Gene expression profiling of breast cancer in Lebanese women. Sci Rep. 2016;6:36639. [PMID: 27857161 PMCID: PMC5114572 DOI: 10.1038/srep36639]

Construction and analysis of a reliable five-gene prognostic signature for colon adenocarcinoma associated with the wild-type allelic state of the COL6A6 gene

Background

Tumors emerge by acquiring a number of mutations over time. The first mutation provides a selective growth advantage compared to adjacent epithelial cells, allowing the cell to create a clone that can outgrow the cells that surround it. Subsequent mutations determine the risk of the tumor progressing to metastatic cancer. Some secondary mutations may inhibit the aggressiveness of the tumor while still increasing the survival of the clone. Meaningful mutations in genes may provide a strong molecular foundation for developing novel therapeutic strategies for cancer.

Methods

The somatic mutation and prognosis in colon adenocarcinoma (COAD) were analyzed. The copy number variation (CNV) and differentially expressed genes (DEGs) between the collagen type VI alpha 6 chain (COL6A6) mutation (COL6A6-MUT) and the COL6A6 wild-type (COL6A6-WT) subgroups were evaluated. The independent prognostic signatures based on COL6A6-allelic state were determined to construct a Cox model. The biological characteristics and the immune microenvironment between the two risk groups were compared.

Results

COL6A6 was found to be highly mutated in COAD at a frequency of 9%. Patients with COL6A6-MUT had a good overall survival (OS) compared to those with COL6A6-WT, who had a different CNV pattern. Significant differences in gene expression were established for 593 genes between the COL6A6-MUT and COL6A6-WT samples. Among them, MUC16, ASNSP1, PRR18, PEG10, and RPL26P8 were determined to be independent prognostic factors. The internally validated prognostic risk model, constructed using these five genes, demonstrated its value by revealing a significant difference in patient prognosis between the high-risk and low-risk groups. Specifically, patients in the high-risk group exhibited a considerably worse prognosis than did those in the low-risk group. The high-risk group had a significantly higher proportion of patients over 60 years of age and patients in stage III. Moreover, the tumor immune dysfunction and exclusion (TIDE) score and the expression of human leukocyte antigen (HLA) family genes were all higher in the high-risk group than that in the low-risk group.

Conclusions

The allelic state of COL6A6 and the five associated DEGs were identified as novel biomarkers for the diagnosis and prognosis of COAD and may be therapeutic targets in COAD.

Unveiling Collagen's Role in Breast Cancer: Insights into Expression Patterns, Functions and Clinical Implications

Collagen, the predominant protein constituent of the mammalian extracellular matrix (ECM), comprises a diverse family of 28 members (I-XXVIII). Beyond its structural significance, collagen is implicated in various diseases or cancers, notably breast cancer, where it influences crucial cellular processes including proliferation, metastasis, apoptosis, and drug resistance, intricately shaping cancer progression and prognosis. In breast cancer, distinct collagens exhibit differential expression profiles, with some showing heightened or diminished levels in cancerous tissues or cells compared to normal counterparts, suggesting specific and pivotal biological functions. In this review, we meticulously analyze the expression of individual collagen members in breast cancer, utilizing Transcripts Per Million (TPM) data sourced from the GEPIA2 database. Through this analysis, we identify collagens that deviate from normal expression patterns in breast cancer, providing a comprehensive overview of their expression dynamics, functional roles, and underlying mechanisms. Our findings shed light on recent advancements in understanding the intricate interplay between these aberrantly expressed collagens and breast cancer. This exploration aims to offer valuable insights for the identification of potential biomarkers and therapeutic targets, thereby advancing the prospects of more effective interventions in breast cancer treatment.

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.

Experimental validation and pan-cancer analysis identified COL10A1 as a novel oncogene and potential therapeutic target in prostate cancer

BACKGROUND

Type X collagen (COL10) is a homologous trimeric non-fibrillar collagen found in the extracellular matrix of human tissues, and it exhibits a distinctive white appearance. Type X collagen α1 chain (COL10A1) is a specific cleaved fragment of type X collagen. However, the expression, prognostic significance, clinicopathological attributes and immune-related associations of COL10A1 in prostate cancer as well as in pan-cancer contexts remain poorly understood.

METHODS

Using bioinformatic analysis of data from the most recent databases (TCGA, GTEx and GEO databases), we have extensively elucidated the role played by COL10A1 in terms of its expression patterns, prognostic implications, and immune efficacy across a pan-cancer spectrum. Subsequently, the biological functions of COL10A1 in prostate cancer were elucidated by experimental validation.

RESULTS

Our findings have confirmed that COL10A1 was highly expressed in most cancers and was associated with poorer prognosis in cancer patients. Immune correlation analysis of COL10A1 in various cancers showed its significant correlation with Tumor mutational burden (TMB), microsatellite instability (MSI) and immune cell infiltration. In addition, knockdown of COL10A1 in prostate cancer resulted in a substantial reduction in the proliferation, migration, and invasive potential of prostate cancer cells.

CONCLUSION

Our pan-cancer analysis of COL10A1 gene provided novel insights into its pivotal role in cancer initiation, progression, and therapeutic implications, underscoring its potential significance in prognosis and immunotherapeutic interventions for cancer, particularly prostate cancer.

