal growth factor receptor-2 (HER2), estrogen receptor (ER), and progesterone receptor (PR), alongside a particularly intricate subclass known as triple-negative breast cancer (TNBC). This subclassification is critical for the stratification of breast cancer and informs therapeutic decision-making processes. Due to a lack of therapeutic targets, such as growth factor receptors, TNBC is the most aggressive type. Hence, identifying targetable regulators such as miRNAs could pave the way for potential therapeutic interventions. To identify common differentially expressed mRNAs (DE-mRNAs) in BC, including TNBC, we leveraged two data sets from the GEO collection and The Cancer Genome Atlas (TCGA). Significant DE-mRNAs were identified through PPI, MCODE, CytoNCA, and CytoHubba analyses. Following this, miRNAs were predicted using mirDIP. We utilized GSE42568, GSE185645, and TCGA and identified 159 common DE-mRNAs. Using Cytoscape plug-ins, we identified the 10 most significant DE-mRNAs in BC. Using mirDIP, target miRNAs for 10 DE-mRNAs were identified. We conducted an advanced analysis on the TNBC GEO data set (GSE45498) to corroborate the significance of shared DE-mRNAs and DE-miRNAs in TNBC. We identified four downregulated DE-miRNAs, including hsa-miR-802, hsa-miR-1258, hsa-miR-548a-3p, and hsa-miR-2053, significantly associated with TNBC. Our study revealed significant miRNA-mRNA interactions, specifically hsa-miR-802/MELK, hsa-miR-1258/NCAPG, miR-548a-3p/CCNA2, and hsa-miR-2053/NUSAP1, in both BC and TNBC. The observed downregulation of hsa-miR-548a-3p is associated with diminished survival rates in BC patients, emphasizing their potential utility as prognostic indicators. Furthermore, the differential expression of mRNAs, including CCNB2, UBE2C, MELK, and KIF2C, correlates with reduced survival outcomes, signifying their critical role as potential targets for therapeutic intervention in both BC and TNBC. These findings highlight specific regulatory mechanisms that are potentially crucial for understanding and treating these cancer types.

Important role and underlying mechanism of non‑SMC condensin I complex subunit G in tumours (Review)

At present, the incidence of tumours is increasing on a yearly basis, and tumourigenesis is usually associated with chromosomal instability and cell cycle dysregulation. Moreover, abnormalities in the chromosomal structure often lead to DNA damage, further exacerbating gene mutations and chromosomal rearrangements. However, the non‑SMC condensin I complex subunit G (NCAPG) of the structural maintenance of chromosomes family is known to exert a key role in tumour development. It has been shown that high expression of NCAPG is closely associated with tumour development and progression. Overexpression of NCAPG variously affects chromosome condensation and segregation during cell mitosis, influences cell cycle regulation, promotes tumour cell proliferation and invasion, and inhibits apoptosis. In addition, NCAPG has been associated with tumour cell stemness, tumour resistance and recurrence. The aim of the present review was to explore the underlying mechanisms of NCAPG during tumour development, with a view towards providing novel targets and strategies for tumour therapy, and through the elucidation of the mechanisms involved, to lay the foundation for future developments in health.

Comprehensive Analysis Identifies Hyaluronan Mediated Motility Receptor and Cell Division Cycle 25C as Potential Prognostic Biomarkers in Head and Neck Squamous Cell Carcinoma

INTRODUCTION

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, but its pathogenic mechanisms remain unclear. This study aimed to identify the potential biomarkers underlying the diagnosis and treatment of HNSCC.

METHODS

Weighted gene co-expression network analysis (WGCNA) followed by pathway enrichment analysis, analysis of infiltrating immune cells, survival analysis, and methylation analysis were applied to identify the potential hub genes underlying the prognosis of HNSCC. The expression of hub genes was validated by immunofluorescence staining.

