Screening and predicted value of potential biomarkers for breast cancer using bioinformatics analysis

Liquid biopsy: An innovative tool in oncology. Where do we stand?

The Liquid Biopsy (LB) represents an ideal surrogate of tumor Tissue Biopsy (TB) when the aim is to obtain useful information on patient prognosis and personalized therapy. This technique renders it possible to isolate circulating tumor cells, circulating tumor DNA and other molecules from biological fluids. The most commonly used fluid for liquid biopsy is blood, but depending on the case it could be necessary to isolate the tumor components from other biological fluids such as urine, pleural effusion, cerebrospinal fluid, and others. The main advantages of liquid biopsy are the minimally invasive nature of the procedure and the possibility of analyzing all tumor clones. Limitations include difficulties in the isolation of tumor components and the requirement for highly sensitive analysis methods to avoid the risk of technical artifacts. In our review we will focus on describing circulating tumor biomarkers to illustrate the variety of information that can be obtained from biological fluids, particularly blood. We will then discuss the advanced biotechnological techniques suitable for the identification and analysis of Circulating Tumor DNA (ctDNA), examining both the potential and limitations of analytical methods and the clinical applicability of liquid biopsy for cancer diagnosis, monitoring, and therapeutic prediction. Additionally, we will explore strategies to enhance this valuable alternative to the more invasive tissue biopsy, with a dedicated focus on ongoing clinical studies, currently approved tests, and guideline recommendations.

Computational molecular insights into ibrutinib as a potent inhibitor of HER2-L755S mutant in breast cancer: gene expression studies, virtual screening, docking, and molecular dynamics analysis

Background

The proposed study integrates several advanced computational techniques to unravel the molecular mechanisms underlying breast cancer progression and drug resistance.

Methods

We investigated HER2-L755S mutation through a multi-step approach, including gene expression analysis, molecular docking, and molecular dynamics simulations.

Results and Discussion

By conducting a network-based analysis of gene expression data from breast cancer samples, key hub genes such as MYC, EGFR, CDKN2A, ERBB2, CDK1, E2F1, TOP2A, MDM2, TGFB1, and FOXM1 were identified, all of which are critical in tumor growth and metastasis. The study mainly focuses on the ERBB2 gene, which encodes the HER2 protein, and its common mutation HER2-L755S, associated with breast cancer and resistance to the drug lapatinib. The HER2-L755S mutation contributes to both tumorigenesis and therapeutic failure. To address this, alternative therapeutic strategies were investigated using combinatorial computational approaches. The stability and flexibility of the HER2-L755S mutation were evaluated through comparative molecular dynamics simulations over 1000 ns using Gromacs in the unbound (Apo) state. Virtual screening with Schrodinger Glide identified ibrutinib as a promising alternative to lapatinib for targeting the HER2-L755S mutant. Detailed docking and molecular dynamics simulations in the bound (Holo) state demonstrated that the HER2-L755S-ibrutinib complex exhibited higher binding affinity and lower binding energy, indicating more stable interactions compared to other complexes. MM-PBSA analysis revealed that the HER2-L755S-ibrutinib complex had more negative binding energy than the HER2-L755S-afatinib, HER2-L755S-lapatinib, and HER2-L755S-neratinib complexes, suggesting that ibrutinib forms the most stable complex with favorable binding interactions.

Conclusion

These results provide in-depth atomic-level insights into the binding mechanisms of these inhibitors, highlighting ibrutinib as a potentially effective inhibitor for the clinical treatment of breast cancer.

Predicting Axillary Lymph Node Metastasis in Young Onset Breast Cancer: A Clinical-Radiomics Nomogram Based on DCE-MRI

Background

Young onset breast cancer, diagnosed in women under 50, is known for its aggressive nature and challenging prognosis. Precisely forecasting axillary lymph node metastasis (ALNM) is essential for customizing treatment plans and enhancing patient results.

Objective

This research sought to create and verify a clinical-radiomics nomogram that combines radiomic features from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) with standard clinical predictors to improve the accuracy of predicting ALNM in young breast cancer patients.

Methods

We performed a retrospective analysis at one facility, involving the creation and validation of a nomogram in two stages.At first, a medical model was developed utilizing conventional indicators like tumor dimensions, molecular classifications, multifocal presence, and MRI-determined ALN status.A more detailed clinical-radiomics model was subsequently developed by integrating radiomic characteristics derived from DCE-MRI images.These models were created using logistic regression analyses on a training dataset, and their effectiveness was assessed by measuring the area under the receiver operating characteristic curve (AUC) in a separate validation dataset.

Results

The clinical-radiomics nomogram surpassed the clinical-only model, recording an AUC of 0.892 in the training dataset and 0.877 in the validation dataset.Significant predictors included MRI-reported ALN status and select radiomic features, which markedly enhanced the model's predictive capacity.

Conclusion

Integrating radiomic features with clinical predictors in a nomogram significantly improves ALNM prediction in young onset breast cancer, providing a valuable tool for personalized treatment planning. This study underscores the potential of merging advanced imaging data with clinical insights to refine oncological predictive models. Future research should expand to multicentric studies and include genomic data to boost the nomogram's generalizability and precision.

The landscape of immunogenic cell death-related genes predicts the overall survival and immune infiltration status of non-small-cell lung carcinoma

Background

Non-small cell lung cancer (NSCLC), which accounts for about 85 % of all lung cancers, currently exhibits insensitivity to most treatment regimens. Therefore, the identification of new and effective biomarkers for NSCLC is crucial for the development of treatment strategies. Immunogenic cell death (ICD), a form of regulated cell death capable of activating adaptive immune responses and generating long-term immune memory, holds promise for enhancing anti-tumor immunity and offering promising prospects for immunotherapy strategies in NSCLC.

Methods

Clinical information and expressive profiles of NSCLC genes were retrieved from the GEO and TCGA databases. By combining these databases, the researchers were able to identify the appropriate genes for use in forecasting outcomes of patients with this type of cancer. We further performed functional enrichment, gene variants and immune privilege correlation analysis to determine the underlying mechanisms. This was followed by univariate and multivariate Cox regression and LASSO regression analyses, we developed a prognostic risk model based on the TCGA cohort, which included 17 gene labels. The results of the external validation were then used to identify the appropriate genes for use in predicting the survival outcome of patients with this type of cancer. In addition, a nomogram was created to help visualise the clinical presentation of the patients. For the analyses, we performed 50 functional and immunoinfiltration assessments for two risk groups.

Results

Using 17 genes (AIRE, APOH, CDKN2A, CEACAM4, COL4A3, CPA, DBH, F10, FCGRB, FGFR4, MMP1, PGLYRP1, SCGB2A2, SLC9A3, UGT2B17 and VIP), The researchers then created a gene signature that could be used to identify patients with an increased risk of contracting cancer. They divided the patients into two groups based on their risk score. The low-risk group exhibited a better prognosis (P < 0.01). The survival curve demonstrated that ICD-related models could accurately predict patient prognosis. Conversely, high-risk subgroups were closely associated with immune-related signaling pathways. The analysis of immune infiltration also showed that the infiltration levels of most immune cells were higher in the high risk sub-group than in the low risk sub-group. In comparison to the low-risk group, the high-risk group was more susceptible to the immune-checkpoint blockade (ICB) treatment.

Conclusion

Our researchers utilized a gene model to analyze the immune inflammation and prognosis of patients with non-small-cell lung cancer (NSCLC). The discovery of new ICD-related genes could lead to the development of new targeted treatments for this condition.