Epigenetic control and genomic imprinting dynamics of the Dlk1-Dio3 domain

Genomic imprinting is an epigenetic process whereby genes are monoallelically expressed in a parent-of-origin-specific manner. Imprinted genes are frequently found clustered in the genome, likely illustrating their need for both shared regulatory control and functional inter-dependence. The Dlk1-Dio3 domain is one of the largest imprinted clusters. Genes in this region are involved in development, behavior, and postnatal metabolism: failure to correctly regulate the domain leads to Kagami-Ogata or Temple syndromes in humans. The region contains many of the hallmarks of other imprinted domains, such as long non-coding RNAs and parental origin-specific CTCF binding. Recent studies have shown that the Dlk1-Dio3 domain is exquisitely regulated via a bipartite imprinting control region (ICR) which functions differently on the two parental chromosomes to establish monoallelic expression. Furthermore, the Dlk1 gene displays a selective absence of imprinting in the neurogenic niche, illustrating the need for precise dosage modulation of this domain in different tissues. Here, we discuss the following: how differential epigenetic marks laid down in the gametes cause a cascade of events that leads to imprinting in the region, how this mechanism is selectively switched off in the neurogenic niche, and why studying this imprinted region has added a layer of sophistication to how we think about the hierarchical epigenetic control of genome function.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Determining the Factors Predicting the Response to Anti-HER2 Therapy in HER2-Positive Breast Cancer Patients

PURPOSE

We aimed to identify the differently expressed genes or related pathways associated with good responses to anti-HER2 therapy and to suggest a model for predicting drug response in neoadjuvant systemic therapy with trastuzumab in HER2-positive breast cancer patients.

METHODS

This study was retrospectively analyzed from consecutively collected patient data. We recruited 64 women with breast cancer and categorized them into 3 groups: complete response (CR), partial response (PR), and drug resistance (DR). The final number of patients in the study was 20. RNA from 20 core needle biopsy paraffin-embedded tissues and 4 cultured cell lines (SKBR3 and BT474 breast cancer parent cells and cultured resistant cells) was extracted, reverse transcribed, and subjected to GeneChip array analysis. The obtained data were analyzed using Gene Ontology, Kyoto Gene and Genome Encyclopedia, Database for Annotation, Visualization and Integrated Discovery.

RESULTS

In total, 6,656 genes differentially expressed between trastuzumab-susceptible and trastuzumab-resistant cell lines were identified. Among these, 3,224 were upregulated and 3,432 were downregulated. Expression changes in 34 genes in several pathways were found to be related to the response to trastuzumab-containing treatment in HER2-type breast cancer, interfering with adhesion to other cells or tissues (focal adhesion) and regulating extracellular matrix interactions and phagosome action. Thus, decreased tumor invasiveness and enhanced drug effects might be the mechanisms explaining the better drug response in the CR group.

CONCLUSIONS

This multigene assay-based study provides insights into breast cancer signaling and possible predictions of therapeutic response to targeted therapies such as trastuzumab.

High expression COL10A1 promotes breast cancer progression and predicts poor prognosis

Background

As a common malignant disease in females, breast cancer (BCa) causes increasing numbers of cancer-related death. Collagen X alpha 1 chain (COL10A1) plays a critical role in the oncogenesis and progression of malignant tumors. However, a systematic analysis of COL10A1 in BCa has not been conducted.

Methods

The COL10A1 expression level and prognostic value in BCa were defined through the Cancer Genome Atlas (TCGA) as well as the Kaplan-Meier plotter data respectively. The expression pattern of COL10A1 was subsequently confirmed on tissue microarray (TMA) by immunochemistry (IHC) staining. Moreover, cellular functional assays which aimed to evaluate cell proliferation, migration, invasion, and apoptosis, were conducted to investigate the oncogenic activity of COL10A1 in BCa. Then, Tumor Immune Estimation Resource (TIMER) was adopted to determine the association between COL10A1 expression and immune cell infiltration.

Results

Bioinformatics analysis revealed that COL10A1 was significantly overexpressed and had notable prognostic value, especially for distant metastasis-free survival (DMFS) in BCa. Moreover, IHC analysis of 140 BCa tissues on TMA chips exhibited the overexpression of COL10A1 was correlated to advanced clinical stage, poor overall survival (OS), and worse recurrence-free survival (RFS). Besides, knockdown of COL10A1 remarkably suppressed cell proliferation, migration, and invasion in BCa cells, and notably promoted cell apoptosis as well. Furthermore, COL10A1 was positively associated with immune cell infiltration including B cell, CD8⁺ T cell, CD4⁺ T cell, macrophage, neutrophil, and dendritic cell.

Conclusion

The results revealed that COL10A1 is a novel oncogene and could serve as a potential prognostic biomarker in BCa. Besides, the downregulation of COL10A1 could inhibit BCa progression, which could be a potential target for BCa therapy.

Altered Expression of Survivin Variants S-2B and S-WT in Breast Cancer Is Related to Adipokine Expression

Breast cancer (BCa) is one of the leading causes of death in women with these types of malignancies. Early detection is pivotal to improve prognosis and reduce mortality. Several proteins and genes have been proposed as biomarkers for cancer; however, further studies are required before a molecule is accepted as a definitive biomarker. This study was aimed at investigating the expression of survivin variants S-WT, S-2B, and S-ΔEx3, as well as adipokines LEP and ADIPOQ in breast cancer. Breast samples were obtained from patients with (n = 27) and without (n = 20) BCa, and relative gene expression was assessed by RT-qPCR. S-WT and S-2B showed a significant increase in BCa samples (p = 0.005 and p = 0.001, respectively) and in high-aggressiveness BCa (p = 0.026 and p = 0.037, respectively). Despite S-ΔEx3 expression remained globally unchanged, when dividing BCa samples according to the stage, this gene showed a significant tendency to increase towards more advanced stages, and the exact opposite effect was observed for LEP. Furthermore, LEP expression showed a negative correlation with S-2B (p = 0.005) and S-WT (p = 0.011), and in the same manner, ADIPOQ was negatively related with these two survivin variants (p = 0.001 and p = 0.005, respectively). Interestingly, S-ΔEx3 expression appears unaffected by LEP and ADIPOQ expressions. Our results highlight the importance of investigating specific variants of a given gene, as sequence variation may grant different correlation with other important structures and diseases.

