Integrated network analysis and machine learning approach for the identification of key genes of triple-negative breast cancer

Potential prognostic biomarkers of hepatocellular carcinoma based on 4D label-free quantitative proteomics analysis pilot investigation

BACKGROUND

Hepatocellular carcinoma carries a poor prognosis and poses a serious threat to global health. Currently, there are few potential prognostic biomarkers available for the prognosis of hepatocellular carcinoma.

METHODS

This pilot study used 4D label-free quantitative proteomics to compare the proteomes of hepatocellular carcinoma and adjacent non-tumor tissue. A total of 66,075 peptides, 6363 identified proteins, and 772 differentially expressed proteins were identified in specimens from three hepatocellular carcinoma patients. Through functional enrichment analysis of differentially expressed proteins by Gene Ontology, KEGG pathway, and protein domain, we identified proteins with similar functions.

RESULTS

Twelve differentially expressed proteins (RPL17, RPL27, RPL27A, RPS5, RPS16, RSL1D1, DDX18, RRP12, TARS2, YARS2, MARS2, and NARS1) were selected for identification and validation by parallel reaction monitoring. Subsequent Western blotting confirmed overexpression of RPL27, RPS16, and TARS2 in hepatocellular carcinoma compared to non-tumor tissue in 16 pairs of clinical samples. Analysis of The Cancer Genome Atlas datasets associated the increased expression of these proteins with poor prognosis. Tissue microarray revealed a negative association between high expression of RPL27 and TARS2 and the prognosis of hepatocellular carcinoma patients, although RPS16 was not significant.

CONCLUSIONS

These data suggest that RPL27 and TARS2 play an important role in hepatocellular carcinoma progression and may be potential prognostic biomarkers of overall survival in hepatocellular carcinoma patients.

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging. However, the knowledge in this regard is still far from complete. Thus, this study aimed to explore these knowledge gaps by analyzing existing gene expression profile data from 3149 breast cancer samples, where each sample was represented by the expression of 19,644 genes and classified into Nottingham histological grade (NHG) classes (Grade 1, 2, and 3). To this end, a machine learning-based framework was designed. First, the profile data were analyzed by using seven feature ranking algorithms to evaluate the importance of features (genes). Seven feature lists were generated, each of which sorted features in accordance with feature importance evaluated from a special aspect. Then, the incremental feature selection method was applied to each list to determine essential features for classification and building efficient classifiers. Consequently, overlapping genes, such as AURKA, CBX2, and MYBL2, were deemed as potentially related to breast cancer malignancy and prognosis, indicating that such genes were identified to be important by multiple feature ranking algorithms. In addition, the study formulated classification rules to reflect special gene expression patterns for three NHG classes. Some genes and rules were analyzed and supported by recent literature, providing new references for studying breast cancer.

Sex differences in colonic gene expression and fecal microbiota composition in a mouse model of obesity-associated colorectal cancer

This study investigated the sex-specific correlation between obesity and colorectal cancer emphasizing a more pronounced association in males. Estrogen, chromosomal genes, and gut bacteria were assessed in C57BL6/J male, female and ovariectomized (OVX) female mice, subjected to either a low-fat diet (LFD) or high-fat diet (HFD) for 14 weeks. Induction of colon tumor involved azoxymethane (10 mg/kg) administration, followed by three cycles of dextran sulfate sodium. Male mice on HFD exhibited higher final body weight and increased colon tumors compared to females. Colonic mucin 2 expression was significantly higher in females. HFD-modulated differentially expressed genes numbered 290 for males, 64 for females, and 137 for OVX females. Only one up-regulated gene (Gfra3) overlapped between females and OVX females, while two down-regulated genes (Thrsp and Gbp11) overlapped between males and OVX females. Genes up-regulated by HFD in males were linked to cytokine-cytokine interaction, HIF-1 signaling pathway, central carbon metabolism in cancer. Sex-specific changes in gut microbial composition in response to HFD were observed. These findings suggest a male-specific vulnerability to HFD-induced colon tumor formation, implicating key genes and colonic bacteria in colon tumorigenesis.