RESULTS

A total of 10,274 differentially expressed genes (DEGs) were identified. Through WGCNA, the yellow module (R2 = 0.33, P = 2e-14) was confirmed to be the most significantly associated with the histological grade of HNSCC, and the "Cell Cycle" proved to be the most enriched signaling pathway. Based on the results of survival analysis and immune cell infiltration, 10 hub genes (HMMR, CENPK, AURKA, CDC25C, FEN1, CKS1B, MAJIN, PCLAF, SPC25, and STAG3) were identified. Eight of these (excluding MAJIN and STAG3) were confirmed by performing survival analysis using another dataset (GSE41613). Further, we identified 4 methylation loci in 3 hub genes (cg15122828 and cg20554926 in HMMR, cg12519992 in CDC25C, and cg2655739 in KIAA0101/PCLAF) as being significantly related to survival. Finally, we demonstrated the high mRNA and protein expression of HMMR and CDC25C in HNSCC patients.

CONCLUSION

Two real hub genes (HMMR and CDC25C) and 3 methylation loci were identified that could potentially serve as prognostic and therapeutic targets for HNSCC, which is significant for studying the pathological mechanisms underlying HNSCC and for developing novel therapies for this disease.

NCAPG as a novel prognostic biomarker in numerous cancers: a meta-analysis and bioinformatics analysis

BACKGROUND

Identification of effective biomarkers for cancer prognosis is a primary research challenge. Recently, several studies have reported the relationship between NCAPG and the occurrence of various tumors. However, none have combined meta-analytical and bioinformatics approaches to systematically assess the role of NCAPG in cancer.

METHODS

We searched four databases, namely, PubMed, Web of Science, Embase, and the Cochrane Library, for relevant articles published before April 30, 2022. The overall hazard ratio or odds ratio and 95% confidence intervals were calculated to assess the relationship between NCAPG expression and cancer survival prognosis or clinical characteristics. Furthermore, the aforementioned results were validated using the GEPIA2, Kaplan-Meier plotter, and PrognoScan databases.

RESULTS

The meta-analysis included eight studies with 1096 samples. The results showed that upregulation of NCAPG was correlated with poorer overall survival (hazard ratio = 2.90, 95% confidence interval = 2.06-4.10, P < 0.001) in the cancers included in the study. Subgroup analysis showed that in some cancers, upregulation of NCAPG was correlated with age, distant metastasis, lymph node metastasis, TNM stage, relapse, differentiation, clinical stage, and vascular invasion. These results were validated using the GEPIA2, UALCAN, and PrognoScan databases. We also explored the processes of NCAPG methylation and phosphorylation.

CONCLUSION

Dysregulated NCAPG expression is associated with the clinical prognostic and pathological features of various cancers. Therefore, NCAPG can serve as a human cancer therapeutic target and a new potential prognostic biomarker.

Knockdown of NCAPG promotes the apoptosis and inhibits the invasion and migration of triple‑negative breast cancer MDA‑MB‑231 cells via regulation of EGFR/JAK/STAT3 signaling

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and the treatment options are extremely limited. Non-SMC condensing I complex subunit G (NCAPG) expression is upregulated in TNBC, but its specific regulatory mechanism in TNBC has not been previously reported. The expression levels of NCAPG in TNBC were analyzed using data obtained from the UALCAN database. RT-qPCR and western blotting were used to detect the expression of NCAPG in various breast cancer cell lines. The expression of NCAPG was knocked down, and cell viability was then detected using a CCK-8 assay, apoptosis was detected using a TUNEL assay, and the expression of the apoptosis-related proteins Bcl-2, Bax and Bad were detected by western blotting. Wound healing and Transwell assays were used to assess migration and invasion. Western blotting was also used to determine the expression levels of migration and invasion-related proteins MMP2 and MMP9, as well as EGFR/JAK/STAT3 pathway-related proteins. Following exogenous treatment with EGF and the JAK/STAT3 signaling pathway agonist colivelin, cell viability, apoptosis, invasion and migration were assessed. The expression of NCAPG in TNBC MDA-MB-231 cells was significantly increased. Inhibition of NCAPG inhibited the activity, promoted apoptosis, and inhibited the invasion and migration of TNBC MDA-MB-231 cells, potentially via regulation of the EGFR/JAK/STAT3 signaling pathway. In conclusion, downregulation of NCAPG can promote apoptosis and inhibit invasion and migration of TNBC MDA-MB-231 cells via EGFR/JAK/STAT3 signaling.

Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma

Background

Multiple myeloma (MM) remains an essentially incurable disease. This study aimed to establish a predictive model for estimating prognosis in newly diagnosed MM based on gene expression profiles.

Methods

RNA-seq data were downloaded from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study and the Genotype-Tissue Expression (GTEx) databases. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction network analysis were performed to identify hub genes. Enrichment analysis was also conducted. Patients were randomly split into training (70%) and validation (30%) datasets to build a prognostic scoring model based on the least absolute shrinkage and selection operator (LASSO). CIBERSORT was applied to estimate the proportion of 22 immune cells in the microenvironment. Drug sensitivity was analyzed using the OncoPredict algorithm.

Results

A total of 860 newly diagnosed MM samples and 444 normal counterparts were screened as the datasets. WGCNA was applied to analyze the RNA-seq data of 1589 intersecting genes between differentially expressed genes and prognostic genes. The blue module in the PPI networks was analyzed with Cytoscape, and 10 hub genes were identified using the MCODE plug-in. A three-gene (TTK, GINS1, and NCAPG) prognostic model was constructed. This risk model showed remarkable prognostic value. CIBERSORT assessment revealed the risk model to be correlated with activated memory CD4 T cells, M0 macrophages, M1 macrophages, eosinophils, activated dendritic cells, and activated mast cells. Furthermore, based on OncoPredict, high-risk MM patients were sensitive to eight drugs.

Conclusions

We identified and constructed a three-gene-based prognostic model, which may provide new and in-depth insights into the treatment of MM patients.

Identification of the Key Genes Involved in the Tumorigenesis and Prognosis of Prostate Cancer

Background. Prostate cancer (PCa) is a malignant tumor in males, with a majority of the cases advancing to metastatic castration resistance. Metastasis is the leading cause of mortality in PCa. The traditional early detection and prediction approaches cannot differentiate between the different stages of PCa. Therefore, new biomarkers are necessary for early detection and clear differentiation of PCa stages to provide precise therapeutic intervention. Methods. The objective of the study was to find significant differences in genes and combine the three GEO datasets with TCGA-PRAD datasets (DEG). Weighted gene coexpression network analysis (WGCNA) determined the gene set and PCa clinical feature correlation module utilizing the TGGA-PRAD clinical feature data. The correlation module genes were rescreened using the biological information analysis tools, with the three hub genes (TOP2A, NCAPG, and BUB1B) for proper verification. Finally, internal (TCGA) and external (GSE32571, GSE70770) validation datasets were used to validate and predict the value of last hub genes. Results. The hub gene was abnormally upregulated in PCa samples during verification. The expression of each gene was favorably connected with the Gleason score and TN tumor grade in clinical samples but negatively correlated with the overall survival rate. The expression of these genes was linked to CD8 naive cells and macrophages, among other cells. Antitumor immune cells like NK and NKT were favorably and adversely correlated with infiltrating cells, respectively. Simultaneously, the GSCV and GSEA indicated that the hub gene is connected with cell proliferation, death, and androgen receptor, among other signaling pathways. Therefore, these genes could influence the incidence and progression of PCa by participating in or modulating various signaling pathways. Furthermore, using the online tool of CMap, we examined the individual medications for Hughes and determined that tipifarnib could be useful for the clinical therapy of PCa. Conclusion. TOP2A, NCAPG, and BUB1B are important genes intimately linked to the clinical prognosis of PCa and can be employed as reliable biomarkers for early diagnosis and prognosis. Moreover, these genes can provide a theoretical basis for precision differentiation and treatment of PCa.