Identification of key tumor stroma-associated transcriptional signatures correlated with survival prognosis and tumor progression in breast cancer

BACKGROUND

The aberrant expression of stromal gene signatures in breast cancer has been widely studied. However, the association of stromal gene signatures with tumor immunity, progression, and clinical outcomes remains lacking.

METHODS

Based on eight breast tumor stroma (BTS) transcriptomics datasets, we identified differentially expressed genes (DEGs) between BTS and normal breast stroma. Based on the DEGs, we identified dysregulated pathways and prognostic hub genes, hub oncogenes, hub protein kinases, and other key marker genes associated with breast cancer. Moreover, we compared the enrichment levels of stromal and immune signatures between breast cancer patients with bad and good clinical outcomes. We also investigated the association between tumor stroma-related genes and breast cancer progression.

RESULTS

The DEGs included 782 upregulated and 276 downregulated genes in BTS versus normal breast stroma. The pathways significantly associated with the DEGs included cytokine-cytokine receptor interaction, chemokine signaling, T cell receptor signaling, cell adhesion molecules, focal adhesion, and extracellular matrix-receptor interaction. Protein-protein interaction network analysis identified the stromal hub genes with prognostic value in breast cancer, including two oncogenes (COL1A1 and IL21R), two protein kinases encoding genes (PRKACA and CSK), and a growth factor encoding gene (PLAU). Moreover, we observed that the patients with bad clinical outcomes were less enriched in stromal and antitumor immune signatures (CD8 + T cells and tumor-infiltrating lymphocytes) but more enriched in tumor cells and immunosuppressive signatures (MDSCs and CD4 + regulatory T cells) compared with the patients with good clinical outcomes. The ratios of CD8 + /CD4 + regulatory T cells were lower in the patients with bad clinical outcomes. Furthermore, we identified the tumor stroma-related genes, including MCM4, SPECC1, IMPA2, and AGO2, which were gradually upregulated through grade I, II, and III breast cancers. In contrast, COL14A1, ESR1, SLIT2, IGF1, CH25H, PRR5L, ABCA6, CEP126, IGDCC4, LHFP, MFAP3, PCSK5, RAB37, RBMS3, SETBP1, and TSPAN11 were gradually downregulated through grade I, II, and III breast cancers. It suggests that the expression of these stromal genes has an association with the progression of breast cancers. These progression-associated genes also displayed an expression association with recurrence-free survival in breast cancer patients.

CONCLUSIONS

This study identified tumor stroma-associated biomarkers correlated with deregulated pathways, tumor immunity, tumor progression, and clinical outcomes in breast cancer. Our findings provide new insights into the pathogenesis of breast cancer.

COL10A1 is a novel factor in the development of choroidal neovascularization

With the dramatic rise in the aging population, researching age-related macular degeneration (AMD), especially the severe form neovascular AMD (nAMD), has become more important than ever. In this study, we found that collagen type X was increased in retina-choroid tissue of mice with laser-induced choroidal neovascularization (CNV) based on immunohistofluorescence. RNA sequencing and bioinformatic analyses were performed to compare the retina-choroid tissue complex of the CNV mouse model to normal controls. Collagen type X alpha 1 chain (Col10a1) was among the most significantly upregulated genes, and the results were validated with an animal model at the mRNA and protein levels by quantitative real-time polymerase chain reaction (qPCR) and western blotting, respectively. COL10A1 was also upregulated in human retinal microvascular endothelial cells (HRMECs), human umbilical vein endothelial cells (HUVECs), RPE19 cells and RF/6A cells under hypoxic conditions. Next, in vitro and in vivo experiments were performed to study the effect of COL10A1 on neovascularization. siRNA knockdown of COL10A1 suppressed the proliferation and tube formation ability of HRMECs under hypoxic conditions. Snail family transcriptional repressor 1 (SNAIL1) and angiopoietin-2 (ANGPT2) were downregulated in COL10A1 knockdown HRMECs under hypoxic conditions and thus were potential downstream genes. Significant decreases in CNV leakage and CNV lesion area, as assessed by fundus fluorescein angiography (FFA) and immunofluorescence of choroidal flat mounts, respectively, were observed in a mouse model intravitreally injected with anti-collagen X monoclonal antibody (mAb) compared to the controls. In conclusion, COL10A1 promotes CNV formation and may represent a new candidate target for the treatment and diagnosis of nAMD and other neovascular diseases.

LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer

The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (p < 0.05). The two genes have their function: (1.) PAMR1 is a tumor suppressor gene, and many studies have proven that overexpression of the gene markedly suppresses cell growth, especially in breast cancer and polycystic ovary syndrome; (2.) SLC24A3 is a sodium–calcium regulator of cells, and high SLC24A3 levels are associated with poor prognosis. In our study, the gene signatures can be used to predict CC and EC prognosis, which could provide novel clinical evidence to serve as a potential biomarker for future diagnosis and treatment.