Silencing the lncRNA EBLN3P Improves Prognosis in Patients with Invasive Breast Cancer by Directly Targeting miR-144-3p

The incidence of breast cancer, a malignant tumor that causes more harm to women, is increasing year by year, affecting women at a younger age. This paper describes the possible practical significance of lncRNA EBLN3P (EBLN3P) in predicting the prognosis of invasive breast cancer. EBLN3P and miR-144-3p levels in tissues and cells were detected by real-time quantitative PCR (RT-qPCR). The association between EBLN3P expression and prognosis of invasive breast cancer was investigated using Cox multivariate regression and Kaplan-Meier curve. The growth efficacy of EBLN3P expression on invasive breast cancer cells was evaluated by Cell Counting kit-8 (CCK-8) method and Transwell method, and the mechanism of EBLN3P targeting miR-144-3p was further studied. EBLN3P was elevated in invasive breast cancer, whereas survival rates were lower in patients with high EBLN3P level. EBLN3P directly targeted miR-144-3p to participate in the mechanism of invasive breast cancer, and EBLN3P knockdown had an inhibitory effect on tumor cells. Silencing EBLN3P inhibited the advancement of invasive breast cancer and was expected to be a promising therapeutic target for clinical intervention and prognosis.

Targeting AURKA with multifunctional nanoparticles in CRPC therapy

Castration-resistant prostate cancer (CRPC) presents significant therapeutic challenges due to its aggressive nature and poor prognosis. Targeting Aurora-A kinase (AURKA) has shown promise in cancer treatment. This study investigates the efficacy of ART-T cell membrane-encapsulated AMS@AD (CM-AMS@AD) nanoparticles (NPs) in a photothermal-chemotherapy-immunotherapy combination for CRPC. Bioinformatics analysis of the Cancer Genome Atlas-prostate adenocarcinoma (TCGA-PRAD) dataset revealed overexpression of AURKA in PCa, correlating with poor clinical outcomes. Single-cell RNA sequencing data from the GEO database showed a significant reduction in immune cells in CRPC. Experimentally, T cell membrane-biomimetic NPs loaded with the AURKA inhibitor Alisertib and chemotherapy drug DTX were synthesized and characterized by dynamic light scattering and transmission electron microscopy, showing good stability and uniformity (average diameter: 158 nm). In vitro studies demonstrated that these NPs inhibited CRPC cell proliferation, increased the G2/M cell population, and elevated apoptosis, confirmed by γH2AX expression. In vivo, CM-AMS@AD NPs accumulated in tumor tissues, significantly slowed tumor growth, decreased proliferation, increased apoptosis, and improved the immune environment, enhancing dendritic cell (DC) maturation and increasing CD8 + /CD4 + ratios. These findings suggest that CM-AMS@AD NPs offer a promising triple-combination therapy for CRPC, integrating photothermal, chemotherapy, and immunotherapy, with significant potential for future clinical applications.

Ubiquitination-Binding Enzyme 2C is Associated with Cancer Development and Prognosis and is a Potential Therapeutic Target

UBE2C (Ubiquitination-binding enzyme 2C), one of the E2 enzymes encoded in the human genome, is a component of the ubiquitin proteasome system and plays a pivotal role in regulating cell cycle progression. Moreover, UBE2C is highly expressed and may play a pivotal role in both high-incidence and high-mortality malignancies, including lung cancers, breast cancers, and esophageal cancers. UBE2C influences a number of key processes, including cell cycle progression, tumor invasion and metastasis, proliferation, and drug resistance. However, few articles have systematically summarized the role of UBE2C in cancer. The aim of this review is to describe the structure and function of UBE2C, focusing on the current status of UBE2C research in malignant tumors. Furthermore, this review presents the potential of UBE2C as a new therapeutic target and a diagnostic and prognostic biomarker. Finally, future research directions for UBE2C are proposed. It is of great value to explore the mechanism of action of UBE2C in the tumor microenvironment (TME). A comprehensive and coherent comprehension of UBE2C will undoubtedly facilitate its transition from fundamental research to clinical applications.

Network pharmacology provides new insights into the mechanism of traditional Chinese medicine and natural products used to treat pulmonary hypertension

BACKGROUND

Pulmonary hypertension (PH) is a rare cardiovascular disease with high morbidity and mortality rates. It is characterized by increased pulmonary arterial pressure. Current research into relevant therapeutic drugs and targets for PH, however, is insufficient still. Traditional Chinese medicine (TCM) and natural products have a long history as therapeutics for PH. Network pharmacology is an approach that integrates drug-target interactions and signaling pathways based on biomarkers information obtained from drug and disease databases. The concept of network pharmacology shows many similarities with the TCM philosophy. Network pharmacology help elucidate the mechanisms of TCM in PH. This review presents representative applications of network pharmacology in the study of the mechanisms of TCM and natural products for the treatment of PH.

METHODS

In this review, we used ("pulmonary hypertension" OR "pulmonary arterial hypertension" OR "chronic thromboembolic pulmonary hypertension") AND ("network pharmacology" OR "systematic pharmacology") as keywords to search for reports from PubMed, Web of Science, and Google Scholar databases from ten years ago. The studies were screened and those chosen are summarized here. The TCM and natural products inPH and their corresponding targets and signaling pathways are described. Additionally, we discuss the application of network pharmacology in the study of TCM in PH to provide insights for future application strategies.

RESULTS

Network pharmacology have shown that AKT-related pathways, HIF-1 signaling pathway, MAPK signaling pathway, TGF-β-Smad pathway, cell cycle-related pathways and inflammation-related pathways are the main signaling pathways enriched in the PH targets of TCM. Reservatrol, curcumol, genistin, formononetin, wogonin, luteolin, baicalein, berberine, triptolide and tanshinone llA are active ingredients specific for PH treatment. A number of databases and tools specific for the treatment of PH are used in network pharmacology and natural product research.

CONCLUSION

Through the reasonable combination of molecular docking, omics technology and bioinformatics technology, the mechanism of multi-targets can be explained more comprehensively. Analyzing the complex mechanism of TCM from the clinical perspective may be a potential development trend of network pharmacology. Combination of predicted targets and traditional pharmacology improves efficiency of drug development.

Transcriptional regulation of DLGAP5 by AR suppresses p53 signaling and inhibits CD8+T cell infiltration in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a challenging subtype with unclear biological mechanisms. Recently, the transcription factor androgen receptor (AR) and its regulation of the DLGAP5 gene have gained attention in TNBC pathogenesis. In this study, we found a positive correlation between high AR expression and TNBC cell proliferation and growth. Furthermore, we confirmed DLGAP5 as a critical downstream regulator of AR with high expression in TNBC tissues. Knockdown of DLGAP5 significantly inhibited TNBC cell proliferation, migration, and invasion. AR was observed to directly bind to the DLGAP5 promoter, enhancing its transcriptional activity and suppressing the activation of the p53 signaling pathway. In vivo experiments further validated that downregulation of AR or DLGAP5 inhibited tumor growth and enhanced CD8+T cell infiltration. This study highlights the crucial roles of AR and DLGAP5 in TNBC growth and immune cell infiltration. Taken together, AR inhibits the p53 signaling pathway by promoting DLGAP5 expression, thereby impacting CD8+T cell infiltration in TNBC.

