Identification of hub genes and potential molecular mechanisms related to chemotherapy sensitivity in bladder cancer: A Comprehensive Analysis

doi:10.21203/rs.3.rs-4810972/v1

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Identification of hub genes and potential molecular mechanisms related to chemotherapy sensitivity in bladder cancer: A Comprehensive Analysis

https://doi.org/10.21203/rs.3.rs-4810972/v1

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Chemotherapy resistance drives bladder cancer (BC) recurrence and metastasis, but the biomarkers and mechanisms of chemotherapy sensitivity are not fully known. We identified differentially expressed genes (DEGs) in chemo-resistant and -sensitive BC patients from TCGA and GEO databases. Analyses like GO, KEGG, random survival forest were conducted. We studied the relationships of hub genes with immune cell infiltration, pathways, drug sensitivity, prognosis, regulation, and cellular heterogeneity using multiple methods. A total of 4042 up-regulated and 1355 down-regulated DEGs were included in the analysis. Four hubs, RNF19A, PCGF5, UNC5CL, and CCDC146, were identified and linked to tumor immune infiltration, immune-related genes, sensitivity to chemotherapeutic drugs, and the expression of disease-related genes like APC and EGFR. GSVA and GSEA analysis revealed varying expression levels of these genes impacting cancer-related signaling pathways. A nomogram and calibration curves based on these hub genes showed excellent prognosis predictive performance. We identified key binding motifs and transcription factors for hub genes using RcisTarget. Our mRNA-miRNA regulatory network and single cell analysis revealed cellular heterogeneity in hub gene expression. Therefore, up-regulation of RNF19A, PCGF5, UNC5CL, and CCDC146 in BC is associated with chemotherapy response and various cellular functions, making them potential predictive biomarkers for chemotherapy sensitivity and prognosis.

Biological sciences/Cancer

Biological sciences/Cancer/Cancer therapy

Bladder cancer (BC)

Chemoresistance

TCGA

Bioinformatics analysis

Bladder cancer (BC) is the primary cause of mortality among genitourinary malignancies, resulting in an estimated nearly 170,000 deaths globally each year.¹ Non-muscle-invasive BC (NMIBC) comprises approximately 75% of newly diagnosed cases, while muscle-invasive BC (MIBC) or metastatic BC make up the remaining 25%, both with poor prognosis. Despite advancements in surgical, radiotherapeutic, chemotherapeutic, immunotherapeutic, and targeted therapeutic modalities for BC, enhancing prognosis remains a significant clinical hurdle.^2,3 Hence, it is imperative to investigate reliable and adequate biomarkers that can be utilized in clinical settings to forecast prognosis.

Systemic therapy, in combination with local therapy, is essential in decreasing recurrence rates. In the case of non-muscle-invasive bladder cancer patients, intravesical chemotherapy may be administered post-transurethral resection of bladder tumor (TURBT) to target and eliminate free-floating tumor cells, thereby preventing urothelial seeding.⁴ A meta-analysis of randomized trials revealed a 39% decrease in the likelihood of tumor recurrence (from 48–37%) with intravesical chemotherapy over a median follow-up period of 3.4 years.⁵ Treatment for individuals with muscle-invasive bladder cancer typically involves neoadjuvant therapy followed by radical cystectomy, pelvic lymph node dissection, and urinary diversion, or alternatively, a bladder-sparing approach.⁶ Neoadjuvant chemotherapy prior to radical cystectomy was demonstrated to improve the survival of Muscle-invasive BC.⁷ Metastatic bladder cancer has a poor prognosis. For decades, the mainstay of treatment has been cisplatin-based cytotoxic chemotherapy. So far, various efforts have been tried to search sensitive, specific and accurate biomarkers to predict the response of systemic therapy. However, none has been applied successfully in clinic except the traditional but effective prognosis prediction methods of tumor-node-metastasis (TNM) staging system.

Recent advancements in the field have led to the development of additional treatment modalities, including immunotherapy, targeted therapy, and antibody-drug conjugates. The combination of immunotherapy and chemotherapy has shown potential synergistic effects. Research indicates that systemic chemotherapy may enhance anti-cancer immune responses by either stimulating immune effectors or inhibiting immunosuppressive factors, thereby improving immune recognition and T-cell responsiveness.⁸ The phase III CheckMate-901 trial demonstrated that the combination therapy of nivolumab with gemcitabine-cisplatin yielded significantly improved outcomes for patients with previously untreated advanced urothelial carcinoma than gemcitabine-cisplatin alone.⁹ The underlying factors contributing to the improved efficacy of chemotherapy combined with immunotherapy in bladder cancer are not yet fully understood.