LINE-1 methylation mediates the inverse association between body mass index and breast cancer risk: A pilot study in the Lebanese population

INTRODUCTION

Lebanon is among the top countries worldwide in combined incidence and mortality of breast cancer, which raises concern about risk factors peculiar to this country. The underlying molecular mechanisms of breast cancer require elucidation, particularly epigenetics, which is recognized as a molecular sensor to environmental exposures.

PURPOSE

We aim to explore whether DNA methylation levels of AHRR (marker of cigarette smoking), SLC1A5 and TXLNA (markers of alcohol consumption), and LINE-1 (a genome-wide repetitive retrotransposon) can act as molecular mediators underlying putative associations between breast cancer risk and pertinent extrinsic (tobacco smoking and alcohol consumption) and intrinsic factors [age and body mass index (BMI)].

METHODS

This is a cross-sectional pilot study which includes breast cancer cases (N = 65) and controls (N = 54). DNA methylation levels were measured using bisulfite pyrosequencing on available peripheral blood samples (N = 119), and Multivariate Imputation by Chained Equations (MICE) was used to impute missing DNA methylation values in remaining samples. Multiple mediation analysis was performed to assess direct and indirect (via DNA methylation) effects of intrinsic and extrinsic factors on breast cancer risk.

RESULTS

In relation to exposure, AHRR hypo-methylation was associated with cigarette but not waterpipe smoking, suggesting potentially different biomarkers of these two forms of tobacco use; SLC1A5 and TXLNA methylation were not associated with alcohol consumption; LINE-1 methylation was inversely associated with BMI (β-value [95% confidence interval (CI)] = -0.04 [-0.07, -0.02]), which remained significant after adjustment for age, smoking and alcohol consumption. In relation to breast cancer, there was no detectable association between AHRR, SLC1A5 or TXLNA methylation and cancer risk, but LINE-1 methylation was significantly higher in breast cancer cases when compared to controls (mean ± SD: 72.00 ± 0.66 versus 70.89 ± 0.73, P = 4.67 × 10-14). This difference remained significant after adjustment for confounders (odds ratio (OR) [95% CI] = 9.75[3.74, 25.39]). Moreover, LINE-1 hypo-methylation mediated 83% of the inverse effect of BMI on breast cancer risk.

CONCLUSION

This pilot study demonstrates that alterations in blood LINE-1 methylation mediate the inverse effect of BMI on breast cancer risk. This warrants large scale studies and stratification based on clinic-pathological types of breast cancer.

DNA methylation association with stage progression of head and neck squamous cell carcinoma

Head and Neck Squamous Cell Carcinoma (HNSCC) is the sixth most common cancer worldwide, which accounts for approximately 6% of all cases and is responsible for an estimated 2% of all cancer deaths. Despite progress in the treatment of squamous cell carcinomas, survival rates remain low. It is a fact that epigenetic modifications have numerous associations with biological processes and complex diseases such as cancer. Hence, a more systematic approach is needed to provide potential screening targets and have an effective therapy method. This study developed a workflow to analyze HM450 methylation arrays with mRNA expression profiles that identified novel signatures of epigenetic regulators for tumor progression. We identified differentially expressed genes and differentially methylated regions and the correlation between associated genes to identify epigenetic modifications underlying regulation roles. We have taken the differentiation direction of expressions into account during the integration of gene expression and DNA methylation modification to detect epigenetic regulators of core genes of tumor-stage progression. Enrichment analysis of selected key genes provides better insight into their functionality. Thus, we have investigated gene copy number alteration and mutations to filter differentially expressed genes, including some members of the fibroblast growth factor family and cyclin-dependent kinase inhibitor family with other potential known regulators. Our analysis has revealed the list of 61 commercial methylation probes positively correlated with 31 differentially expressed genes, which can be associated with HNSC metastasis stages. Most of these genes have already reported potential epigenetic regulators, and their role in cancer progression was studied. We suggest these selected probes of DNA methylation as potential targets of the epigenetic regulators in revealed genes that have displayed significant genetic and epigenetic modification behavior during cancer stage progression and tumor metastasis.

Bioinformatics analysis of prognostic significance of COL10A1 in breast cancer

Background: Collagen type X alpha 1 (COL10A1) is overexpressed in diverse tumors and displays vital roles in tumorigenesis. However, the prognostic value of COL10A1 in breast cancer remains unclear.

Methods: The expression of COL10A1 was analyzed by the Oncomine database and UALCAN cancer database. The relationship between COL10A1 expression level and clinical indicators including prognostic data in breast cancer were analyzed by the Kaplan–Meier Plotter, PrognoScan, and Breast Cancer Gene-Expression Miner (bc-GenExMiner) databases.

Results: COL10A1 was up-regulated in different subtypes of breast cancer. Estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2) status and nodal status were positively correlated with COL10A1 expression. Conversely, age, the Scarff–Bloom–Richardson (SBR) grade, basal-like status, and triple-negative status were negatively related to COL10A1 level in breast cancer samples compared with normal tissues. Patients with increased COL10A1 expression level showed worse overall survival (OS), relapse-free survival (RFS), distant metastasis-free survival (DMFS) and disease-free survival (DFS). COL10A1 was positively correlated with metastatic relapse-free survival. GSEA analysis revealed that enrichment of TGF-β signaling pathway. 15-leucine-rich repeat containing membrane protein (LRRC15) is a correlated gene of COL10A1.

Conclusion: Bioinformatics analysis revealed that COL10A1 might be considered as a predictive biomarker for prognosis of breast cancer. Further experiments and clinical trials are essential to elucidate the value of COL10A1 in breast cancer treatment.