Molecular targets and therapeutic strategies for triple-negative breast cancer

Triple-negative breast cancer (TNBC) is known for its heterogeneous complexity and is often difficult to treat. TNBC lacks the expression of major hormonal receptors like estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 and is further subdivided into androgen receptor (AR) positive and AR negative. In contrast, AR negative is also known as quadruple-negative breast cancer (QNBC). Compared to AR-positive TNBC, QNBC has a great scarcity of prognostic biomarkers and therapeutic targets. QNBC shows excessive cellular growth and proliferation of tumor cells due to increased expression of growth factors like EGF and various surface proteins. This study briefly reviews the limited data available as protein biomarkers that can be used as molecular targets in treating TNBC as well as QNBC. Targeted therapy and immune checkpoint inhibitors have recently changed cancer treatment. Many studies in medicinal chemistry continue to focus on the synthesis of novel compounds to discover new antiproliferative medicines capable of treating TNBC despite the abundance of treatments currently on the market. Drug repurposing is one of the therapeutic methods for TNBC that has been examined. Moreover, some additional micronutrients, nutraceuticals, and functional foods may be able to lower cancer risk or slow the spread of malignant diseases that have already been diagnosed with cancer. Finally, nanomedicines, or applications of nanotechnology in medicine, introduce nanoparticles with variable chemistry and architecture for the treatment of cancer. This review emphasizes the most recent research on nutraceuticals, medication repositioning, and novel therapeutic strategies for the treatment of TNBC.

Hedgehog gene expression patterns among intrinsic subtypes of breast cancer: Prognostic relevance

PURPOSE

Hedgehog (Hh) signaling is a crucial developmental regulatory pathway recognized as a primary oncogenesis driver in various human cancers. However, its role in breast carcinoma (BC) has been underexplored.

METHODS

We analyzed the expression of several Hh associated genes in a clinical series and breast cancer cell lines. We included 193 BC stratified according to intrinsic immunophenotypes. Gene expression profiling ofBOC, PTCH, SMO, GLI1, GLI2, and GLI3 was performed by qRT-PCR. Results were correlated with clinical-pathological variables and outcome.

RESULTS

We observed expression ofGLI2 in triple-negative/basal-like (TN/BL) and GLI3 in luminal cells. In samples, BOC, GLI1, GLI2, and GLI3 expression correlated significantly with luminal tumors and good prognostic factors. In contrast, PTCH and SMO correlated with TN/BL phenotype and nodal involvement. Patients whose tumors expressed SMO had a poorer outcome, especially those with HER2 phenotype. Positive lymph-node status and high SMO remained independent poor prognostic factors.

CONCLUSION

Our results support a differential Hh pathway activation in BC phenotypes.SMO levels stratified patients at risk of recurrence and death in HER2 phenotype, and it showed an independent prognostic value. Therefore, SMO could be a potential therapeutic target for a subset of BC patients.

Construction and Analysis of mRNA and lncRNA Regulatory Networks Reveal the Key Genes Associated with Prostate Cancer Related Fatigue During Localized Radiation Therapy

Background:

Localized radiation therapy is the first-line option for the treatment of nonmetastatic prostate cancer (PCa). Previous studies revealed that long non-coding RNAs (lncRNAs) had crucial roles in disease progression. However, the mechanisms of lncRNAs underlying prostate cancerrelated fatigue remained largely unclear.

Objective:

The present study aimed to uncover the key genes related to PCa related fatigue during localized radiation therapy by constructing mRNA and lncRNA regulatory networks.

Methods:

We analyzed GSE30174, which included 10 control samples and 40 PCa related fatigue samples, to identify differently expressed lncRNAs and mRNAs in PCa related fatigue. A proteinprotein interaction network was constructed to reveal the interactions among mRNAs. Co-expression network analysis was applied to identify the key lncRNAs and reveal the functions of these lncRNAs in PCa related fatigue.

Results and Discussion:

This research found 1271 dysregulated mRNAs and 205 dysregulated lncRNAs in PCa related fatigue using GSE30174. Bioinformatics analysis showed that PCa related fatigue with mRNAs and lncRNAs were associated with inflammatory response and immune response related biological processes. Furthermore, we constructed a PPI network and lncRNA co-expression network related to fatigue in PCa. Of note, we observed that the dysregulated lncRNAs and mRNAs, such as SEC61A2, ADCY6, LPAR5, COL7A1, ALB, COL1A1, SNHG1, LINC01215, LINC00926, GNG4, LMO7, and COL4A6, in PCa related fatigue could predict the outcome of PCa patients.