To sum up, the optimal candidates for chemotherapy treatment in bladder cancer patients have not been definitively identified. The molecular mechanisms responsible for chemotherapy resistance in bladder cancer have yet to be elucidated. Therefore, our study aims to investigate the differentially expressed genes (DEGs) between chemotherapy non-responders and responders, examine the association between hub genes and tumor immune infiltration, and explore potential mechanisms in order to predict the sensitivity of chemotherapy and enhance the prognosis of bladder cancer patients undergoing chemotherapy,

Data download

The Series Matrix File of GSE77883 was downloaded from the NCBI GEO public database¹⁰, including 3 control group and 3 chemo-resistant group. The limma package¹¹ was used for variance analysis, and the variance analysis filter condition was p < 0.05 & |logFC| > 1. The GSE129845 single-cell data file, with a total of 3 samples, was obtained from the NCBI GEO public database, for this scRNA analysis.

The raw message RNA (mRNA) expression dataset of bladder cancer analyzed in this study was obtained from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) database, including 19 normal group and 412 tumor tissues.

Identification of hub genes by random survival forest analysis

Feature selection was done using the random ForestSRC package. Prognostic-related genes were ranked using a random survival forest algorithm (nrep = 1000, which indicates that Monte Carlo simulation cycles should be 1000). Final marker genes were those with relative importance greater than 0.6.

Analysis of GO and KEGG functions

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to assess relevance of hub genes using the R package "ClusterProfiler". A significant pathway was defined as one that had both a p-value and q-value less than 0.05.

Analysis of immune cell infiltration on hub genes

The CIBERSORT method was used to deconvolute the expression matrix of immune cell subtypes based on support vector regression. The dataset includes 547 biomarkers distinguishing 22 human immune cell phenotypes. Bladder cancer patient data was analyzed using CIBERSORT, correlating gene expression and immune cell content through Spearman correlation analysis.

Gene set variation analysis (GSVA)

The GSVA algorithm was employed in this study to systematically score the entire gene set of interest, thereby assessing gene-level changes and evaluating the biological function of the samples.

Gene set enrichment analysis (GSEA)

Differential expression of KEGG signaling pathways between groups with high and low gene expression levels was determined through Gene Set Enrichment Analysis (GSEA), followed by analysis of the molecular mechanisms of key genes. Phenotype replacements were set at 1000 for the analysis.

Drug sensitivity analysis

To predict the chemosensitivity of each tumor sample, we used the R package "pRRophetic" based on the largest pharmacogenomics database (GDSC). Using the GDSC training set, 10 cross-validations were performed to evaluate the accuracy of estimating half maximal inhibitory concentrations (IC50) for each specific chemotherapeutic drug. A default value was selected for all parameters, including 'combat' for removing batch effects.

Nomogram model construction

Through the utilization of regression analysis, nomograms are developed by incorporating the expression of pivotal genes and clinical symptoms. These nomograms employ a line segment with a scale to depict the interplay between variables within the predictive model. By constructing a multifactor regression model, the contribution of each influencing factor to the outcome variable is assessed based on the magnitude of the regression coefficient. Subsequently, a score is assigned to each value level of the influencing factors, and these scores are aggregated to calculate the total score for predicting the outcome.

Regulatory network analysis of hub genes

This study used the R package "RcisTarget" to predict transcription factors based on motifs. The calculations depend on motif enrichment scores and additional annotation files were generated through motif and gene sequence analysis. The first step in evaluating motif overexpression in a gene set involved calculating the area under the curve (AUC) for each motif-motif-set combination. The NES of each motif was determined by analyzing the AUC distribution of all motifs in the gene set using the rcitarget.hg19.motifdb.cisbpont.500bp database.

miRNA analysis

MicroRNAs (miRNAs) are small non-coding RNAs known to modulate gene expression by facilitating mRNA degradation or inhibiting mRNA translation. Subsequently, an investigation was conducted to ascertain whether certain miRNAs associated with hub genes regulate the transcription or degradation of specific target genes. The miRNAs linked to hub genes were identified through the mircode database, and the resulting miRNA-gene network was visually represented. ¹²