Identification of Seven Cell Cycle-Related Genes with Unfavorable Prognosis and Construction of their TF-miRNA-mRNA regulatory network in Breast Cancer

Breast cancer (BC), with complex tumorigenesis and progression, remains the most common malignancy in women. We aimed to explore some novel and significant genes with unfavorable prognoses and potential pathways involved in BC initiation and progression via bioinformatics methods. BC tissue-specific microarray datasets of GSE42568, GSE45827 and GSE54002, which included a total of 651 BC tissues and 44 normal breast tissues, were obtained from the Gene Expression Omnibus (GEO) database, and 124 differentially expressed genes (DEGs) were identified between BC tissues and normal breast tissues via R software and an online Venn diagram tool. Database for Annotation, Visualization and Integration Discovery (DAVID) software showed that 65 upregulated DEGs were mainly enriched in the regulation of the cell cycle, and Search Tool for the Retrieval of Interacting Genes (STRING) software identified the 39 closest associated upregulated DEGs in protein-protein interactions (PPIs), which validated the high expression of genes in BC tissues by the Gene Expression Profiling Interactive Analysis (GEPIA) tool. In addition, 36 out of 39 BC patients showed significantly worse outcomes by Kaplan-Meier plotter (KM plotter), and an additional Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that seven genes (cyclin E2 (CCNE2), cyclin B1 (CCNB1), cyclin B2 (CCNB2), mitotic checkpoint serine/threonine kinase B (BUB1B), dual-specificity protein kinase (TTK), cell division cycle 20 (CDC20), and pituitary tumor transforming gene 1 (PTTG1)) were markedly enriched in the cell cycle pathway. Analysis of the clinicopathological characteristics of hub genes revealed that seven cell cycle-related genes (CCRGs) were significantly highly expressed in four BC subtypes (luminal A, luminal B, HER2-positive and triple-negative (TNBC)), and except for the CCNE2 gene, high expression levels were significantly associated with tumor pathological grade and stage and metastatic events of BC. Furthermore, genetic mutation analysis indicated that genetic alterations of CCRGs could also significantly affect BC patients' prognosis. A quantitative real-time polymerase chain reaction (qRT-PCR) assay found that the seven CCRGs were significantly differentially expressed in BC cell lines. Integration of published multilevel expression data and a bioinformatics computational approach were used to predict and construct a regulation mechanism: a transcription factor (TF)-microRNA (miRNA)-messenger RNA (mRNA) regulation network. The present work is the first to construct a regulatory network of TF-miRNA-mRNA in BC for CCRGs and provides new insights into the molecular mechanism of BC.

Upregulated Collagen COL10A1 Remodels the Extracellular Matrix and Promotes Malignant Progression in Lung Adenocarcinoma

Collagens are major components of the ECM in various organs, including the lungs. Ectopic expression of collagens can regulate the tumor progression and disease outcome through remodeling of the extracellular matrix (ECM). However, it remains largely unexplored whether collagens are involved in the tumor progression of lung adenocarcinoma (LUAD). Analysis of three LUAD transcriptional expression profiles showed that COL10A1 mRNA expression was up-regulated and associated with poor prognosis. Gain- and loss-of-function studies were performed to observe that up-regulated COL10A1 promotes LUAD cell proliferation and invasion in vitro and in vivo. In molecular mechanism study, we found that COL10A1 interacts with DDR2 and affects the downstream FAK signaling pathway to regulate LUAD cell progression. The expression of COL10A1 on tissue microarray (TMA) was also measured to explore the association between COL10A1 expression and patient outcome. The results addressed that COL10A1 is up-regulated and positively correlated with lymph node metastasis in lung adenocarcinoma, and the COL10A1 expression is also an independent prognostic factor. In summary, the up-regulated COL10A1 remodels the ECM and the COL10A1/DDR2/FAK axis regulates the proliferation and metastasis of LUAD cells, implying that COL10A1 is a promising therapeutic target and prognostic marker for LUAD patients.

Transcriptomic study of yak mammary gland tissue during lactation

Yak milk, a high-quality milk, is one of the best raw materials for dairy products and economically important to pastoral herdsmen. To make a further understanding of the molecular differences in mammary tissues of the yaks with different milk production during lactation, in this study, we took the use of RNA-seq to perform high-throughput sequencing and analysis of the mammary gland transcriptomes of both high-yielding yak and low-yielding yaks during lactation. By the comparison and analysis of the transcriptome data for the mammary gland tissue of high-yielding yak and low-yield yak, 144 differential genes were screened out, of which 49 were upregulated and 95 were downregulated. Further functional analysis indicated that these differential genes involved in multiple classes based on Gene Ontology (GO) and multiple Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The GO analysis showed that the functions of the differential genes are closely related to the carbohydrate metabolism and other biological processes. KEGG pathway analysis revealed that these genes are mostly enriched in the pathway of antigen processing and presentation, phagosome pathway and type I diabetes pathway and enriched followed by extracellular matrix receptor interaction pathway. Moreover, several other pathways related to amino acid metabolism also showed significant enrichment. Here, the mammary gland transcriptomes of high-yielding yak and low-yielding yaks during lactation have for the first time been compared, and the related differential genes have been screened out and analyzed. Our study paves a way for the further elucidation of the basic molecular mechanism of yak mammary gland tissue, and at the same time provides new ideas for improving the milk production of yaks.

High plasma levels of COL10A1 are associated with advanced tumor stage in gastric cancer patients

BACKGROUND

Gastric cancer (GC) remains an aggressive malignancy with a high rate of mortality, being the third leading cause of cancer-related death. More than one million newly diagnosed cases and 782685 deaths due to GC were reported in 2018. GC is characterized by limited effective treatment options and the lack of consistent biomarkers for the diagnosis and prognosis of these patients. The discovery of new biomarkers useful in the early diagnosis of GC is mandatory.