The role of NCAPG in various of tumors

Non-SMC Condensin I complex subunit G (NCAPG), a mitosis-associated chromosomal condensation protein, is related to sister chromatid appropriate separation during the condensation and fusion of chromosomes and responsible for the condensation and stabilization of chromosomes during meiosis and mitosis. Studies have shown that NCAPG is highly adjusted in a variety of cancers, and its related molecular mechanism affects tumor cell proliferation, invasion, metastasis, and apoptosis including hepatocellular carcinoma, prostate cancer, breast cancer, gastric cancer, gliomas, lung adenocarcinoma, colorectal cancer, ovarian cancer, and endometrial cancer. Clinically, the expression of NCAPG is strongly correlated with N-classification, M-classification, and clinical stage, and NCAPG is valuable for the prognosis of patients with lung adenocarcinoma. In addition, NCAPG can also reduce the sensitivity of tumor cells such as breast cancer to reduce the reaction of the original chemotherapy, so that tumor cells are drug-resistance. In summary, NCAPG can serve as a new diagnosis and treatment target for a variety of cancers, and is also a very promising prognostic marker. Therefore, this review summarizes the critical role of NCAPG in the diagnosis, treatment, and prognosis for various cancers, and the mechanism by which NCAPG plays its pivotal roles.

Novel Insights into miR-944 in Cancer

Simple Summary

miR-944 is localized in intron 4 of TP63. ΔNp63 in intron 3 of TP63 recruits the transcription factor AP-2 to promote miR-944 gene expression, which mediates epidermal differentiation induction by ΔNp63. miR-944 is dysregulated in various cancers. In squamous cell carcinoma. miR-944 can target and inhibit 27 protein-coding genes, thereby regulating cell cycle, proliferation, apoptosis, epithelial mesenchymal transition, cancer cell invasion and migration, and other cell behaviors. The genes targeted by miR-944 are involved in three signaling pathways, including the Wnt/β-catenin pathway, Jak/STAT3 pathway, and PI3K/AKT pathway. miR-944 was regulated by a total of 11 competing endogenous RNAs, including 6 circular RNAs and 5 long non-coding RNAs. Abnormally expressed miR-944 can act as an independent prognostic factor and is closely related to tumor invasion, lymph node metastasis, TNM staging, and drug resistance. miR-944 is expected to become a critical biomarker with great clinical application value in cancer.

Abstract

miRNA is a class of endogenous short-chain non-coding RNAs consisting of about 22 nucleotides. miR-944 is located in the fourth intron of the TP63 gene in the 3q28 region. miR-944 is abnormally expressed in cancers in multiple systems including neural, endocrine, respiratory, reproductive, and digestive systems. miR-944 can target at least 27 protein-coding genes. miR-944 can regulate a series of cell behaviors, such as cell cycle, proliferation, invasion and migration, EMT, apoptosis, etc. miR-944 participates in the networks of 11 ceRNAs, including six circRNAs and five lncRNAs. miR-944 is involved in three signaling pathways. The abnormal expression of miR-944 is closely related to the clinicopathological conditions of various cancer patients. Deregulated expression of miR-944 is significantly associated with clinicopathology and prognosis in cancer patients. In addition, miR-944 is also associated with the development of DDP, RAPA, DOX, and PTX resistance in cancer cells. miR-944 is involved in the anticancer molecular mechanisms of matrine and Rhenium-liposome drugs. In conclusion, this work systematically summarizes the related findings of miR-944, which will provide potential hints for follow-up research on miR-944.

NCAPG facilitates colorectal cancer cell proliferation, migration, invasion and epithelial-mesenchymal transition by activating the Wnt/β-catenin signaling pathway

BACKGROUND

The condensation complex gene non-SMC condensin I complex subunit G(NCAPG), a cell cycle-associated condensin, is over-expressed in various cancers. However, its biological function in colorectal cancer (CRC) has yet to be deciphered. In this study, we investigated the role of NCAPG in CRC progression.