Influence of H19 polymorphisms on breast cancer: risk assessment and prognostic implications via LincRNA H19/miR-675 and downstream pathways

Introduction

Breast cancer, as the most prevalent malignancy among women globally, continues to exhibit rising incidence rates, particularly in China. The disease predominantly affects women aged 40 to 60 and is influenced by both genetic and environmental factors. This study focuses on the role of H19 gene polymorphisms, investigating their impact on breast cancer susceptibility, clinical outcomes, and response to treatment.

Methods

We engaged 581 breast cancer patients and 558 healthy controls, using TaqMan assays and DNA sequencing to determine genotypes at specific loci (rs11042167, rs2071095, rs2251375). We employed in situ hybridization and immunohistochemistry to measure the expression levels of LincRNA H19, miR-675, MRP3, HOXA1, and MMP16 in formalin-fixed, paraffin-embedded samples. Statistical analyses included chi-squared tests, logistic regression, and Kaplan-Meier survival curves to evaluate associations between genetic variations, gene expression, and clinical outcomes.

Results

Genotypes AG at rs11042167, GT at rs2071095, and AC at rs2251375 were significantly associated with increased risk of breast cancer. Notably, the AA genotype at rs11042167 and TT genotype at rs2071095 were linked to favorable prognosis. High expression levels of LincRNA H19, miR-675, MRP3, HOXA1, and MMP16 in cancer tissues correlated with advanced disease stages and poorer survival rates. Spearman correlation analysis revealed significant positive correlations between the expression of LincRNA H19 and miR-675 and specific genotypes, highlighting their potential regulatory roles in tumor progression.

Discussion

The study underscores the critical roles of LincRNA H19 and miR-675 as prognostic biomarkers in breast cancer, with their overexpression associated with disease progression and adverse outcomes. The H19/LincRNA H19/miR-675/MRP3-HOXA1-MMP16 axis offers promising targets for new therapeutic strategies, reflecting the complex interplay between genetic markers and breast cancer pathology.

Conclusion

The findings confirm that certain H19 SNPs are associated with heightened breast cancer risk and that the expression profiles of related genetic markers can significantly influence prognosis and treatment response. These biomarkers hold potential as targets for personalized therapy and early detection strategies in breast cancer, underscoring the importance of genetic research in understanding and managing this disease.

Molecular Mechanisms Linking Genes and Vitamins of the Complex B Related to One-Carbon Metabolism in Breast Cancer: An In Silico Functional Database Study

Carcinogenesis is closely related to the expression, maintenance, and stability of DNA. These processes are regulated by one-carbon metabolism (1CM), which involves several vitamins of the complex B (folate, B2, B6, and B12), whereas alcohol disrupts the cycle due to the inhibition of folate activity. The relationship between nutrients related to 1CM (all aforementioned vitamins and alcohol) in breast cancer has been reviewed. The interplay of genes related to 1CM was also analyzed. Single nucleotide polymorphisms located in those genes were selected by considering the minor allele frequency in the Caucasian population and the linkage disequilibrium. These genes were used to perform several in silico functional analyses (considering corrected p-values < 0.05 as statistically significant) using various tools (FUMA, ShinyGO, and REVIGO) and databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and GeneOntology (GO). The results of this study showed that intake of 1CM-related B-complex vitamins is key to preventing breast cancer development and survival. Also, the genes involved in 1CM are overexpressed in mammary breast tissue and participate in a wide variety of biological phenomena related to cancer. Moreover, these genes are involved in alterations that give rise to several types of neoplasms, including breast cancer. Thus, this study supports the role of one-carbon metabolism B-complex vitamins and genes in breast cancer; the interaction between both should be addressed in future studies.

NKAIN1, as an oncogene, promotes the proliferation and metastasis of breast cancer, affecting its prognosis

Na, K-ATPase interaction (NKAIN) is a transmembrane protein family, which can interact with Na, K-ATPase β1 subunit. NKAIN1 plays an important role in alcohol-dependent diseases such as endometrial and prostate cancers. However, the relationship between NKAIN1 and human breast cancer has not been studied. Hence, this study aimed to explore the relationship between NKAIN1 expression and breast cancer. Data used in this study were mainly from the Cancer Genome Atlas, including differential expression analysis, Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, multiple Cox regression analysis, co-expression gene analysis, and gene set enrichment analysis. Analyses were performed using reverse transcription-quantitative polymerase chain reaction, western blot analysis, and immunohistochemistry on 46 collected samples. The knockdown or overexpression of NKAIN1 in vitro in MCF-7 and MDA-MB-231 cell lines altered the proliferation and migration abilities of tumor cells. In vivo experiments further confirmed that NKAIN1 knockdown effectively inhibited the proliferation and migration of cancer cells. Therefore, our study identified NKAIN1 as an oncogene that is highly expressed in breast cancer tissues. The findings highlight the potential of NKAIN1 as a molecular biomarker of breast cancer.

Eg5 and Diseases: From the Well-Known Role in Cancer to the Less-Known Activity in Noncancerous Pathological Conditions

Eg5 is a protein encoded by KIF11 gene and is primarily involved in correct mitotic cell division. It is also involved in nonmitotic processes such as polypeptide synthesis, protein transport, and angiogenesis. The scientific literature sheds light on the ubiquitous functions of KIF11 and its involvement in the onset and progression of different pathologies. This review focuses attention on two main points: (1) the correlation between Eg5 and cancer and (2) the involvement of Eg5 in noncancerous conditions. Regarding the first point, several tumors revealed an overexpression of this kinesin, thus pushing to look for new Eg5 inhibitors for clinical practice. In addition, the evaluation of Eg5 expression represents a crucial step, as its overexpression could predict a poor prognosis for cancer patients. Referring to the second point, in specific pathological conditions, the reduced activity of Eg5 can be one of the causes of pathological onset. This is the case of Alzheimer's disease (AD), in which Aβ and Tau work as Eg5 inhibitors, or in acquired immune deficiency syndrome (AIDS), in which Tat-mediated Eg5 determines the loss of CD4+ T-lymphocytes. Reduced Eg5 activity, due to mutations of KIF11 gene, is also responsible for pathological conditions such as microcephaly with or without chorioretinopathy, lymphedema, or intellectual disability (MCLRI) and familial exudative vitreous retinopathy (FEVR). In conclusion, this review highlights the double impact that overexpression or loss of function of Eg5 could have in the onset and progression of different pathological situations. This emphasizes, on one hand, a possible role of Eg5 as a potential biomarker and new target in cancer and, on the other hand, the promotion of Eg5 expression/activity as a new therapeutic strategy in different noncancerous diseases.

HOXD10 regulates intestinal permeability and inhibits inflammation of dextran sulfate sodium-induced ulcerative colitis through the inactivation of the Rho/ROCK/MMPs axis

Ulcerative colitis (UC) has been identified as a severe inflammatory disease with significantly increased incidence across the world. The detailed role and mechanism of HOXD10 in UC remain unclear. In present study, we found that HOXD10 was lowly expressed in UC samples and was notably decreased by dextran sulfate sodium (DSS) administration. Overexpression of HOXD10 dramatically ameliorated DSS-induced UC symptoms, including the loss of weight, increased disease activity index values, and the shortened colon length. Additionally, terminal-deoxynucleoitidyl transferase mediated nick end labeling and immunohistochemistry staining assays showed that HOXD10 overexpression suppressed cell apoptosis and facilitated proliferation of colon tissues after DSS treatment. Moreover, HOXD10 overexpression obviously suppressed DSS-triggered inflammatory response by decreasing the expression level of TNF-α, IL-6, and IL-1β. Furthermore, overexpression of HOXD10 effectively restored the intestinal permeability, thereby alleviating DSS-induced intestinal barrier dysfunction. Mechanistic study demonstrated that HOXD10 significantly reduced the activities of Rho/ROCK/MMPs axis in colon tissues of mice with UC. In conclusion, this study revealed that HOXD10 might effectively improve DSS-induced UC symptoms by suppressing the activation of Rho/ROCK/MMPs pathway.

