Identification of novel prognostic indicators for triple-negative breast cancer patients through integrative analysis of cancer genomics data and protein interactome data

A 7-lncRNA signature associated with the prognosis of colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is the most common colon cancer exhibiting high mortality. Due to their association with cancer progression, long noncoding RNAs (lncRNAs) are now being used as prognostic biomarkers. In the present study, we used relevant clinical information and expression profiles of lncRNAs originating from The Cancer Genome Atlas database, aiming to construct a prognostic lncRNA signature to estimate the prognosis of patients.

Methods

The samples were randomly spilt into training and validation cohorts. In the training cohort, prognosis-related lncRNAs were selected from differentially expressed lncRNAs using the univariate Cox analysis. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were employed for identifying prognostic lncRNAs. The prognostic signature was constructed by these lncRNAs.

Results

The prognostic model was able to calculate each COAD patient’s risk score and split the patients into groups of low and high risks. Compared to the low-risk group, the high-risk group had significant poor prognosis. Next, the prognostic signature was validated in the validation, as well as all cohorts. The receiver operating characteristic (ROC) curve and c-index were determined in all cohorts. Moreover, these prognostic lncRNA signatures were combined with clinicopathological risk factors to construct a nomogram for predicting the prognosis of COAD in the clinic. Finally, seven lncRNAs (CTC-273B12.10, AC009404.2, AC073283.7, RP11-167H9.4, AC007879.7, RP4-816N1.7, and RP11-400N13.2) were identified and validated by different cohorts. The Kyoto Encyclopedia of Genes and Genomes analysis of the mRNAs co-expressed with the seven prognostic lncRNAs suggested four significantly upregulated pathways, which were AGE-RAGE, focal adhesion, ECM-receptor interaction, and PI3K/Akt signaling pathways.

Conclusion

Thus, our study verified that the seven lncRNAs mentioned can be used as biomarkers to predict the prognosis of COAD patients and design personalized treatments.

Deconvolution of the Genomic and Epigenomic Interaction Landscape of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer. Emerging evidenced suggests that both genetics and epigenetic factors play a role in the pathogenesis of TNBC. However, oncogenic interactions and cooperation between genomic and epigenomic variation have not been characterized. The objective of this study was to deconvolute the genomic and epigenomic interaction landscape in TNBC using an integrative genomics approach, which integrates information on germline, somatic, epigenomic and gene expression variation. We hypothesized that TNBC originates from a complex interplay between genomic (both germline and somatic variation) and epigenomic variation. We further hypothesized that these complex arrays of interacting genomic and epigenomic factors affect entire molecular networks and signaling pathways which, in turn, drive TNBC. We addressed these hypotheses using germline variation from genome-wide association studies and somatic, epigenomic and gene expression variation from The Cancer Genome Atlas (TCGA). The investigation revealed signatures of functionally related genes containing germline, somatic and epigenetic variations. DNA methylation had an effect on gene expression. Network and pathway analysis revealed molecule networks and signaling pathways enriched for germline, somatic and epigenomic variation, among them: Role of BRCA1 in DNA Damage Response, Hereditary Breast Cancer Signaling, Molecular Mechanisms of Cancer, Estrogen-Dependent Breast Cancer, p53, MYC Mediated Apoptosis, and PTEN Signaling pathways. The investigation revealed that integrative genomics is a powerful approach for deconvoluting the genomic-epigenomic interaction landscape in TNBC. Further studies are needed to understand the biological mechanisms underlying oncogenic interactions between genomic and epigenomic factors in TNBC.