AIM

To evaluate the potential of COL10A1 as a circulating biomarker for the diagnosis and prognosis of gastric adenocarcinoma patients.

METHODS

Plasma and tissue obtained from 49 patients with gastric adenocarcinoma have been used in exploring the expression of COL10A1. Real-time PCR and western blot techniques were used to evaluate COL10A1 level in gastric tumor tissue compared to normal adjacent tissue. The circulating level of COL10A1 was also evaluated by ELISA in plasma of gastric adenocarcinoma patients. Survival analysis was made in order to evaluate the potential of COL10A1 as a biomarker for the diagnosis and prognosis of gastric adenocarcinoma patients.

RESULTS

Our results showed a significant increase in COL10A1 gene expression and protein levels in gastric tumor tissue compared to adjacent normal tissue (P < 0.05). COL10A1 seems to show an elevated expression from the beginning of carcinogenesis, in the early stages, and its increased level remains elevated during cancer progression. A significant increase of COL10A1 plasma level in gastric adenocarcinoma patients was also identified. Moreover, increased COL10A1 plasma level was associated with poor survival of the patients. Plasma COL10A1 performed a diagnostic value in GC with area under the receiver operating characteristic curve (AUC) of 0.9171 (P = 0.0002), sensitivity of 87.76%, and specificity of 100.0%. Furthermore, this study demonstrated the potential role of plasma COL10A1 in the early detection of GC, as in the early stage, we obtained an AUC of 0.8789 (P = 0.0030), sensitivity of 81.25%, and specificity of 100.0%.

CONCLUSION

Circulating expression level of COL10A1 is significantly increased in gastric adenocarcinoma patients being associated with poor survival and is a potential biomarker for early detection of GC.

Collagen type VI α5 gene variations may predict the risk of lung cancer development in Chinese Han population

The abundant expression of collagen type VI α5 (COL6A5) exists in lung tissue, and its role in lung cancer is still unknown. We performed a genetic association study with an attempt to detect the relationships between single nucleotide polymorphisms (SNPs) in COL6A5 and lung cancer predisposition in Chinese Han population. We finally selected six tag-SNPs to determine their genotypes among 510 lung cancer patients and 495 healthy controls with the MassARRAY platform. The associations of SNPs and lung cancer risk were estimated by logistic regression method with adjustment for confounding factors. Two available databases were used for gene expression and prognosis analysis. COL6A5 rs13062453, rs1497305, and rs77123808 were significantly associated with the risk of lung cancer in the whole population or stratified subgroups (p < 0.05). Among them, COL6A5 rs13062453 and rs1497305 were also linked to the susceptibility of lung adenocarcinoma. Additionally, rs1497305 was found to be strongly related to the TNM staging under five genetic models (p < 0.05). Results from databases suggested the important role of COL6A5 in lung cancer development. COL6A5 polymorphisms rs13062453, rs1497305 and rs77123808 were associated with lung cancer risk in Chinese Han population. These findings first yield new insight of COL6A5 in lung cancer.

Study of Gene Expression Profiles of Breast Cancers in Indian Women

Breast cancer is the most common cancer among women globally. In India, the incidence of breast cancer has increased significantly during the last two decades with a higher proportion of the disease at a young age compared to the west. To understand the molecular processes underlying breast cancer in Indian women, we analysed gene expression profiles of 29 tumours and 9 controls using microarray. In the present study, we obtained 2413 differentially expressed genes, consisting of overexpressed genes such as COL10A1, COL11A1, MMP1, MMP13, MMP11, GJB2, and CST1 and underexpressed genes such as PLIN1, FABP4, LIPE, AQP7, LEP, ADH1A, ADH1B, and CIDEC. The deregulated pathways include cell cycle, focal adhesion and metastasis, DNA replication, PPAR signaling, and lipid metabolism. Using PAM50 classifier, we demonstrated the existence of molecular subtypes in Indian women. In addition, qPCR validation of expression of metalloproteinase genes, MMP1, MMP3, MMP11, MMP13, MMP14, ADAMTS1, and ADAMTS5 showed concordance with that of the microarray data; wherein we found a significant association of ADAMTS5 down-regulation with older age (≥55 years) of patients. Together, this study reports gene expression profiles of breast tumours from the Indian subcontinent, throwing light on the pathways and genes associated with the breast tumourigenesis in Indian women.

Prospective molecular mechanism of COL5A1 in breast cancer based on a microarray, RNA sequencing and immunohistochemistry