Single cell analysis

The data underwent initial processing utilizing the Seurat package, followed by tSNE method analysis to elucidate spatial relationships among cluster pairs. Subsequently, cluster annotation was conducted using the celldex package, with emphasis on identifying key cells pivotal to development. Marker genes for each cell subtype were then extracted from the single-cell expression profile using the logfc.threshold option of FindAllMarkers, with the parameter set to 1.¹³

Statistical analysis

All statistical analyses were carried out using the R language. All statistical tests were two-sided, and p < 0.05 was considered statistically significant.

Identification of DEG sets associated with chemo-resistant BC patients compared to chemo-sensitive BC patients

DEGs analysis was conducted on the GEO dataset GSE77883 using limma, indicating that 5397 DEGs exhibited differential expression between chemotherapy-resistant and chemotherapy-sensitive bladder cancer patients based on a significance threshold of P < 0.05. Among that, 4042 genes were up regulated and 1355 genes were down-regulated (Fig. 1).

Identification of hub genes and enrichment analysis

A random survival forest algorithm was utilized to assess the importance of prognostic-related genes (Fig. 2A). Six genes met the screening threshold criteria, namely RNF19A, PCGF5, UNC5CL, CCDC146, APBA3, and NDUF A3, as depicted in Fig. 2B. Among these genes, only four hub genes, RNF19A, PCGF5, UNC5CL, and CCDC146, showed statistical significance in Kaplan-Meier survival analysis (Fig. 2C-F). GO analysis showed enrichment in netrin-activated signaling pathway, postsynaptic modulation of chemical synaptic transmission, and regulation of gene expression by genomic imprinting, among other pathways (Fig. 3A). KEGG analysis indicated enrichment in the Signaling pathways regulating pluripotency of stem cells pathway (Fig. 3B).

Exploration of the clinical predictive value of four hub genes based on multi‑omics studies

The tumor microenvironment, including various components like fibroblasts, immune cells, and growth factors, is essential in determining the prognosis and treatment outcomes of tumors. Researchers studied how hub genes may affect bladder cancer progression by analyzing their relationship with immune infiltration. Figure 4A shows the distribution of immune infiltrating cells in each patient, while Fig. 4B illustrates the Pearson correlation between immune cells. Figure 4C highlights that T cells follicular helper, Macrophages M0, and Macrophages M1 were significantly higher in bladder cancer patients compared to other groups. RNF19A is positively correlated with T cells CD4 naive, Dendritic cells activated, and negatively correlated with T cells CD4 memory activated, Macrophages M0, among others. PCGF5 displayed significant positive correlations with T cells CD4 memory activated, T cells CD4 memory resting, among others, and significant negative correlations with T cells regulatory (Tregs), Macrophages M0, among others. UNC5CL demonstrated significant positive correlations with Tregs, B cells memory, among others, and significant negative correlations with T cells CD4 memory activated, Macrophages M2, among others. Conversely, CCDC146 showed significant positive correlations with Monocytes, Tregs, and significant negative correlations with Macrophages M0, T cells CD4 memory activated, among others. Bubble map for the correlations between four hub genes and tumor immune infiltration cells is depicted in Fig. 4D. These analyses support the notion that hub genes are intricately linked to the composition of immune infiltrating cells and are pivotal in shaping the immune microenvironment.