Conclusions:

This research could provide novel mechanisms underlying fatigue and identify new biomarkers for the prognosis of PCa.

Bioinformatics Analysis Identifies IL6ST as a Potential Tumor Suppressor Gene for Triple-Negative Breast Cancer

Improved insight into the molecular mechanisms of triple-negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study was to identify genes significantly associated with TNBC and further analyze their prognostic significance. The Cancer Genome Atlas (TCGA) TNBC database and gene expression profiles of GSE76275 from Gene Expression Omnibus (GEO) were used to explore differentially co-expressed genes in TNBC compared with those in normal tissues and non-TNBC breast cancer tissues. Differential gene expression and weighted gene co-expression network analyses identified 24 differentially co-expressed genes. Functional annotation suggested that these genes were primarily enriched in processes such as metabolism, membrane, and protein binding. The protein-protein interaction (PPI) network further identified ten hub genes, five of which (MAPT, CBS, SOX11, IL6ST, and MEX3A) were confirmed to be differentially expressed in an independent dataset (GSE38959). Moreover, CBS and MEX3A expression was upregulated, whereas IL6ST expression was downregulated in TNBC tissues compared to that in other breast cancer subtypes. Furthermore, lower expression of IL6ST was associated with worse overall survival in patients with TNBC. Thus, IL6ST might play an important role in TNBC progression and could serve as a tumor suppressor gene for diagnosis and treatment.

Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease

Background

Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learning approach.

Methods

At the transcriptomic level, four microarray datasets (GSE41177, GSE79768, GSE115574, GSE14975) were downloaded from the Gene Expression Omnibus database, which included 130 available atrial samples from AF and sinus rhythm (SR) patients with valvular heart disease. Microarray meta-analysis was adopted to identified differentially expressed genes (DEGs). At the proteomic level, a qualitative and quantitative analysis of proteomics in the left atrial appendage of 18 patients (9 with AF and 9 with SR) who underwent cardiac valvular surgery was conducted. The machine learning correlation-based feature selection (CFS) method was introduced to selected feature genes of AF using the training set of 130 samples involved in the microarray meta-analysis. The Naive Bayes (NB) based classifier constructed using training set was evaluated on an independent validation test set GSE2240.

Results

863 DEGs with FDR < 0.05 and 482 differentially expressed proteins (DEPs) with FDR < 0.1 and fold change > 1.2 were obtained from the transcriptomic and proteomic study, respectively. The DEGs and DEPs were then analyzed together which identified 30 biomarkers with consistent trends. Further, 10 features, including 8 upregulated genes (CD44, CHGB, FHL2, GGT5, IGFBP2, NRAP, SEPTIN6, YWHAQ) and 2 downregulated genes (TNNI1, TRDN) were selected from the 30 biomarkers through machine learning CFS method using training set. The NB based classifier constructed using the training set accurately and reliably classify AF from SR samples in the validation test set with a precision of 87.5% and AUC of 0.995.

Conclusion

Taken together, our present work might provide novel insights into the molecular mechanism and provide some promising diagnostic and therapeutic targets of AF.

Quadruple-negative breast cancer: novel implications for a new disease

Based on the androgen receptor (AR) expression, triple-negative breast cancer (TNBC) can be subdivided into AR-positive TNBC and AR-negative TNBC, also known as quadruple-negative breast cancer (QNBC). QNBC characterization and treatment is fraught with many challenges. In QNBC, there is a greater paucity of prognostic biomarkers and therapeutic targets than AR-positive TNBC. Although the prognostic role of AR in TNBC remains controversial, many studies revealed that a lack of AR expression confers a more aggressive disease course. Literature characterizing QNBC tumor biology and uncovering novel biomarkers for improved management of the disease remains scarce. In this comprehensive review, we summarize the current QNBC landscape and propose avenues for future research, suggesting potential biomarkers and therapeutic strategies that warrant investigation.