METHODS

Tissues and cells were used to measure NCAPG expression levels and their association with clinicopathological characteristics. NCAPG silencing and overexpression in CRC cells were used to measure its effect on proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) progression. In addition, mRNA, and protein expression levels of key EMT biomarkers were measured. The underlying mechanism of NCAPG modulating CRC progression was further explored using western blotting, co-immunoprecipitation (CO-IP), and immunofluorescence (IF) assays.

RESULTS

NCAPG was over-expressed in CRC tissues and cell lines. High expression levels were associated with differentiation levels, lymph metastasis, and vascular invasion in patients. NCAPG silencing suppressed, while NCAPG overexpression promoted the proliferative, migration, and invasive capacity of HCT116 and SW480 cells. Mechanistically, we discovered that NCAPG participated in regulating the EMT process and the Wnt/β-catenin signaling pathway to facilitate CRC invasion and metastasis. Additional experiments demonstrated that NCAPG activated the Wnt/β-catenin signaling pathway by binding to β-catenin in CRC cells.

CONCLUSION

NCAPG acts as an oncogene involved in the development and progression of CRC by binding to β-catenin to activate the Wnt/β-catenin signaling pathway.

NCAPG Promotes Tumor Progression and Modulates Immune Cell Infiltration in Glioma

Glioma is one of the most deadly types of brain cancer. As it is highly invasive, the prognosis for glioma patients remains dismal, with median survival rarely exceeding 16 months. Thus, developing a new prognostic biomarker for glioma and investigating its molecular mechanisms is necessary for the development of an efficient treatment strategy. In this study, we analyzed a cohort of 1,131 glioma patients using RNA-seq data from The Cancer Genome Atlas (TCGA project) and Gene Expression Omnibus (GSE4290 and GSE16011 datasets), and validated the results using the RNA-seq data of 1,018 gliomas from the Chinese Glioma Genome Atlas (CGGA project). We used the R language as the main tool for statistical analysis and data visualization. We found that NCAPG, a mitosis-associated chromosomal condensing protein, is highly expressed in glioma tissues. Furthermore, the expression of NCAPG increased significantly with the increase in tumor grade, and high NCAPG expression was found to be a predictor of poor overall survival in glioma patients (P < 0.001). This result shows that NCAPG expression could be an independent prognostic factor. Importantly, when the expression of NCAPG was knocked down, the CCK-8 assay revealed that the proliferation of glioma cells (LN-229 and T98G cell lines) decreased significantly compared with the control group. In addition, the healing rates of these cells were significantly lower in the si-NCAPG group than in the control group (P < 0.001). We then used the CIBERSORT algorithm to analyze the expression levels of 22 subpopulations of immune cells and found that NCAPG was significantly negatively correlated with natural killer cell activation. In addition, it was positively correlated with MHC-I molecules and ADAM17. Our study is first in comprehensively describing the high expression of NCAPG in glioma. It also shows that NCAPG can function as an independent prognostic predictor of glioma, and that targeting NCAPG can be a new strategy for the treatment of glioma patients.

Identification of Pathologic and Prognostic Genes in Prostate Cancer Based on Database Mining

Background: Prostate cancer (PCa) is an epithelial malignant tumor that occurs in the urinary system with high incidence and is the second most common cancer among men in the world. Thus, it is important to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. The present study aimed to identify potential biomarkers to reveal the underlying molecular mechanisms.

Methods: Differentially expressed genes (DEGs) between PCa tissues and matched normal tissues from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset were screened out by R software. Weighted gene co-expression network analysis was performed primarily to identify statistically significant genes for clinical manifestations. Protein–protein interaction (PPI) network analysis and network screening were performed based on the STRING database in conjunction with Cytoscape software. Hub genes were then screened out by Cytoscape in conjunction with stepwise algorithm and multivariate Cox regression analysis to construct a risk model. Gene expression in different clinical manifestations and survival analysis correlated with the expression of hub genes were performed. Moreover, the protein expression of hub genes was validated by the Human Protein Atlas database.