Discussion on specific signaling mechanisms related to hub genes

Subsequent analysis focused on the specific signaling pathways associated with the four hub genes, examining the impact of candidate genes on disease progression-related signaling pathways. The Gene Set Variation Analysis (GSVA) results indicated that RNF19A was involved in the enrichment of signaling pathways such as ESTROGEN RESPONSE EARLY, ANDROGEN RESPONSE, and APICAL SURFACE (Fig. 5A); PCGF5 was associated with the enrichment of signaling pathways including COMPLEMENT, INTERFERON GAMMA RESPONSE, and UV RESPONSE DN (Fig. 5B); and UNC5CL was linked to the enrichment of XENOBIOTIC signaling pathways such as METABOLISM, PEROXISOME, and FATTY ACID METABOLISM (Fig. 5C). CCDC146 was found to enrich several signaling pathways, including ESTROGEN RESPONSE EARLY, APICAL SURFACE, and BILE ACID METABOLISM (Fig. 5D). Subsequent GSEA analysis revealed that high expression of RNF19A enriched pathways such as ALPHA LINOLENIC ACID METABOLISM, ETHER LIPID METABOLISM, GLYCEROPHOSPHOLIPID METABOLISM, and LINOLEIC ACID METABOLISM (Fig. 6A). Additionally, high expression of PCGF5 was associated with enrichment of pathways such as ADHERENS JUNCTION, T CELL RECEPTOR SIGNALING PATHWAY, and VIRAL MYOCARDITIS (Fig. 6B). Elevated levels of UNC5CL have been shown to enhance glycerophospholipid metabolism, glycosylphosphatidylinositol GPI anchor biosynthesis, peroxisome function, and valine leucine and isoleucine degradation signaling pathways (Fig. 6C). Similarly, heightened expression of CCDC146 can enrich glycerophospholipid metabolism, linoleic acid metabolism, mTOR signaling pathway, and phosphatidylinositol signaling system signaling pathways (Fig. 6D).

Sensitivity to Chemotherapies for hub genes in BC

We conducted an analysis of chemotherapeutic drug sensitivity across varying levels of hub gene expression using the GDSC database and the R package "pRRophetic." The findings indicate a significant correlation between the expression levels of RNF19A, PCGF5, UNC5CL, and CCDC146 with the sensitivity of various drugs including AKT.inhibitor.VIII, ATRA, Bexarotene, ABT.263, Cyclopamine, and Methotrexate as illustrated in Fig. 7A, 7B, 7C,and 7D.

Construction of nomogram and development of calibration curves to predict the outcome of patients with BC

A nomogram was developed utilizing the TCGA BC dataset, incorporating the expression levels of RNF19A, PCGF5, UNC5CL, and CCDC146, as well as clinical characteristics such as age, gender, stage, tumor (T), lymph node (N), and metastasis (M) stage. Logistic regression analysis revealed varying degrees of contribution from the clinical parameters and four hub genes (RNF19A, PCGF5, UNC5CL, and CCDC146) in the scoring process. Higher total points on the nomogram corresponded to lower 3-year and 5-year survival probabilities, as depicted in Fig. 8A. Simultaneously, the calibration curves for the probabilities of 3-year and 5-year overall survival (OS) indicated that the nomogram-predicted OS closely aligned with the observed OS, as depicted in Fig. 8B.

Transcriptional regulation analysis

In this investigation, the predictive capabilities of four hub genes, namely RNF19A, PCGF5, UNC5CL, and CCDC146, were assessed in relation to transcription factors (TFs). The analysis revealed that these genes were under the regulation of multiple TFs, prompting a motif enrichment analysis to be conducted for these TFs (Fig. 9A). The findings demonstrated that the motif labeled as cisbp__M0322 exhibited the highest normalized enrichment score (NES) (Fig. 9B), followed by cisbp_M1386 and cisbp_M0753 (Fig. 9C and 9D). Additionally, reverse prediction analysis was conducted on the hub genes using the mircode database. A total of 79 miRNAs and 159 mRNA-miRNA relationship pairs were identified and visualized using Cytoscape (Fig. 9E).

Study of the relationship between four hub genes and disease‑related genes

Disease genes associated with the tumorigenesis of bladder cancer were obtained from the GeneCards database (https://www.genecards.org/). The results indicated significant differences in the expression of multiple disease-related genes between the control group and the BC group, including APC, ATM, BRCA1, and BRCA2 (Fig. 10A). Pearson correlation analysis revealed that hub genes RNF19A, PCGF5, UNC5CL, and CCDC146 were significantly correlated with the expression of various disease-related genes. The relationship between the expression levels of PCGF5 and APC (r = 0.44) as well as UNC5CL and EGFR (r=-0.22) was illustrated in Fig. 10B.

Overview of hub gene expression in single cells

Single-cell analysis was performed utilizing the Seurat package, cells were clustered using the tSNE algorithm, HumanPrimaryCellAtlasData was utilized as annotation data, and clusters were annotated using the R package SingleR. All cells were categorized into six groups: Epithelial cells, Fibroblasts, Smooth muscle cells, Endothelial cells, Monocytes, and T cells (Fig. 11A). The expression levels of RNF19A, PCGF5, UNC5CL, and CCDC146 in these cell types are depicted in Fig. 11B-C. Subsequently, the co-expression of these four key genes and the tumor progression gene TP53 within the six cell marker genes was visualized (Fig. 12).