Identification of the Significant Genes Regulated by Estrogen Receptor in Estrogen Receptor-Positive Breast Cancer and Their Expression Pattern Changes When Tamoxifen or Fulvestrant Resistance Occurs

Breast cancer is the most frequent malignant tumor in women, and the estrogen receptor (ER) plays a vital role in the vast majority of breast cancers. The purpose of the present study was to identify the significant genes regulated by ER in ER-positive breast cancer and to explore their expression pattern changes when tamoxifen or fulvestrant resistance occurs. For this purpose, the gene expression profiles GSE11324, GSE27473, and GSE5840 from the Gene Expression Omnibus database were used, which contain gene expression data from MCF7 cells treated with estrogen, MCF7 cells with silencing of ER, and tamoxifen- and fulvestrant-resistant MCF7 cells treated with estrogen (17β-estradiol), respectively. Differentially expressed genes (DEGs) between the treatment group and negative control were identified and subjected to pathway enrichment and protein–protein interaction (PPI) analyses. There were 230 DEGs in common among the three datasets, including 160 genes positively regulated by ER and 70 genes negatively regulated by ER. DEGs mainly showed enrichment for pathways in cancer, progesterone-mediated oocyte maturation, RNA transport, glycerophospholipid metabolism, oocyte meiosis, platelet activation, and so on. PPI network and modular analysis selected three significant clusters containing 19 genes. A total of 44 genes were involved in Kyoto Encyclopedia of Gene and Genome pathway results or PPI modular analysis, and 16 of them were found to correlate with relapse-free survival in patients with ER⁺/human epidermal growth factor receptor 2-negative breast cancer who had undergone endocrine therapies only. Some of the genes’ expression patterns were different among wild-type, tamoxifen-resistant, and fulvestrant-resistant MCF7 cells such as DDX18, ANAPC7, MAD2L1, RSL1D1, and CALCR, etc., indicating different resistance mechanisms and potential prognostic markers or therapeutic targets for fulvestrant- or tamoxifen-resistant breast cancer.

Analysis of differentially expressed mRNAs and the prognosis of cholangiocarcinoma based on TCGA database

Background

The effective evaluation of cholangiocarcinoma (CCA) is challenging due to a lack of accurate screening tools. Consequently, there is an urgent need to screen out effective biomarkers. Bioinformatics analysis on a substantial amount of transcriptomic data to screen biomolecules allows for the verification of histological samples, and can provide a new method for CCA biomolecule screening in diagnosis and prognosis.

Methods

EdgeR model was used to analyze The Cancer Genome Atlas (TCGA)-extracted CCA data set, and to determine the differential expression of mRNAs. Based on this, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to perform functional and pathway enrichment analysis. Subsequently, a protein interaction network was also established to identify the key differential node genes. Then, the previously determined differential genes were analyzed to establish a link between these genes and clinical prognosis. Finally, we used tissue samples to realize our results via IHC, Western blot and qRT-PCR.

Results

A total of 5,561 differential mRNAs were screened, including 3,473 upregulated genes and 2,088 downregulated genes. GO and KEGG enrichment analysis showed that the upregulated genes had significantly enriched cell adhesion, concentrated chromosomal motility, and microtubule motility. Downregulated genes were significantly enriched in heterologous metabolism and exosomes. Furthermore, we found upregulated genes were significantly enriched in the cancer pathways and cell cycle. Downregulated genes were enriched in the metabolic pathways and biosynthesis of antibiotics. Ten hub genes were screened out through the protein interaction network; among these, the AURKB and PLK1 genes were closely related to the clinical prognosis of patients. Results of the immunohistochemical staining, Western blot and qRT-PCR all showed that the expression of AURKB and PLK1 in cancer tissues was higher than that in the adjacent tissues, and this difference was statistically significant (P<0.05).

Conclusions

The upregulated genes were significantly enriched in the biological processes of cell division, cell cycle, and related cell components. AURKB and PLK1 play a key role in differentially expressed gene nodes. These genes are closely related to the prognosis of patients and can be used as potential diagnostic tools and prognostic biomarkers.