Proteins with Evolutionarily Hypervariable Domains are Associated with Immune Response and Better Survival of Basal-like Breast Cancer Patients

Maltase-glucoamylase (MGAM) and MGAM2 both belong to the glycoside hydrolase family 31. MGAM, a therapeutic target for type 2 diabetes, is α-1,4-glucosidase and expressed in the intestine to catalyze starch digestion. MGAM2, however, is largely uncharacterized. By investigating The Cancer Genome Atlas data, we found that among breast cancer subtypes, MGAM2 expression is nearly exclusive to basal-like breast cancers (BLBCs), whereas MGAM tends to express in luminal A breast cancers. Moreover, MGAM2 expression is associated with better patient survival and correlated with immune genes/signatures, unlike MGAM. Both genes have emerged in mammals, but diverged after the placental-marsupial split. In placentals, MGAM2 has likely lost its α-1,4-glucosidase activity due to mutations in key catalytic sites, and has acquired a large domain that is extracellular, threonine-rich and evolutionarily hypervariable (EHV). Guided by MGAM2 findings, our genome-wide search identified >1000 human proteins with EHV regions. These proteins are enriched in immune functions and molecules, including major histocompatibility complex proteins. Their genes are expressed higher in BLBCs and are associated with better patient survival, like MGAM2. Their EHV-coding sequences are rich in simple repeats and harbor more cancer passenger mutations. In conclusion, MGAM2 diverges from MGAM structurally and likely functionally in placentals. MGAM2 is among >1000 human proteins with EHV regions and associated with immune response. We propose that these EHV molecules may have significant implication in cancer immunotherapy and BLBC treatment.

Identification of modules of hepatic encephalopathy based on protein-protein network and gene expression data

Hepatic encephalopathy (HE) is regarded as a complication of liver cirrhosis, and 50–75% of patients who have been diagnosed with cirrhosis have HE syndrome. The aim of this study was to identify genes and pathways associated with HE alcoholics. Human protein-protein interactions were downloaded from the STRING database. Gene expression data were downloaded from EMBL-EBI. Combined score and Pearson's correlation coefficient were calculated to construct differential co-expression networks. Graph-theoretical measure was used to calculate the module connectivity dynamic score of multiple differential modules. In total, 11,134 genes were obtained after mapping between probes and genes. Then, 501,736 pairs and 16,496 genes were obtained to form background protein-protein interaction networks, 1,435 edges and 460 nodes were obtained constituting differential co-expression networks. Twenty-three seed genes and 10 significantly differential modules were identified. Four significantly differential modules which had larger connectivity alternation were observed. The identified seed genes and significantly differential modules offer novel understanding and molecular targets for the treatment of HE alcoholics.

Identification of differentially expressed genes in human breast cancer cells induced by 4-hydroxyltamoxifen and elucidation of their pathophysiological relevance and mechanisms

While tamoxifen (TAM) is used for treating estrogen receptor (ER)a-positive breast cancer patients, its anti-breast cancer mechanisms are not completely elucidated. This study aimed to examine effects of 4-hydroxyltamoxifen (4-OH-TAM) on ER-positive (ER⁺) breast cancer MCF-7 cell growth and gene expression profiles. MCF-7 cell growth was inhibited by 4-OH-TAM dose-dependently with IC₅₀ of 29 μM. 332 genes were up-regulated while 320 genes were down-regulated. The mRNA levels of up-regulated genes including STAT1, STAT2, EIF2AK2, TGM2, DDX58, PARP9, SASH1, RBL2 and USP18 as well as down-regulated genes including CCDN1, S100A9, S100A8, ANXA1 and PGR were confirmed by quantitative real-time PCR (qRT-PCR). In human breast tumor tissues, mRNA levels of EIF2Ak2, USP18, DDX58, RBL2, STAT2, PGR, S1000A9, and CCND1 were significantly higher in ER⁺- than in ER^--breast cancer tissues. The mRNA levels of EIF2AK2, TGM2, USP18, DDX58, PARP9, STAT2, STAT1, PGR and CCND1 were all significantly higher in ER⁺-tumor tissues than in their corresponding tumor-adjacent tissues. These genes, except PGR and CCND1 which were down-regulated, were also up-regulated in ER⁺ MCF-7 cells by 4-OH-TAM. Total 14 genes mentioned above are involved in regulation of cell proliferation, apoptosis, cell cycles, and estrogen and interferon signal pathways. Bioinformatics analysis also revealed other novel and important regulatory factors that are associated with these genes and involved in the mentioned functional processes. This study has paved a foundation for elucidating TAM anti-breast cancer mechanisms in E2/ER-dependent and independent pathways.