Breast cancer (BC) has a complex etiology and pathogenesis, and is the most common malignant tumor type in females, in USA in 2018, yet its relevant molecular mechanisms remain largely unknown. The collagen type V α-1 chain (COL5A1) gene is differentially expressed in renal and ovarian cancer. Using bioinformatics methods, COL5A1 was determined to also be a significant gene in BC, but its association with BC has not been sufficiently reported. COL5A1 microarray and relevant clinical data were collected from the Gene Expression Omnibus, The Cancer Genome Atlas and other databases to summarize COL5A1 expression in BC and its subtypes at the mRNA and protein levels. All associated information was comprehensively analyzed by various software. The clinical significance of the mutation was obtained via the cBioPortal. Furthermore, Gene Ontology functional annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were also performed to investigate the mechanism of COL5A1 in BC. Immunohistochemistry was also conducted to detect and confirm COL5A1 expression. It was determined that COL5A1 was highly expressed in BC tissues, compared with normal tissues at the mRNA level [standard mean difference, 0.84; 95% confidence interval (CI), 0.60-1.07; P=0.108]. The area under the summary receiver operator characteristic curve for COL5A1 was 0.87 (95% CI, 0.84-0.90). COL5A1 expression was altered in 32/817 (4%) sequenced samples. KEGG analysis confirmed the most notable pathways, including focal adhesion, extracellular matrix-receptor interaction and regulation of the actin cytoskeleton. Immunohistochemical detection was used to verify the expression of COL5A1 in 136 selected cases of invasive BC tissues and 55 cases of adjacent normal tissues, while the rate of high expression of COL5A1 in BC was up to 90.4%. These results indicated that COL5A1 is highly expressed at the mRNA and protein levels in BC, and the prognosis of patients with BC with high COL5A1 expression may be reduced; therefore, COL5A1 may be used independently or combined with other detection factors in BC diagnosis.

Development of prognostic signature and nomogram for patients with breast cancer

To identify prognostic signature that could predict the survival of patients with breast cancer (BC).Breast cancer samples and normal breast tissues in the TCGA-BRCA and GSE7390 were included. Differentially expressed genes (DEGs) were identified using the "limma" method. Overall survival (OS) associated with DEGs were obtained using univariate and multivariable Cox proportional-hazards regression analysis, and the corresponding prognostic signature and nomogram were constructed. Calibration analysis and decision curve analysis (DCA) were performed.In all, 742 DEGs were identified, 19 of which were independently correlated with the OS of BC patients. The OS of patients in the 19-gene signature low-risk group was significantly better than that in high-risk group (hazard ratio [HR] 0.3506, 95% confidence interval [CI] 0.2488-0.4939), and the 19-gene based signature was demonstrated to be an independent prognostic factor in patient with BC in the TCGA-BRCA cohort (HR 1.501, 95% CI 1.374-1.640) and validation cohort GSE7392 ((HR 0.3557, 95% CI 0.2155-0.5871, P < .0001)). The primary and internally validated C-indexes for the 19-gene signature-based nomogram were 0.817 and 8.013, respectively. The results of calibration analysis and DCA analysis confirmed the robustness and the clinical usability of the nomogram.We constructed a prognostic signature and nomogram for patient with BC, which showed good application prospect.

TGF-β1-SOX9 axis-inducible COL10A1 promotes invasion and metastasis in gastric cancer via epithelial-to-mesenchymal transition

Molecular biomarkers that predict disease progression might promote drug development and therapeutic strategies in aggressive cancers, such as gastric cancer (GC). High-throughput mRNA sequencing (RNA-seq) revealed that collagen type X alpha 1 (COL10A1) is a disease progression-associated gene. Analysis of 103 GC patients showed that high COL10A1 mRNA expression was associated with GC metastasis and reduced survival. We analyzed the COL10A1 promoter using the UCSC genome website and JASPAR database, and we found potential SOX9 binding site. Here, we demonstrated that SOX9 and COL10A1 were both up-regulated in GC. We observed a positive correlation between the expression patterns of SOX9 and COL10A1 in GC cells and tissues. The results of electrophoretic mobility shift assay (EMSA), chromatin immunoprecipitation (ChIP) assay and promoter reporter indicated that SOX9 could directly bind to the COL10A1 gene promoter and activate its transcription. Biological function experiments showed that COL10A1 regulated the migration and invasion of GC cells. Knockdown COL10A1 inhibited lung and abdominal cavity metastasis in a nude mouse model. Moreover, transforming growth factor-β1 (TGF-β1) treatment up-regulated the phosphorylation of Smad2 and increased SOX9 and COL10A1 expression. COL10A1 was confirmed to be a potential inducer of epithelial-to-mesenchymal transition (EMT). SOX9 was essential for COL10A1-mediated EMT, and cell migration, invasion and metastasis. Co-expression of SOX9 and COL10A1 was associated with tumor progression and was strongly predictive of overall survival in GC patients. In summary, this study elucidated the mechanistic link between COL10A1 and the TGF-β1-SOX9 axis. These findings indicated that COL10A1 might play a crucial role in GC progression and serve as a potential biomarker and therapeutic target in GC patients.

Breast Cancer Risk Factors: a Cross- Cultural Comparison between the West and the East

Background: The incidence of breast cancer in Lebanon is higher than any other Middle –Eastern country, is diagnosed at a younger age than women in Western countries, and is more aggressive and fatal. Therefore addressing risk factors in this population is of paramount importance. Methods: A descriptive cross-sectional, comparative design evaluated the risk factors for breast cancer in a convenient sample of 105 Lebanese-American women with 250 Lebanese. Odds Ratio, Chi square t-tests or ANOVA were used to compare the two groups’ risk factors and knowledge of screening tools. Associations found to be statistically significant were included in three multiple logistic regression models to estimate the odds of each variable for performing a mammography, a clinical breast exam (CBE) and a breast self-exam (BSE). Results: There were more Muslims in the Lebanese-American sample who perceived having a better income, and had a better understanding of the effectiveness of the mammogram, the CBE and the BSE. The Lebanese group reached menopause at an older age, t = 2.66, p = 0.05, smoked more, OR = 1.42, p = 0.001 and were five times more likely to live close to a main road or highway, OR = 5.75, p = 0.001 than the Lebanese-American group. The Lebanese- Americana group breast fed longer χ2= 11.68, p = .008, used contraceptives more, OR = 1.74, p = 0.027, exercised more, OR = 1.61, p < 0.001, and consumed more vegetables and fruits than their Lebanese counterparts, OR= 1.22, p = 0.002, and OR = 1.27 p = 0.001, respectively. For ever having a mammogram, the duration of breastfeeding (>6 months), effectiveness of the mammogram, and exercise were significant predictors. While for ever having a CBE, smoking, eating fruits, and exercise were significant. No variables were related to performing a BSE. Conclusion: The Lebanese women had higher risk factors for developing breast cancer, and had less knowledge of the benefits of breast cancer screening tools, calling for the importance of promoting healthy life styles and education.