Identification of allograft inflammatory factor-1 suppressing the progression and indicating good prognosis of osteosarcoma

BACKGROUND

Osteosarcoma is one of the most common cancers worldwide. Intense efforts have been made to elucidate the pathogeny, but the mechanisms of osteosarcoma are still not well understood. We aimed to investigate the potential biomarker, allograft inflammatory factor-1 (AIF1), affecting the progression and prognosis of osteosarcoma.

METHODS

Three microarray datasets were downloaded from GEO datasets and one was obtained from the TCGA dataset. The differentially expressed genes (DEGs) were identified. GO and KEGG functional enrichment analyses of overlapped DEGs were performed. The PPI network of overlapped DEGs was constructed by STRING and visualized with Cytoscape. Overall survival (OS) and Metastasis free survival (MFS) were analyzed from GSE21257. Finally, the effect of the most relevant core gene affecting the progression of osteosarcoma was examined in vitro.

RESULTS

One hundred twenty six DEGs were identified, consisting of 65 upregulated and 61 downregulated genes. Only AIF1 was significantly associated with OS and MFS. It was found that AIF1 could be enriched into the NF-κB signaling pathway. GSEA and ssGSEA analyses showed that AIF1 was associated with the immune invasion of tumors. Cell experiments showed that AIF1 was underexpressed in osteosarcoma cell lines, while the malignant propriety was attenuated after overexpressing the expression of AIF1. Moreover, AIF1 also affects the expression of the NF-κB pathway.

CONCLUSION

In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of osteosarcoma, and provide candidate targets for diagnosis and treatment of osteosarcoma.

An ensemble of bioinformatics and machine learning approaches to identify shared breast cancer biomarkers among diverse populations

Breast cancer continues to be a prominent cause for substantial loss of life among women globally. Despite established treatment approaches, the rising prevalence of breast cancer is a concerning trend regardless of geographical location. This highlights the need to identify common key genes and explore their biological significance across diverse populations. Our research centered on establishing a correlation between common key genes identified in breast cancer patients. While previous studies have reported many of the genes independently, our study delved into the unexplored realm of their mutual interactions, that may establish a foundational network contributing to breast cancer development. Machine learning algorithms were employed for sample classification and key gene selection. The best performance model further selected the candidate genes through expression pattern recognition. Subsequently, the genes common in all the breast cancer patients from India, China, Czech Republic, Germany, Malaysia and Saudi Arabia were selected for further study. We found that among ten classifiers, Catboost exhibited superior performance with an average accuracy of 92%. Functional enrichment analysis and pathway analysis revealed that calcium signaling pathway, regulation of actin cytoskeleton pathway and other cancer-associated pathways were highly enriched with our identified genes. Notably, we observed that these genes regulate each other, forming a complex network. Additionally, we identified PALMD gene as a novel potential biomarker for breast cancer progression. Our study revealed key gene modules forming a complex network that were consistently expressed in different populations, affirming their critical role and biological significance in breast cancer. The identified genes hold promise as prospective biomarkers of breast cancer prognosis irrespective of country of origin or ethnicity. Future investigations will expand upon these genes in a larger population and validate their biological functions through in vivo analysis.

Preparation of hepatocellular carcinoma mRNA vaccines based on potential tumor targets and immunophenotypes

Background

With the development of messenger RNA (mRNA)-based therapeutics for malignant tumor, mRNA vaccines have shown considerable promise for tumor immunotherapy. Immunophenotypes can reflect the tumor microenvironment, which might have a significant influence on the effect of immunotherapy. This study seeks to discover and validate effective antigens that can be employed to develop mRNA vaccines for hepatocellular carcinoma (HCC) and to construct immunophenotypes and immune landscapes to identify potential beneficiaries.

Methods

RNA sequencing (RNASeq) data, mutation information, and clinical information were obtained from HCC patients and control cases from The Cancer Genome Atlas - Liver Hepatocellular Carcinoma (TCGA-LIHC), International Cancer Genome Consortium - Liver Cancer (ICGC-LIRI) and Gene Expression Omnibus (GEO) cohorts. Gene Expression Profiling Interactive Analysis (GEPIA2.0), cBioPortal for Cancer Genomics (cBioPortal), Tumor IMmune Estimation Resource (TIMER2.0), and immunohistochemistry (IHC) were employed to discover tumor antigens. ConsensusClusterPlus was employed to perform consistency matrix building and immunophenotypic clustering. Single sample gene set enrichment analysis (ssGSEA), ESTIMATE and monocle2 were employed to map immune cell distribution. Weighted correlation network analysis (WGCNA) was employed to identify potential gene modules that influence the efficacy of mRNA vaccines.

Results

Six antigen targets were discovered in the TCGA cohort, including AURKA, CDC25C, KPNA2, MCM3, NEK2 and TUBG1, which were associated with antigen-presenting cell infiltration and poor prognosis. IHC scores of AURKA, CDC25C and MCM3 were higher in tumor tissues, and high scores of AURKA and CDC25C indicated poor prognosis in the validation cohort. Five immunophenotypes derived from TCGA-LIHC and ICGC-LIRI cohorts were consistent. Furthermore, increased expression of blue and black modules may reduce vaccine responsiveness.

Conclusions

AURKA, CDC25C, KPNA2, MCM3, NEK2 and TUBG1 may be potential targets for mRNA vaccine development for HCC, especially AURKA and CDC25C. HCC patients with IS1 and IS5 subtypes perhaps present an autoimmunosuppressed state, then IS2 and IS3 subtypes perhaps the potential beneficiaries.

Identification of Novel Prognostic Biomarkers for Colorectal Cancer by Bioinformatics Analysis

BACKGROUND/AIMS

Colorectal cancer (CRC) ranks third among malignancies in terms of global incidence and has a poor prognosis. The identification of effective diagnostic and prognostic biomarkers is critical for CRC treatment. This study intends to explore novel genes associated with CRC progression via bioinformatics analysis.

MATERIALS AND METHODS

Dataset GSE184093 was selected from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between CRC and noncancerous specimens. Functional enrichment analyses were implemented for probing the biological functions of DEGs. Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter databases were employed for gene expression detection and survival analysis, respectively. Western blotting and real-time quantitative polymerase chain reaction were employed for detecting molecular protein and messenger RNA levels, respectively. Flow cytometry, Transwell, and CCK-8 assays were utilized for examining the effects of GBA2 and ST3GAL5 on CRC cell behaviors.

RESULTS

There were 6464 DEGs identified, comprising 3005 downregulated DEGs (dDEGs) and 3459 upregulated DEGs (uDEGs). Six dDEGs were significantly associated with the prognoses of CRC patients, including PLCE1, PTGS1, AMT, ST8SIA1, ST3GAL5, and GBA2. Upregulating ST3GAL5 or GBA2 repressed the malignant behaviors of CRC cells.

CONCLUSION

We identified 6 genes related to CRC progression, which could improve the disease prognosis and treatment.

Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis

Breast cancer (BC) has emerged as an extremely destructive malignancy, causing significant harm to female patients and society at large. Proteomic research holds great promise for early diagnosis and treatment of diseases, and the integration of proteomics with genomics can offer valuable assistance in the early diagnosis, treatment, and improved prognosis of BC patients. In this study, we downloaded breast cancer protein expression data from The Cancer Genome Atlas (TCGA) and combined proteomics with genomics to construct a proteomic-based prognostic model for BC. This model consists of nine proteins (HEREGULIN, IDO, PEA15, MERIT40_pS29, CIITA, AKT2, CD171 DVL3, and CABL9). The accuracy of the model in predicting the survival prognosis of BC patients was further validated through risk curve analysis, survival curve analysis, and independent prognostic analysis. We further confirmed the impact of differential expression of these nine key proteins on overall survival in BC patients, and the differential expression of the key proteins and their encoding genes was validated using immunohistochemical staining. Enrichment analysis revealed functional associations primarily related to PPAR signaling pathway, steroid hormone metabolism, chemokine signaling pathway, DNA conformation changes, immunoglobulin production, and immunoglobulin complex in the high- and low-risk groups. Immune infiltration analysis revealed differential expression of immune cells between the high- and low-risk groups, providing a theoretical basis for subsequent immunotherapy. The model constructed in this study can predict the survival of BC patients, and the identified key proteins may serve as biomarkers to aid in the early diagnosis of BC. Enrichment analysis and immune infiltration analysis provide a necessary theoretical basis for further exploration of the molecular mechanisms and subsequent immunotherapy.

Characteristics of HPV integration in cervical adenocarcinoma and squamous carcinoma

PURPOSE

HPV integration usually occurs in HPV-related cancer, and is the main cause of cancer. But the carcinogenic mechanism of HPV integration is unclear. The study aims to provide a theoretical basis for understanding the pathogenesis of cervical adenocarcinoma (AC) and cervical squamous carcinoma (SCC).

METHODS

We used HPV capture sequencing to obtain HPV integration sites in AC and SCC, and analyzed cytobands, distribution of genetic and genomic elements, identified integration hotspot genes, clinicopathological parameters, breakpoints of HPV16 and performed pathway analysis. Then we conducted immunohistochemical (IHC) assay to preliminarily verify the expression of most frequently integrated genes in AC, STARD3 and ERBB2.

RESULTS

The results revealed that the most frequently observed integrated cytoband was 17q12 in AC and 21p11.2 in SCC, respectively. The breakpoints in both AC and SCC were more tended to occur within gene regions, compared to intergenetic regions. Compared to SCC samples, AC samples had a higher prevalence of genomic elements. In AC, HPV integration has no significantly difference with clinicopathological parameters, but in SCC integration correlated with differentiation (P < 0.05). Breakpoints of HPV in SCC located in LCR more frequently compared to AC, which destroyed the activation of promoter p97. Hotspot genes of HPV integration were STARD3 and ERBB2 in AC, and RNA45S rDNA and MIR3648-1 in SCC, respectively. Meanwhile, we preliminarily proved that the expression of STARD3 and ERBB2, the most frequently integrated genes, would increase after integration.

CONCLUSION

These results suggested that HPV may utilize the powerful hosts' promoters to express viral oncogenes and overexpression of viral oncogenes plays a significant role in the carcinogenesis of SCC. In AC, HPV integration may affect hosts' oncogenes, and the dysregulation of oncogenes may primarily contribute to progression of AC.

Undifferentially Expressed CXXC5 as a Transcriptionally Regulatory Biomarker of Breast Cancer

This work hypothesizes that some genes undergo radically changed transcription regulations (TRs) in breast cancer (BC), but don't show differential expressions for unknown reasons. The TR of a gene is quantitatively formulated by a regression model between the expression of this gene and multiple transcription factors (TFs). The difference between the predicted and real expression levels of a gene in a query sample is defined as the mqTrans value of this gene, which quantitatively reflects its regulatory changes. This work systematically screens the undifferentially expressed genes with differentially expressed mqTrans values in 1036 samples across five datasets and three ethnic groups. This study calls the 25 genes satisfying the above hypothesis in at least four datasets as dark biomarkers, and the strong dark biomarker gene CXXC5 (CXXC Finger Protein 5) is even supported by all the five independent BC datasets. Although CXXC5 does not show differential expressions in BC, its transcription regulations show quantitative associations with BCs in diversified cohorts. The overlapping long noncoding RNAs (lncRNAs) may have contributed their transcripts to the expression miscalculations of dark biomarkers. The mqTrans analysis serves as a complementary view of the transcriptome-based detections of biomarkers that are ignored by many existing studies.

Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis

Genomics and bioinformatics methods were used to screen genes and molecular markers correlated with colorectal cancer incidence and progression, and their biological functions were analyzed. Differentially expressed genes were obtained using the GEO2R program following colorectal cancer chip data GSE44076 retrieval from the Gene Expression Omnibus gene expression comprehensive database. An online database (David) that combines annotation, visualization, and gene discovery was utilized for investigating genes. Pathway and protein analyses were performed via resources from the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Visual analysis of the KEGG pathway was carried out according to ClueGO and CluePedia to establish the PPI network of gene interaction between pathways; the genes with the highest connectivity were screened by the molecular complex detection analysis method as Hub genes in this study; gene expression was verified by GEPIA online analysis tool, and Kaplan-Meier survival curve was drawn for prognosis analysis. By analyzing GSE44076 microarray data, 86 genes were selected, and colorectal cancer tissues' upregulation was observed in 27 genes and downregulation in 59 ones. GO assessment revealed that the differentially expressed genes were basically correlated with retinol dehydrogenase activity, carbon dehydrogenase activity, collagen-containing extracellular matrix, anchored component of memory, and cellular hormone metabolic process. Moreover, the KEGG assessment revealed that the differential genes contained various signal pathways such as retinol metabolism, chemical carotenogenesis, and nitrogen metabolism. Through further analysis of the PPI protein network, 4 clusters were obtained, and 16 Hub genes were screened out by combining the degree of each gene. Through the analysis of each gene on the prognosis of colon cancer through the GEPIA online analysis website, it was found that the expression levels of AQP8, CXCL8, and ZG16 genes were remarkably associated with colon cancer prognosis (P < 0.05). Genomics and bioinformatics methods can effectively analyze the genes and molecular markers correlated with colorectal cancer incidence and progression, help to systematically clarify the molecular mechanism of 16 key genes in colorectal cancer development and progression, and provide a theoretically valid insight for the screening of diagnostic markers of colorectal cancer and the selection of accurate targets for drug therapy.

Expression profiling of luminal B breast tumor in Indian women

PURPOSE

In this study, we aimed at profiling of luminal B breast cancer specific gene expression pattern in Indian women using mRNA-seq and validation based on TCGA expression data.

METHODS

RNA isolated from luminal B tumor and adjacent normal tissues was used for library construction and sequencing. Reference-based assemblies of these reads were used for differential gene expression analysis using DeSeq2. The DEGs were evaluated using TCGA expression data. Kaplan-Meier survival method was used to evaluate association between genes showing luminal B specific differential expression pattern and breast cancer prognosis and statistical significance was assessed using log-rank test. Alternate splicing analysis was done using rmats.

RESULTS

Differential expression analysis identified 2371 differentially expressed genes (DEGs) in luminal B breast tumors in comparison with adjacent normal tissues of Indian Women. Of them, 1692 DEGs were validated using TCGA luminal B paired samples. Integration of this data with the DEGs obtained by comparative analysis of unpaired luminal B with luminal A unpaired samples from TCGA resulted in 291 DEGs showing luminal B specific expression pattern. Further, 26 genes of prognostic value were identified. Differential splicing analysis between luminal B tumors and adjacent normal tissues in our cohort led to the identification of 687 genes showing significant differential alternate splicing events.