A thorough understanding of the mechanisms underlying chemotherapy sensitivity is crucial for enhancing outcomes in bladder cancer. Nevertheless, there remains a gap in comprehensive research on hub genes related to chemotherapy resistance. Therefore, we systematically investigated the biomarkers and potential mechanisms of chemotherapy resistance in bladder cancer patients through bioinformatics analyses.

In this study, DEGs between chemo-resistant and chemo-sensitive bladder cancer patients were identified using the limma package in the Gene Expression Omnibus (GEO) dataset GSE77883. A total of 4042 up-regulated and 1355 down-regulated genes were identified. Random survival forest analysis of the DEGs from The Cancer Genome Atlas Bladder Cancer (TCGA-BC) cohort revealed six genes that met the threshold, namely RNF19A, PCGF5, UNC5CL, CCDC146, APBA3, and NDUF A3. RNF19A, PCGF5, UNC5CL, and CCDC146 were found to be significantly associated with the survival of bladder cancer patients and identified as hub genes in the study. GSVA and GSEA analysis showed that RNF19A, PCGF5, UNC5CL, and CCDC146 have varying expression levels that impact cancer-related signaling pathways. These genes were also linked to tumor immune infiltration, immune-related genes, sensitivity to chemotherapeutic drugs, and the expression of disease-related genes such as APC and EGFR. A nomogram was created to predict bladder cancer outcomes based on these genes and clinical characteristics. The nomogram accurately predicted overall survival in bladder cancer patients. Additionally, significant binding motifs and transcription factors for hub genes were identified using RcisTarget. We developed an mRNA-miRNA regulatory network and utilized single cell analysis to investigate the cellular heterogeneity of hub gene expression.

The gene RNF19A, also referred to as RNF144B, functions as an E3 ubiquitin-protein ligase that plays a role in regulating multiple cellular processes such as protein degradation, DNA damage response, and cell cycle regulation.^14,15 Numerous studies have indicated that aberrant expression of RNF19A is linked to tumorigenesis and treatment outcomes in various cancer types.^16–18 In recent years, scholarly attention has been directed towards elucidating the role of RNF19A in conferring resistance to chemotherapy. Research findings have demonstrated a correlation between elevated RNF19A levels and resistance to a range of chemotherapeutic agents, including taxanes, platinum-based compounds, and tyrosine kinase inhibitors.¹⁹ RNF19A facilitates chemoresistance through various mechanisms, such as promoting drug efflux, suppressing apoptosis, and activating DNA repair pathways.¹⁸ Given its significant impact on tumor prognosis and chemoresistance, RNF19A is increasingly recognized as a promising target for therapeutic intervention. To the best of our knowledge, there has been no investigation into the role of RNF19A in the development of chemo-resistance in bladder cancer.

The gene PCGF5, also referred to as RNF110, is a member of the polycomb group (PcG) protein family, which plays a role in epigenetic regulation and transcriptional repression.²⁰ Previous research has revealed PCGF5 promoted NKX6-3 activation in B-cell development and lymphomas.²¹ Previous research has also reported that PCGF5 repressed transcription of NKL homeobox gene MSX1 in T-ALL cells.²² The gene UNC5CL, alternatively referred to as PPP1R74, is a member of the UNC5 family of netrin receptors and plays a role in axonal guidance and neuronal development.²³ A recent study showed that UNC5CL is recognized as a prognostic indicator and gemcitabine-resistance genes in bladder cancer.²² Three genome-wide association studies in Chinese populations identified variants at UNC5CL as new OSCC susceptibility loci.²⁴ Accordingly, the gene CCDC146, also known as KIAA0427, is a member of the coiled-coil domain-containing protein family and is involved in a variety of cellular processes, such as cellular adhesion and signal transduction.²⁵ CCDC146 was a potential therapeutic strategy for lymph node metastasis of breast cancer.²⁶ CCDC146 has also showed good diagnostic potential and clinical relevance as molecular markers for clinical diagnosis, treatment and prognosis of thyroid cancer.²⁷ Nonetheless, the present study was the first to recognize these hub genes as a prognostic indicator in bladder cancer.