A combined approach with gene-wise normalization improves the analysis of RNA-seq data in human breast cancer subtypes

Breast cancer (BC) is increasing in incidence and resistance to treatment worldwide. The challenges in limited therapeutic options and poor survival outcomes in BC subtypes persist because of its molecular heterogeneity and resistance to standard endocrine therapy. Recently, high throughput RNA sequencing (RNA-seq) has been used to identify biomarkers of disease progression and signaling pathways that could be amenable to specific therapies according to the BC subtype. However, there is no single generally accepted pipeline for the analysis of RNA-seq data in biomarker discovery due, in part, to the needs of simultaneously satisfying constraints of sensitivity and specificity. We proposed a combined approach using gene-wise normalization, UQ-pgQ2, followed by a Wald test from DESeq2. Our approach improved the analysis based on within-group comparisons in terms of the specificity when applied to publicly available RNA-seq BC datasets. In terms of identifying differentially expressed genes (DEGs), we combined an optimized log2 fold change cutoff with a nominal false discovery rate of 0.05 to further minimize false positives. Using this method in the analysis of two GEO BC datasets, we identified 797 DEGs uniquely expressed in triple negative BC (TNBC) and significantly associated with T cell and immune-related signaling, contributing to the immunotherapeutic efficacy in TNBC patients. In contrast, we identified 1403 DEGs uniquely expressed in estrogen positive and HER2 negative BC (ER+HER2-BC) and significantly associated with eicosanoid, notching and FAK signaling while a common set of genes was associated with cellular growth and proliferation. Thus, our approach to control for false positives identified two distinct gene expression profiles associated with these two subtypes of BC which are distinguishable by their molecular and functional attributes.

Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer

Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non-tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t-tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein-protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in ‘cell division’, the ‘proteinaceous extracellular matrix (ECM)’, ‘ECM structural constituents’ and ‘ECM-receptor interaction’, whereas downregulated genes were mainly enriched in ‘response to drugs’, ‘extracellular space’, ‘transcriptional activator activity’ and the ‘peroxisome proliferator-activated receptor signaling pathway’. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2-a, baculoviral inhibitor of apoptosis repeat-containing protein 5, cyclin-dependent kinase 1, G2/mitotic-specific cyclin-B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in ‘mitotic nuclear division’, ‘mid-body’, ‘protein binding’ and ‘cell cycle’. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide potential molecular targets and biomarkers for BC.

An integrated meta-analysis approach to identifying medications with potential to alter breast cancer risk through connectivity mapping

BACKGROUND

Gene expression connectivity mapping has gained much popularity in recent years with a number of successful applications in biomedical research testifying its utility and promise. A major application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state. In this study, we are additionally interested in small molecule compounds that may enhance a disease state or increase the risk of developing that disease. Using breast cancer as a case study, we aim to develop and test a methodology for identifying commonly prescribed drugs that may have a suppressing or inducing effect on the target disease (breast cancer).

RESULTS

We obtained from public data repositories a collection of breast cancer gene expression datasets with over 7000 patients. An integrated meta-analysis approach to gene expression connectivity mapping was developed, which involved unified processing and normalization of raw gene expression data, systematic removal of batch effects, and multiple runs of balanced sampling for differential expression analysis. Differentially expressed genes stringently selected were used to construct multiple non-joint gene signatures representing the same biological state. Remarkably these non-joint gene signatures retrieved from connectivity mapping separate lists of candidate drugs with significant overlaps, providing high confidence in their predicted effects on breast cancers. Of particular note, among the top 26 compounds identified as inversely connected to the breast cancer gene signatures, 14 of them are known anti-cancer drugs.

CONCLUSIONS

A few candidate drugs with potential to enhance breast cancer or increase the risk of the disease were also identified; further investigation on a large population is required to firmly establish their effects on breast cancer risks. This work thus provides a novel approach and an applicable example for identifying medications with potential to alter cancer risks through gene expression connectivity mapping.

Analysis of differential gene expression profile identifies novel biomarkers for breast cancer

Breast cancer is the most prevalent cancer diagnosis in women. We aimed to identify biomarkers for breast cancer prognosis. mRNA expression profiling was performed using Gene Chip Human Transcriptome Array 2.0. Microarray analysis and series test of cluster (STC) analysis were used to screen the differential expressed mRNAs and the expression trend of genes. Immumohistochemical staining with 100 clinical specimens was used to validate two differentially expressed genes, ITGA11 and Jab1. In the present study, significantly enriched Gene Ontology (GO) terms and pathways were identified. 26 model profiles were used to summarize the expression pattern of differentially expressed genes. Results of immunohistochemistry were consistent with those of the microarray, in that ITGA11 and Jab1 were differentially expressed with the same trend. Survival analyses using the Kaplan–Meier method demonstrated that breast cancer patients with high levels of either ITGA11 or Jab1 had a significant association with worse prognosis. Our study identified ITGA11 and Jab1 as novel biomarkers for breast cancer.