Results: A total of 1,621 DEGs (870 downregulated genes and 751 upregulated genes) were identified from the TCGA-PRAD dataset. Eight prognostic genes [BUB1, KIF2C, CCNA2, CDC20, CCNB2, PBK, RRM2, and CDC45] and four hub genes (BUB1, KIF2C, CDC20, and PBK) potentially correlated with the pathogenesis of PCa were identified. A prognostic model with good predictive power for survival was constructed and was validated by the dataset in GSE21032. The survival analysis demonstrated that the expression of RRM2 was statistically significant to the prognosis of PCa, indicating that RRM2 may potentially play an important role in the PCa progression.

Conclusion: The present study implied that RRM2 was associated with prognosis and could be used as a potential therapeutic target for PCa clinical treatment.

NCAPG is a prognostic biomarker of immune infiltration in non-small-cell lung cancer

Purpose: Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related deaths. The protein NCAPG plays a significant role in tumor development. Patients & methods: We set up a tissue microarray (containing 140 NSCLC and ten normal lung tissues) and performed immunohistochemistry to assess NCAPG expression in the tissues of 140 patients. The prognostic value of NCAPG in NSCLC was assessed using the univariate and multivariate Cox proportional hazards regression models and Kaplan-Meier plots. We analyzed the association between NCAPG and immune infiltration in NSCLC. Results: Multifactorial analysis and Kaplan-Meier plots revealed that upregulation of NCAPG expression was an independent factor in the prognosis of NSCLC. Data from CIBERSORT showed a negative correlation between NCAPG and the expression of memory CD4⁺ T cells, CD8⁺ T cells, dendritic cells, macrophages, mast cells and natural killer cells (p < 0.001). Gene set enrichment analysis revealed that cell cycle, adhesion and proliferation were significantly enriched in samples with a high NCAPG expression. Conclusion: NCAPG is a novel biomarker of prognosis and is associated with immune cell infiltration in the tumor microenvironment. Thus it may be a potential target in NSCLC treatment.

Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.

Construction of a Prognosis-Related Gene Signature by Weighted Gene Coexpression Network Analysis in Ewing Sarcoma

Background

Ewing sarcoma (ES) is the second most common pediatric bone tumor with a high rate of metastasis, high recurrence, and low survival rate. Therefore, the identification of new biomarkers which can improve the prognosis of ES patients is urgently needed.

Methods

Here, GSE17679 dataset was downloaded from GEO databases. WGCNA method was used to identify one module associating with OVS (overall vital survival) and event. cytoHubba was used to screen out 50 hub genes from the module genes. Then, GSE17679 dataset was randomly divided into train cohort and test cohort. Next, univariate Cox analysis, LASSO regression analysis, and multivariate Cox analysis were conducted on 50 hub genes combined with train cohort data to select pivotal genes. Finally, an optimal 7-gene-based risk assessment model was established, which was verified by test cohort, entire GSE17679, and two independent datasets (GSE63157 and TCGA-SARC).

Results

The results of the functional enrichment analysis revealed that the OVS and event-associated module were mainly enriched in the protein transcription, cell proliferation, and cell-cycle control. And the train cohort was divided into high-risk and low-risk subgroups based on the median risk score; the results showed that the survival of the low-risk subgroup was significantly longer than high-risk. ROC analysis revealed that AUC values of 1, 3, and 5-year survival were 0.85, 0.94, and 0.88, and Kaplan-Meier analysis also revealed that P value < 0.0001, indicating that this model was accurate, which was also verified in the test, entire cohort, and two independent datasets (GSE63157 and TCGA-SARC). Then, we performed a comprehensive analysis (differential expression analysis, correlation analysis and survival analysis) of seven pivotal genes, and found that four genes (NCAPG, KIF4A, NUF2 and CDC20) plays a more crucial role in the prognosis of ES.

Conclusion

Taken together, this study established an optimal 7-gene-based risk assessment model and identified 4 potential therapeutic targets, to improve the prognosis of ES patients.