Integrative analysis of lncRNAs and miRNAs with coding RNAs associated with ceRNA crosstalk network in triple negative breast cancer

Triple negative breast cancer (TNBC) is a particular subtype of breast malignant tumor with poorer prognosis than other molecular subtypes. Currently, there is increasing focus on long non-coding RNAs (lncRNAs), which can act as competing endogenous RNAs (ceR-NAs) and suppress miRNA functions involved in post-transcriptional regulatory networks in the tumor. Therefore, to investigate specific mechanisms of TNBC carcinogenesis and improve treatment efficiency, we comprehensively integrated expression profiles, including data on mRNAs, lncRNAs and miRNAs obtained from 116 TNBC tissues and 11 normal tissues from The Cancer Genome Atlas. As a result, we selected the threshold with |log2FC|>2.0 and an adjusted p-value >0.05 to obtain the differentially expressed mRNAs, miRNAs and lncRNAs. Hereafter, weighted gene co-expression network analysis was performed to identify the expression characteristics of dysregulated genes. We obtained five co-expression modules and related clinical feature. By means of correlating gene modules with protein-protein interaction network analysis that had identified 22 hub mRNAs which could as hub target genes. Eleven key dysregulated differentially expressed micro RNAs (DEmiRNAs) were identified that were significantly associated with the 22 hub potential target genes. Moreover, we found that 14 key differentially expressed lncRNAs could interact with the key DEmiRNAs. Then, the ceRNA crosstalk network of TNBC was constructed by utilizing key lncRNAs, key miRNAs, and hub mRNAs in Cytoscape software. We analyzed and described the potential characteristics of biological function and pathological roles of the TNBC ceRNA co-regulatory network; also, the survival analysis was performed for each molecule. These findings revealed that ceRNA crosstalk network could play an important role in the development and progression for TNBC. In addition, we also identified that some molecules in the ceRNA network possess clinical correlation and prognosis.

Male occult triple-negative breast cancer with dermatomyositis: a case report and review of the literature

Occult breast cancer is defined by the presence of axillary metastases without an identifiable primary breast tumor. Here, we report a rare case of a male occult breast cancer with dermatomyositis. We performed a modified radical mastectomy consisting of whole breast mastectomy and axillary lymph node dissection. Immunohistochemistry and fluorescent in situ hybridization analyses demonstrated an adenocarcinoma likely of breast origin, which was an occult triple-negative breast cancer. Interestingly, the patient’s previously noted periorbital dermatomyositis resolved promptly following surgical excision.

A long non-coding RNA expression signature to predict survival of patients with colon adenocarcinoma

Colon cancer is the most common type of gastrointestinal cancer and is still the leading cause of cancer-related mortality worldwide. Long non-coding RNAs (lncRNAs) have been proved to be superior biomarkers in cancer diagnosis and prognosis than miRNAs and protein-coding genes. In the current study, our objective was to detect novel lncRNA biomarkers by analyzing lncRNA expression profiles and clinical data in a large cohort of patients with colon patients from The Cancer Genome Atlas (TCGA). By using Cox regression analysis, we identified two lncRNAs (SNHG6 and CTD-2354A18.1) which could be independent prognostic factors for predicting clinical outcome in colon adenocarcinoma. Then a linear combination of these two lncRNA biomarkers (SNHG6 and CTD-2354A18.1), termed two-lncRNA signature, was developed in the training set as a predictor for OS in patients with colon adenocarcinoma, and validated in the testing set and the entire patient set. This two-lncRNA signature demonstrated significant prognostic performance in both the testing set and the entire patient set which classified the patients into two groups with significantly different OS. The multivariate and stratified analysis suggested that the prognostic value of the two-lncRNA signature was independent of other traditional clinical variables. Functional analysis suggested that these two lncRNA biomarkers might be mainly involved in transcription/translation-related or DNA repair-related biological processes. In summary, our results warrant further studies on these lncRNAs that will improve our understanding of the mechanisms associated with pathogenesis and progression of colon adenocarcinoma.