Therapeutic opportunities for PLK1 inhibitors: Spotlight on BRCA1-deficiency and triple negative breast cancers

Polo-Like Kinases (PLKs) are central players of mitotic progression in Eukaryotes. Given the intimate relationship between cell cycle progression and cancer development, PLKs in general and PLK1 in particular have been thoroughly studied as biomarkers and potential therapeutic targets in oncology. The oncogenic properties of PLK1 overexpression across different types of human cancers are attributed to its roles in promoting mitotic entry, centrosome maturation, spindle assembly and cytokinesis. While several academic labs and pharmaceutical companies were able to develop potent and selective inhibitors of PLK1 (PLK1i) for preclinical research, such compounds have reached only limited success in clinical trials despite their great pharmacokinetics. Even though this could be attributed to multiple causes, the housekeeping roles of PLK1 in both normal and cancer cells are most likely the main reason for clinical trials failure and withdraw due to toxicities issues. Therefore, great efforts are being invested to position PLK1i in the treatment of specific types of cancers with revised dosages schemes. In this mini review we focus on two potential niches for PLK1i that are supported by recent evidence: triple negative breast cancers (TNBCs) and BRCA1-deficient cancers. On the one hand, we recollect several lines of strong evidence indicating that TNBCs are among the cancers with highest PLK1 expression and sensitivity to PLK1i. These findings are encouraging because of the limited therapeutics options available for TNBC patients, which rely mainly on classic chemotherapy. On the other hand, we discuss recent evidence that unveils synthetic lethality induction by PLK1 inhibition in BRCA1-deficient cancers cells. This previously unforeseen therapeutic link between PLK1 and BRCA1 is promising because it defines novel therapeutic opportunities for PLK1i not only for breast cancer (i.e. TNBCs with BRCA1 deficiencies), but also for other types of cancers with BRCA1-deficiencies, such as pancreatic and prostate cancers.

Quadruple negative breast cancer

Quadruple negative breast cancer (QNBC), lacking the expression of ER (estrogen receptor), PR (progesterone receptor), HER2 (human epidermal growth factor receptor-2) and AR (androgen receptor), was regarded as one breast cancer subtype with the worst prognosis. Recently, the molecular features of QNBC are not well understood. Different from AR-positive triple-negative breast cancer, QNBC is insensitive to conventional chemotherapeutic agents and has no efficient treatment targets. However, QNBC has been shown to express unique proteins that may be amenable to use in the development of targeted therapies. Here we reviewed the features of QNBC and proteins that may serve as effective targets for QNBC treatment, such as ACSL4, SKP2, immune checkpoint inhibitors, EGFR, MicroRNA signatures and Engrailed 1.

Identification of Hub Genes Using Co-Expression Network Analysis in Breast Cancer as a Tool to Predict Different Stages

Background

Breast cancer has a high mortality rate and is the most common cancer of women worldwide. Our gene co-expression network analysis identified the genes closely related to the pathological stage of breast cancer.

Material/Methods

We performed weighted gene co-expression network analysis (WGCNA) from the Gene Expression Omnibus (GEO) database, and performed pathway enrichment analysis on genes from significant modules.

Results

A non-metastatic sample (374) of breast cancer from GSE102484 was used to construct the gene co-expression network. All 49 hub genes have been shown to be upregulated, and 19 of the 49 hub genes are significantly upregulated in breast cancer tissue. The roles of the genes CASC5, CKAP2L, FAM83D, KIF18B, KIF23, SKA1, GINS1, CDCA5, and MCM6 in breast cancer are unclear, so in order to better reveal the staging of breast cancer markers, it is necessary to study those hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes indicated that 49 hub genes were enriched to sister chromatid cohesion, spindle midzone, microtubule motor activity, cell cycle, and something else. Additionally, there is an independent data set – GSE20685 – for module preservation analysis, survival analysis, and gene validation.

Conclusions

This study identified 49 hub genes that were associated with pathologic stage of breast cancer, 19 of which were significantly upregulated in breast cancer. Risk stratification, therapeutic decision making, and prognosis predication might be improved by our study results. This study provides new insights into biomarkers of breast cancer, which might influence the future direction of breast cancer research.

AURKB: a promising biomarker in clear cell renal cell carcinoma

Background

Aurora kinase B (AURKB) is an important carcinogenic factor in various tumors, while its role in clear cell renal cell carcinoma (ccRCC) still remains unclear. This study aimed to investigate its prognostic value and mechanism of action in ccRCC.