CONCLUSION

This study profiled gene expression pattern of luminal B tumors of Indian women and identified 26 key genes of prognostic value for luminal B breast cancer. This study also profiled differential alternate splicing and identified important alternate splicing events in luminal B breast cancer.

DLGAP5 Regulates the Proliferation, Migration, Invasion, and Cell Cycle of Breast Cancer Cells via the JAK2/STAT3 Signaling Axis

The aim of this study was to discover new biomarkers to detect breast cancer (BC), which is an aggressive cancer with a high mortality rate. In this study, bioinformatic analyses (differential analysis, weighted gene co-expression network analysis, and machine learning) were performed to identify potential candidate genes for BC to study their molecular mechanisms. Furthermore, Quantitative Real-time PCR and immunohistochemistry assays were used to examine the protein and mRNA expression levels of a particular candidate gene (DLGAP5). And the effects of DLGAP5 on cell proliferation, migration, invasion, and cell cycle were further assessed using the Cell Counting Kit-8 assay, colony formation, Transwell, wound healing, and flow cytometry assays. Moreover, the changes in the JAK2/STAT3 signaling-pathway-related proteins were detected by Western Blot. A total of 44 overlapping genes were obtained by differential analysis and weighted gene co-expression network analysis, of which 25 genes were found in the most tightly connected cluster. Finally, NEK2, CKS2, UHRF1, DLGAP5, and FAM83D were considered as potential biomarkers of BC. Moreover, DLGAP5 was highly expressed in BC. The down-regulation of DLGAP5 may inhibit the proliferation, migration, invasion, and cell cycle of BC cells, and the opposite was true for DLGAP5 overexpression. Correspondingly, silencing or overexpression of the DLGAP5 gene inhibited or activated the JAK2/STAT3 signaling pathway, respectively. DLGAP5, as a potential biomarker of BC, may impact the cell proliferation, migration, invasion, cell cycle, and BC development by modulating the JAK2/STAT3 signaling pathway.

DEPDC1B-mediated USP5 deubiquitination of β-catenin promotes breast cancer metastasis by activating the wnt/β-catenin pathway

Breast cancer has become the malignant disease with the highest morbidity and mortality among female cancer patients. The prognosis of metastatic breast cancer is very poor, and the therapeutic effects still need to be improved. The molecular mechanism of breast cancer has not been fully clarified. Bioinformatics analysis was used to find the differentially expressed gene that affects the occurrence and development of breast cancer. Furthermore, scratch assays, Transwell assays, immunofluorescence, and Western blotting were used to determine the biological behavior of breast cancer cells affected by DEP domain-containing protein 1B (DEPDC1B). The molecular mechanism was investigated by mass spectrometry analysis, coimmunoprecipitation, and ubiquitin assays. Here, we found that DEPDC1B was highly expressed in breast cancer cells and tissues and was associated with lower overall survival (OS) in patients. We found that DEPDC1B interference significantly inhibited tumor invasion and migration in vitro and tumor metastasis in vivo. Mechanistically, DEPDC1B was first shown to activate the wnt/β-catenin signaling pathway as an oncogene in breast cancer cells. In addition, we also confirmed the interaction between DEPDC1B, ubiquitin-specific protease 5 (USP5), and β-catenin. Then, we found that DEPDC1B mediates the deubiquitination of β-catenin via USP5, which promotes cell invasion and migration. Our findings provide new insights into the carcinogenic mechanism of DEPDC1B, suggesting that DEPDC1B can be considered a potential therapeutic target for breast cancer.NEW & NOTEWORTHY By using bioinformatics analysis and the experimental techniques of cell biology and molecular biology, we found that DEP domain-containing protein 1B (DEPDC1B) can promote the invasion and migration of breast cancer cells and that DEPDC1B mediates the deubiquitination of β-catenin by ubiquitin-specific protease 5 (USP5), thus activating the wnt/β-catenin pathway. Our findings provide new insights into the carcinogenic mechanism of DEPDC1B, suggesting that DEPDC1B can be used as a potential therapeutic target for breast cancer.

Role of Ubiquitin Ligases and Conjugases in Targeted Cancer Therapy

The ubiquitin proteasome system regulates the activity of many short-lived proteins in cells [...].

Bioinformatics analysis to identify breast cancer-related potential targets and candidate small molecule drugs

OBJECTIVE

The purpose of this study is to identify potential targets associated with breast cancer and screen potential small molecule drugs using bioinformatics analysis.

METHODS

DEGs analysis of breast cancer tissues and normal breast tissues was performed using R language limma analysis on the GSE42568 and GSE205185 datasets. Functional enrichment analysis was conducted on the intersecting DEGs. The STRING analysis platform was used to construct a PPI network, and the top 10 core nodes were identified using Cytoscape software. QuartataWeb was utilized to build a target-drug interaction network and identify potential drugs. Cell survival and proliferation were assessed using CCK8 and colony formation assays. Cell cycle analysis was performed using flow cytometry. Western blot analysis was conducted to assess protein levels of PLK1, MELK, AURKA, and NEK2.

RESULTS

A total of 54 genes were consistently upregulated in both datasets, which were functionally enriched in mitotic cell cycle and cell cycle-related pathways. The 226 downregulated genes were functionally enriched in pathways related to hormone level regulation and negative regulation of cell population proliferation. Ten key genes, namely CDK1, CCNB2, ASPM, AURKA, TPX2, TOP2A, BUB1B, MELK, RRM2, and NEK2 were identified. The potential drug Fostamatinib was predicted to target AURKA, MELK, CDK1, and NEK2. In vitro experiments demonstrated that Fostamatinib inhibited the proliferation of breast cancer cells, induced cell arrest in the G2/M phase, and down-regulated MELK, AURKA, and NEK2 proteins.

CONCLUSION

In conclusion, Fostamatinib shows promise as a potential drug for the treatment of breast cancer by regulating the cell cycle and inhibiting the proliferation of breast cancer cells.

A novel signature based on cancer-associated fibroblast genes to predict prognosis, immune feature, and therapeutic response in breast cancer

Breast cancer (BC) ranks first in the incidence of tumors in women and remains the most prevalent malignancy in women worldwide. Cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) profoundly influence the progression, recurrence, and therapeutic resistance in BC. Here, we intended to establish a risk signature based on screened CAF-associated genes in BC (BCCGs) for patient stratification. Initially, BCCGs were screened by a combination of several CAF gene sets. The identified BCGGs were found to differ significantly in the overall survival (OS) of BC patients. Accordingly, we constructed a prognostic prediction signature of 5 BCCGs, which were independent prognostic factors associated with BC based on univariate and multivariate Cox regression. The risk model divided patients into low- and high-risk groups, accompanied by different OS, clinical features, and immune infiltration characteristics. Receiver operating characteristic (ROC) curves and a nomogram further validated the predictive performance of the prognostic model. Notably, 21 anticancer agents targeting these BCCGs possessed better sensitivity in BC patients. Meanwhile, the elevated expression of the majority of immune checkpoint genes suggested that the high-risk group may benefit more from immune checkpoint inhibitors (ICIs) therapy. Taken together, our well-established model is a robust instrument to precisely and comprehensively predict the prognosis, immune features, and drug sensitivity in BC patients, for combating BC.