Evidence has demonstrated that the effectiveness of chemotherapy is not solely attributed to its direct cytotoxic effects, but also relies on the concurrent stimulation of anti-tumor immune responses. Furthermore, previous study revealed significant variations in the responses to anticancer drugs among closely related cells within the same tumor.²⁸ The hub genes identified in our study were found to have strong correlations with immune infiltrating cells and immune-related genes in bladder cancer. Single cell analysis demonstrated differential expression levels of the hub genes across various cell types. Additionally, coexpression was observed between the hub genes and TP53, a commonly mutated gene. The TP53 mutation, along with its related pathways, may act as driver mutations in bladder cancer, thereby promoting disease progression and influencing cancer prognosis and therapeutic strategy.²⁹BMC These findings suggest that immunity plays a significant role in chemoresistance and may elucidate the underlying mechanisms of chemoresistance and the potential efficacy of combining chemotherapy with immunotherapy in bladder cancer.

Nevertheless, it is important to acknowledge the limitations and deficiencies present in our current research efforts. Initially, the sample sizes obtained from the TCGA and GEO databases were limited in scope. Additionally, the outcomes lacked experimental verifications both in vivo and in vitro. Notwithstanding these limitations, the preliminary investigation still yielded significant and constructive insights. Subsequent research endeavors will aim to substantiate the involvement of the key genes RNF19A, PCGF5, UNC5CL, and CCDC146 in the development of chemoresistance in bladder cancer through a series of comprehensive experiments. The study will examine the impact of the overexpression and knockdown of RNF19A, PCGF5, UNC5CL, and CCDC146 on various cellular processes such as cell cycle distribution, proliferation, colony-forming ability, apoptosis, and invasiveness following chemotherapy in human bladder cancer cell lines and in vivo models. Additionally, the investigation will encompass an analysis of the relevant signaling pathways and the regulatory network involving the transcription factor and disease‑related genes.

Taken together, DEGs and four hub genes were identified in chemo-resistant and chemo-sensitive bladder cancer patients. The hub genes were linked to tumor immune infiltration, immune-related genes, chemotherapeutic sensitivity, and disease-related gene expression. A prognostic nomogram including clinicopathological features and hub genes predicted overall survival in bladder cancer patients. Cellular heterogeneity of hub gene expression was also studied.

The initial study identified hub genes that could be predictive biomarkers for chemotherapy response and prognosis in bladder cancer. Future articles will delve deeper into the effects of these hub genes on bladder cancer cells and their potential signaling pathways related to chemotherapy sensitivity.

Ethics statement

Ethical approval was waived by the Ethics Committee of the First Affiliated Hospital, Sun Yat‐Sen University because preexisting data with no personal identifiers were used.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflict of interest statement

No potential conflicts of interest are disclosed.

Acknowledgment

This study was supported by the Youth Funds of the Basic and Applied Basi Research Foundation of Guangdong Province, China (No. 2020A1515110089).

Author contributions

Cui Chen designed and supervised the study. Cui Chen and Peng Sun collected the data and drafted the manuscript. Shao‐Yong Chen performed the statistical analysis. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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No competing interests reported.

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Identification of hub genes and potential molecular mechanisms related to chemotherapy sensitivity in bladder cancer: A Comprehensive Analysis

Status:

Version 1

Abstract

Figures

Introduction

Methods

Data download

Identification of hub genes by random survival forest analysis

Analysis of GO and KEGG functions

Analysis of immune cell infiltration on hub genes

Gene set variation analysis (GSVA)

Gene set enrichment analysis (GSEA)

Drug sensitivity analysis

Nomogram model construction

Regulatory network analysis of hub genes

miRNA analysis

Single cell analysis

Statistical analysis

Results

Identification of DEG sets associated with chemo-resistant BC patients compared to chemo-sensitive BC patients

Identification of hub genes and enrichment analysis

Exploration of the clinical predictive value of four hub genes based on multi‑omics studies

Discussion on specific signaling mechanisms related to hub genes

Sensitivity to Chemotherapies for hub genes in BC

Transcriptional regulation analysis

Study of the relationship between four hub genes and disease‑related genes

Overview of hub gene expression in single cells

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1