Methods

Gene expression profiles and clinical data of ccRCC patients were downloaded from The Cancer Genome Atlas database. R software was utilized to analyze the expression and prognostic role of AURKB in ccRCC. Gene set enrichment analysis (GSEA) was used to analyze AURKB related signaling pathways in ccRCC.

Results

AURKB was expressed at higher levels in ccRCC tissues than normal kidney tissues. Increased AURKB expression in ccRCC correlated with high histological grade, pathological stage, T stage, N stage and distant metastasis (M stage). Kaplan-Meier survival analysis suggested that high AURKB expression patients had a worse prognosis than patients with low AURKB expression levels. Multivariate Cox analysis showed that AURKB expression is a prognostic factor of ccRCC. GSEA indicated that genes involved in autoimmune thyroid disease, intestinal immune network for IgA production, antigen processing and presentation, cytokine-cytokine receptor interaction, asthma, etc., were differentially enriched in the AURKB high expression phenotype.

Conclusions

AURKB is a promising biomarker for predicting prognosis of ccRCC patients and a potential therapeutic target. In addition, AURKB might regulate progression of ccRCC through modulating intestinal immune network for IgA production and cytokine-cytokine receptor interaction, etc. signaling pathways. However, more research is necessary to validate the findings.

A genome-wide screen for human salicylic acid (SA)-binding proteins reveals targets through which SA may influence development of various diseases

Salicylic acid (SA) is the major metabolite and active ingredient of aspirin; both compounds reduce pain, fever, and inflammation. Despite over a century of research, aspirin/SA's mechanism(s) of action is still only partially understood. Here we report the results of a genome-wide, high-throughput screen to identify potential SA-binding proteins (SABPs) in human HEK293 cells. Following photo-affinity crosslinking to 4-azidoSA and immuno-selection with an anti-SA antibody, approximately 2,000 proteins were identified. Among these, 95 were enriched more than 10-fold. Pathway enrichment analysis with these 95 candidate SABPs (cSABPs) revealed possible involvement of SA in multiple biological pathways, including (i) glycolysis, (ii) cytoskeletal assembly and/or signaling, and (iii) NF-κB-mediated immune signaling. The two most enriched cSABPs, which corresponded to the glycolytic enzymes alpha-enolase (ENO1) and pyruvate kinase isozyme M2 (PKM2), were assessed for their ability to bind SA and SA's more potent derivative amorfrutin B1 (amoB1). SA and amoB1 bound recombinant ENO1 and PKM2 at low millimolar and micromolar concentrations, respectively, and inhibited their enzymatic activities in vitro. Consistent with these results, low millimolar concentrations of SA suppressed glycolytic activity in HEK293 cells. To provide insights into how SA might affect various human diseases, a cSABP-human disorder/disease network map was also generated.

Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer

Background

Triple negative breast cancer (TNBC) is a specific subtype of breast cancer with a poor prognosis due to its aggressive biological behaviour and lack of therapeutic targets. We aimed to explore some novel genes and pathways related to TNBC prognosis through bioinformatics methods as well as potential initiation and progression mechanisms.

Methods

Breast cancer mRNA data were obtained from The Cancer Genome Atlas database (TCGA). Differential expression analysis of cancer and adjacent cancer, as well as, triple negative breast cancer and non-triple negative breast cancer were performed using R software. The key genes related to the pathogenesis were identified by functional and pathway enrichment analysis and protein-protein interaction network analysis. Based on univariate and multivariate Cox proportional hazards model analyses, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic performance of our model.

Results

Based on mRNA expression profiling of breast cancer patients from the TCGA database, 755 differentially expressed overlapping mRNAs were detected between TNBC/non-TNBC samples and normal tissue. We found eight hub genes associated with the cell cycle pathway highly expressed in TNBC. Additionally, a novel six-gene (TMEM252, PRB2, SMCO1, IVL, SMR3B and COL9A3) signature from the 755 differentially expressed mRNAs was constructed and significantly associated with prognosis as an independent prognostic signature. TNBC patients with high-risk scores based on the expression of the 6-mRNAs had significantly shorter survival times compared to patients with low-risk scores (P < 0.0001).

Conclusions

The eight hub genes we identified might be tightly correlated with TNBC pathogenesis. The 6-mRNA signature established might act as an independent biomarker with a potentially good performance in predicting overall survival.