Identification of New Key Genes and Their Association with Breast Cancer Occurrence and Poor Survival Using In Silico and In Vitro Methods

Breast cancer is one of the most prevalent types of cancer diagnosed globally and continues to have a significant impact on the global number of cancer deaths. Despite all efforts of epidemiological and experimental research, therapeutic concepts in cancer are still unsatisfactory. Gene expression datasets are widely used to discover the new biomarkers and molecular therapeutic targets in diseases. In the present study, we analyzed four datasets using R packages with accession number GSE29044, GSE42568, GSE89116, and GSE109169 retrieved from NCBI-GEO and differential expressed genes (DEGs) were identified. Protein-protein interaction (PPI) network was constructed to screen the key genes. Subsequently, the GO function and KEGG pathways were analyzed to determine the biological function of key genes. Expression profile of key genes was validated in MCF-7 and MDA-MB-231 human breast cancer cell lines using qRT-PCR. Overall expression level and stage wise expression pattern of key genes was determined by GEPIA. The bc-GenExMiner was used to compare expression level of genes among groups of patients with respect to age factor. OncoLnc was used to analyze the effect of expression levels of LAMA2, TIMP4, and TMTC1 on the survival of breast cancer patients. We identified nine key genes, of which COL11A1, MMP11, and COL10A1 were found up-regulated and PCOLCE2, LAMA2, TMTC1, ADAMTS5, TIMP4, and RSPO3 were found down-regulated. Similar expression pattern of seven among nine genes (except ADAMTS5 and RSPO3) was observed in MCF-7 and MDA-MB-231 cells. Further, we found that LAMA2, TMTC1, and TIMP4 were significantly expressed among different age groups of patients. LAMA2 and TIMP4 were found significantly associated and TMTC1 was found less correlated with breast cancer occurrence. We found that the expression level of LAMA2, TIMP4, and TMTC1 was abnormal in all TCGA tumors and significantly associated with poor survival.

Demystifying the Role of Prognostic Biomarkers in Breast Cancer through Integrated Transcriptome and Pathway Enrichment Analyses

Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of death in women. Researchers have discovered an increasing number of molecular targets for BC prognosis and therapy. However, it is still urgent to identify new biomarkers. Therefore, we evaluated biomarkers that may contribute to the diagnosis and treatment of BC. We searched TCGA datasets and identified differentially expressed genes (DEGs) by comparing tumor (100 samples) and non-tumor (100 samples) tissues using the Deseq2 package. Pathway and functional enrichment analysis of the DEGs was performed using the DAVID (Database for Annotation, Visualization, and Integrated Discovery) database. The protein–protein interaction (PPI) network was identified using the STRING database and visualized through Cytoscape software. Hub gene analysis of the PPI network was completed using cytohubba plugins. The associations between the identified genes and overall survival (OS) were analyzed using a Kaplan–Meier plot. Finally, we have identified hub genes at the transcriptome level. A total of 824 DEGs were identified, which were mostly enriched in cell proliferation, signal transduction, and cell division. The PPI network comprised 822 nodes and 12,145 edges. Elevated expression of the five hub genes AURKA, BUB1B, CCNA2, CCNB2, and PBK are related to poor OS in breast cancer patients. 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. The enriched functions and pathways included the cell cycle, oocyte meiosis, and the p53 signaling pathway. The identified five hub genes in breast cancer have the potential to become useful targets for the diagnosis and treatment of 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.

Screening of Therapeutic Targets for Pancreatic Cancer by Bioinformatics Methods

Pancreatic cancer (PC) has the lowest survival rate and the highest mortality rate among all cancers due to lack of effective treatments. The objective of the current study was to identify potential therapeutic targets in PC. Three transcriptome datasets, namely GSE62452, GSE46234, and GSE101448, were analyzed for differentially expressed genes (DEGs) between cancer and normal samples. Several bioinformatics methods, including functional analysis, pathway enrichment, hub genes, and drugs were used to screen therapeutic targets for PC. Fisher's exact test was used to analyze functional enrichments. To screen DEGs, the paired t-test was employed. The statistical significance was considered at p <0.05. Overall, 60 DEGs were detected. Functional enrichment analysis revealed enrichment of the DEGs in "multicellular organismal process", "metabolic process", "cell communication", and "enzyme regulator activity". Pathway analysis demonstrated that the DEGs were primarily related to "Glycolipid metabolism", "ECM-receptor interaction", and "pathways in cancer". Five hub genes were examined using the protein-protein interaction (PPI) network. Among these hub genes, 10 known drugs targeted to the CPA1 gene and CLPS gene were found. Overall, CPA1 and CLPS genes, as well as candidate drugs, may be useful for PC in the future.

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.

Validation of a Disease-Free Survival Prediction Model Using UBE2C and Clinical Indicators in Breast Cancer Patients

Objective

To explore the validation of a disease-free survival (DFS) model for predicting disease progression based on the combination of ubiquitin-conjugating enzyme E2 C (UBE2C) levels and clinical indicators in breast cancer patients.

Methods

We enrolled 121 patients with breast cancer, collected their baseline characteristics and follow-up data, and analyzed the UBE2C levels in tumor tissues. We studied the relationship between UBE2C expression in tumor tissues and disease progression events of patients. We used the Kaplan-Meier method for identifying the disease-free survival rate of patients, and the multivariate Cox regression analysis to study the risk factors affecting the prognosis of patients. We sought to develop and validate a model for predicting disease progression.

Results

We found that the level of expression of UBE2C could effectively distinguish the prognosis of patients. In the Receiver Operating Characteristic (ROC) curve analysis, the Area under the ROC Curve (AUC) = 0.826 (0.714-0.938) indicating that high levels of UBE2C was a high-risk factor for poor prognosis. After evaluating different models using the ROC curve, Concordance index (C-index), calibration curve, Net Reclassification Index (NRI), Integrated Discrimination Improvement Index (IDI), and other methods, we finally developed a model for the expression of Tumor-Node (TN) staging using Ki-67 and UBE2C, which had an AUC=0.870, 95% CI of 0.786-0.953. The traditional TN model had an AUC=0.717, and 95% CI of 0.581-0.853. Decision Curve Analysis (DCA) and Clinical Impact Curve (CIC) analysis indicated that the model had good clinical benefits and it was relatively simple to use.

Conclusion

We found that high levels of UBE2C was a high-risk factor for poor prognosis. The use of UBE2C in addition to other breast cancer-related indicators effectively predicted the possible disease progression, thus providing a reliable basis for clinical decision-making.

Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms

Detecting breast cancer (BC) at the initial stages of progression has always been regarded as a lifesaving intervention. With modern technology, extensive studies have unraveled the complexity of BC, but the current standard practice of early breast cancer screening and clinical management of cancer progression is still heavily dependent on tissue biopsies, which are invasive and limited in capturing definitive cancer signatures for more comprehensive applications to improve outcomes in BC care and treatments. In recent years, reviews and studies have shown that liquid biopsies in the form of blood, containing free circulating and exosomal microRNAs (miRNAs), have become increasingly evident as a potential minimally invasive alternative to tissue biopsy or as a complement to biomarkers in assessing and classifying BC. As such, in this review, the potential of miRNAs as the key BC signatures in liquid biopsy are addressed, including the role of artificial intelligence (AI) and machine learning platforms (ML), in capitalizing on the big data of miRNA for a more comprehensive assessment of the cancer, leading to practical clinical utility in BC management.