Microarray analyses reveal genes related to progression and prognosis of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma is a high morbidity and mortality cancer in China. Here are few biomarkers and therapeutic targets. Our study was aimed to identify candidate genes correlated to ESCC. Oncomine, The Cancer Genome Atlas, Gene Expression Omnibus were retrieved for eligible ESCC data. Deregulated genes were identified by meta-analysis and validated by an independent dataset. Survival analyses and bioinformatics analyses were used to explore potential mechanisms. Copy number variant analyses identified upstream mechanisms of candidate genes. In our study, top 200 up/down-regulated genes were identified across two microarrays. A total of 139 different expression genes were validated in GSE53625. Survival analysis found that nine genes were closely related to prognosis. Furthermore, Gene Ontology analyses and Kyoto Encyclopedia of Genes and Genomes analyses showed that different expression genes were mainly enriched in cell division, cell cycle and cell-cell adhesion pathways. Copy number variant analyses indicated that overexpression of ECT2 and other five genes were correlated with copy number amplification. The current study demonstrated that ECT2 and other eight candidate genes were correlated to progression and prognosis of esophageal squamous cell carcinoma, which might provide novel insights to the mechanisms.

Identification of potential long non-coding RNA biomarkers associated with the progression of colon cancer

Increasing evidence has suggested that dysregulated lncRNA expression played important roles in the development and progression of human cancers. Although prognostic roles of lncRNAs have been recognized for colon cancer (CC) patients, the search for novel lncRNA biomarkers potentially involved in CC progression is an urgent and still largely unmet medical need. In this study, we evaluated the lncRNA expression changes during the progression of CC by analyzing two cohorts of previously published expression profiles of CC patients and identified hundreds of differentially expressed lncRNAs. Then we identified eight lncRNAs that closely associated with the progression of CC patients from a large number of significantly altered lncRNAs using random forest supervised classification algorithm. Finally, an SVM-based lncRNA risk classifier was developed to discriminate high-risk CC patients from persons with early-stage and validated in both the training dataset and testing dataset by survival analysis and five-fold cross-validation strategy. Our pathway enrichment analysis based on protein-coding genes that are co-expressed with lncRNAs, suggested that variation in expression of eight lncRNAs biomarkers might affect critical pathways involved in CC progression. With further validation, these eight lncRNAs might have significant implications for the clinical management of CC patients with early stage and improve our understanding of cancer progression.

Identification of long non-coding RNAs biomarkers associated with progression of endometrial carcinoma and patient outcomes

Endometrial carcinoma is a complex disease characterized by both genetic, epigenetic and environmental factors. Increasing evidence has suggested that long non-coding RNAs (lncRNAs) play important roles in the development and progression of cancers. In this study, we performed a comparison analysis for lncRNA expression between patients with early-stage (stage I/II) and those with advanced-stage (stage III/IV) derived from The Cancer Genome Atlas (TCGA) project and identified 17 differentially expressed lncRNAs using student t-test. Five of the 17 differentially expressed lncRNAs were selected as optimal biomarkers that are significantly associated with progression of UCEC using random forest feature selection procedure. A risk classifier of five lncRNAs was developed to as a molecular signature that identifies patients at high risk for progression using support vector machine. Results of five-lncRNA risk classifier achieved high discriminatory performance in distinguishing advanced stage from early stage with 78% prediction accuracy, 96.6% sensitivity and 76.6% specificity. Functional analysis suggested that these five lncRNA biomarkers may play critical roles in the progression of UCEC by participating in important cancer-related biological processes. Our study will help to improve our understanding of underlying mechanisms in the progression of UCEC and provide novel lncRNAs as candidate predictive biomarkers for the identification of patients with high risk for progression.