Bioinformatics and network-based screening and discovery of potential molecular targets and small molecular drugs for breast cancer

Accurate identification of molecular targets of disease plays an important role in diagnosis, prognosis, and therapies. Breast cancer (BC) is one of the most common malignant cancers in women worldwide. Thus, the objective of this study was to accurately identify a set of molecular targets and small molecular drugs that might be effective for BC diagnosis, prognosis, and therapies, by using existing bioinformatics and network-based approaches. Nine gene expression profiles (GSE54002, GSE29431, GSE124646, GSE42568, GSE45827, GSE10810, GSE65216, GSE36295, and GSE109169) collected from the Gene Expression Omnibus (GEO) database were used for bioinformatics analysis in this study. Two packages, LIMMA and clusterProfiler, in R were used to identify overlapping differential expressed genes (oDEGs) and significant GO and KEGG enrichment terms. We constructed a PPI (protein-protein interaction) network through the STRING database and identified eight key genes (KGs) EGFR, FN1, EZH2, MET, CDK1, AURKA, TOP2A, and BIRC5 by using six topological measures, betweenness, closeness, eccentricity, degree, MCC, and MNC, in the Analyze Network tool in Cytoscape. Three online databases GSCALite, Network Analyst, and GEPIA were used to analyze drug enrichment, regulatory interaction networks, and gene expression levels of KGs. We checked the prognostic power of KGs through the prediction model using the popular machine learning algorithm support vector machine (SVM). We suggested four TFs (TP63, MYC, SOX2, and KDM5B) and four miRNAs (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, and hsa-mir-23b-3p) as key transcriptional and posttranscriptional regulators of KGs. Finally, we proposed 16 candidate repurposing drugs YM201636, masitinib, SB590885, GSK1070916, GSK2126458, ZSTK474, dasatinib, fedratinib, dabrafenib, methotrexate, trametinib, tubastatin A, BIX02189, CP466722, afatinib, and belinostat for BC through molecular docking analysis. Using BC cell lines, we validated that masitinib inhibits the mTOR signaling pathway and induces apoptotic cell death. Therefore, the proposed results might play an effective role in the treatment of BC patients.

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.

Advances of Epigenetic Biomarkers and Epigenome Editing for Early Diagnosis in Breast Cancer

Epigenetic modifications are known to regulate cell phenotype during cancer progression, including breast cancer. Unlike genetic alterations, changes in the epigenome are reversible, thus potentially reversed by epi-drugs. Breast cancer, the most common cause of cancer death worldwide in women, encompasses multiple histopathological and molecular subtypes. Several lines of evidence demonstrated distortion of the epigenetic landscape in breast cancer. Interestingly, mammary cells isolated from breast cancer patients and cultured ex vivo maintained the tumorigenic phenotype and exhibited aberrant epigenetic modifications. Recent studies indicated that the therapeutic efficiency for breast cancer regimens has increased over time, resulting in reduced mortality. Future medical treatment for breast cancer patients, however, will likely depend upon a better understanding of epigenetic modifications. The present review aims to outline different epigenetic mechanisms including DNA methylation, histone modifications, and ncRNAs with their impact on breast cancer, as well as to discuss studies highlighting the central role of epigenetic mechanisms in breast cancer pathogenesis. We propose new research areas that may facilitate locus-specific epigenome editing as breast cancer therapeutics.

Diagnostic, Prognostic, and Immunological Roles of HELLS in Pan-Cancer: A Bioinformatics Analysis

Background

Inappropriate repair of DNA damage drives carcinogenesis. Lymphoid-specific helicase (HELLS) is an important component of the chromatin remodeling complex that helps repair DNA through various mechanisms such as DNA methylation, histone posttranslational modification, and nucleosome remodeling. Its role in human cancer initiation and progression has garnered recent attention. Our study aims to provide a more systematic and comprehensive understanding of the role of HELLS in the development and progression of multiple malignancies through analysis of HELLS in cancers.

Methods

We explored the role of HELLS in cancers using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. Multiple web platforms and software were used for data analysis, including R, Cytoscape, HPA, Archs4, TISIDB, cBioPortal, STRING, GSCALite, and CancerSEA.

Results

High HELLS expression was found in a variety of cancers and differentially expressed across molecular and immune subtypes. HELLS was involved in many cancer pathways. Its expression positively correlated with Th2 and Tcm cells in most cancers. It also correlated with genetic markers of immunomodulators in various cancers.

Conclusions

Our study elucidates the role HELLS plays in promotion, inhibition, and treatment of different cancers. HELLS is a potential cancer diagnostic and prognostic biomarker with immune, targeted, or cytotoxic therapeutic value. This work is a prerequisite to clinical validation and treatment of HELLS in cancers.

Environmental Phenol and Paraben Exposure Risks and Their Potential Influence on the Gene Expression Involved in the Prognosis of Prostate Cancer

Prostate cancer (PCa) is one of the leading malignant tumors in US men. The lack of understanding of the molecular pathology on the risk of food supply chain exposures of environmental phenol (EP) and paraben (PB) chemicals limits the prevention, diagnosis, and treatment options. This research aims to utilize a risk assessment approach to demonstrate the association of EP and PB exposures detected in the urine samples along with PCa in US men (NHANES data 2005−2015). Further, we employ integrated bioinformatics to examine how EP and PB exposure influences the molecular pathways associated with the progression of PCa. The odds ratio, multiple regression model, and Pearson coefficients were used to evaluate goodness-of-fit analyses. The results demonstrated associations of EPs, PBs, and their metabolites, qualitative and quantitative variables, with PCa. The genes responsive to EP and PB exposures were identified using the Comparative Toxicogenomic Database (CTD). DAVID.6.8, GO, and KEGG enrichment analyses were used to delineate their roles in prostate carcinogenesis. The plug-in CytoHubba and MCODE completed identification of the hub genes in Cytoscape software for their roles in the PCa prognosis. It was then validated by using the UALCAN database by evaluating the expression levels and predictive values of the identified hub genes in prostate cancer prognosis using TCGA data. We demonstrate a significant association of higher levels of EPs and PBs in the urine samples, categorical and numerical confounders, with self-reported PCa cases. The higher expression levels of the hub genes (BUB1B, TOP2A, UBE2C, RRM2, and CENPF) in the aggressive stages (Gleason score > 8) of PCa tissues indicate their potential role(s) in the carcinogenic pathways. Our results present an innovative approach to extrapolate and validate hub genes responsive to the EPs and PBs, which may contribute to the severity of the disease prognosis, especially in the older population of US men.

Biomarkers for Breast Adenocarcinoma Using In Silico Approaches

This work elucidates the idea of finding probable critical genes linked to breast adenocarcinoma. In this study, the GEO database gene expression profile data set (GSE70951) was retrieved to look for genes that were expressed variably across breast adenocarcinoma samples and healthy tissue samples. The genes were confirmed to be part of the PPI network for breast cancer pathogenesis and prognosis. In Cytoscape, the CytoHubba module was used to discover the hub genes. For correlation analysis, the predictive biomarker of these hub genes, as well as GEPIA, was used. A total of 155 (85 upregulated genes and 70 downregulated genes) were identified. By integrating the PPI and CytoHubba data, the major key/hub genes were selected from the results. The KM plotter is employed to find the prognosis of those major pivot genes, and the outcome shows worse prognosis in breast adenocarcinoma patients. Further experimental validation will show the predicted expression levels of those hub genes. The overall result of our study gives the consequences for the identification of a critical gene to ease the molecular targeting therapy for breast adenocarcinoma. It could be used as a prognostic biomarker and could lead to therapy options for breast adenocarcinoma.