Identification of Personalized Chemoresistance Genes in Subtypes of Basal-Like Breast Cancer Based on Functional Differences Using Pathway Analysis

MDFI promotes the proliferation and tolerance to chemotherapy of colorectal cancer cells by binding ITGB4/LAMB3 to activate the AKT signaling pathway

Colorectal cancer (CRC) is one of the most lethal cancers. Single-cell RNA sequencing (scRNA-seq) and protein-protein interactions (PPIs) have enabled the systematic study of CRC. In our research, the activation of the AKT pathway in CRC was analyzed by KEGG using single-cell sequencing data from the GSE144735 dataset. The correlation and PPIs of MDFI and ITGB4/LAMB3 were examined. The results were verified in the TCGA and CCLE and further tested by coimmunoprecipitation experiments. The effect of MDFI on the AKT pathway via ITGB4/LAMB3 was validated by knockdown and lentiviral overexpression experiments. The effect of MDFI on oxaliplatin/fluorouracil sensitivity was probed by colony formation assay and CCK8 assay. We discovered that MDFI was positively associated with ITGB4/LAMB3. In addition, MDFI was negatively associated with oxaliplatin/fluorouracil sensitivity. MDFI upregulated the AKT pathway by directly interacting with LAMB3 and ITGB4 in CRC cells, and enhanced the proliferation of CRC cells via the AKT pathway. Finally, MDFI reduced the sensitivity of CRC cells to oxaliplatin and fluorouracil. In conclusion, MDFI promotes the proliferation and tolerance to chemotherapy of colorectal cancer cells, partially through the activation of the AKT signaling pathway by the binding to ITGB4/LAMB3. Our findings provide a possible molecular target for CRC therapy.

CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

SCAMP3 promotes breast cancer progression through the c-MYC-β-Catenin-SQSTM1 growth and stemness axis

The cellular trafficking protein secretory-carrier-membrane-protein 3 (SCAMP3) has been previously shown to promote hepatocellular carcinoma, melanoma, glioma and pancreatic adenocarcinoma. Moreover, previous work has shown that SCAMP3 regulates the epidermal growth factor receptor (EGFR) in triple negative breast cancer (TNBC). However, the oncogenic role of SCAMP3 in different molecular subtypes of breast cancer (BRCA) remains largely unknown. In this study, the role of SCAMP3 in different molecular subtypes of BRCA was investigated using in silico, in vitro and in vivo approaches. In silico analysis of BRCA patient samples showed that SCAMP3 is highly overexpressed in different BRCA molecular subtypes, advanced disease grades and lymph node metastatic stages. Depletion of SCAMP3 inhibited BRCA cell growth, stemness, clonogenic potential and migration and promoted autophagy and cellular senescence. The expression of stemness markers CD44 and OCT4A was reduced in SCAMP3-silenced MDA-MB-231 cells. SCAMP3 overexpression promoted cell proliferation, clonogenicity, tumor spheroid formation and migration in vitro and tumor growth in vivo. SCAMP3 promoted epithelial-mesenchymal-transition (EMT) by regulating E-cadherin expression. SCAMP3 enhanced in vivo tumor growth in MDA-MB-231 tumor xenograft mouse model. Mechanistically, SCAMP3 depletion inhibited β-Catenin, c-MYC and SQSTM1 expression, while its overexpression increased the expression of the same oncogenic proteins. Increased SCAMP3 expression associated with increased chemoresistance in BRCA cells while its depletion associated with increased sensitivity to chemotherapy. BRCA patients with high SCAMP3 expression showed poor prognosis, decreased overall survival and relapse free survival relative to counterparts with reduced SCAMP3 expression. These findings suggest that SCAMP3 exerts a wide range of oncogenic effects in different molecular subtypes of BRCA by modulating the c-MYC-β-Catenin-SQSTM1 axis that targets tumor growth, metastasis, stemness and chemoresistance.

Transcriptomic Analysis of Subtype-Specific Tyrosine Kinases as Triple Negative Breast Cancer Biomarkers

Triple negative breast cancer (TNBC) shows impediment to the development of targeted therapies due to the absence of specific molecular targets. The high heterogeneity across TNBC subtypes, which can be classified to be at least four subtypes, including two basal-like (BL1, BL2), a mesenchymal (M), and a luminal androgen receptor (LAR) subtype, limits the response to cancer therapies. Despite many attempts to identify TNBC biomarkers, there are currently no effective targeted therapies against this malignancy. In this study, thus, we identified the potential tyrosine kinase (TK) genes that are uniquely expressed in each TNBC subtype, since TKs have been typically used as drug targets. Differentially expressed TK genes were analyzed from The Cancer Genome Atlas (TCGA) database and were confirmed with the other datasets of both TNBC patients and cell lines. The results revealed that each TNBC subtype expressed distinct TK genes that were specific to the TNBC subtype. The identified subtype-specific TK genes of BL1, BL2, M, and LAR are LYN, CSF1R, FGRF2, and SRMS, respectively. These findings could serve as a potential biomarker of specific TNBC subtypes, which could lead to an effective treatment for TNBC patients.

Integrative and Comprehensive Pan-Cancer Analysis of Lymphocyte-Specific Protein Tyrosine Kinase in Human Tumors

Lymphocyte-specific protein tyrosine kinase (LCK) is common in a variety of hematologic malignancies but comparatively less common in solid tumors. This study aimed to explore the potential diagnostic and prognostic value of LCK across tumors through integrative and comprehensive pan-cancer analysis, as well as experimental validation. Multiple databases were used to explore the expression, alteration, prognostic value, association with immune infiltration, and potential functional pathways of LCK in pan-cancers. The results were further validated by western blotting and qPCR of patient samples as well as tumor cell lines. High LCK expression typically represents a better prognosis. Notably, drug sensitivity prediction of LCK identified P-529 as a candidate for drug development. Gene Annotations (GO) and KEGG analyses showed significant enrichment of PD-L1 and the T-cell receptor pathway. The results from patient samples and tumor cell lines confirmed these conclusions in LIHC. In conclusion, LCK is differentially expressed in multiple tumors and normal tissues. Further analysis highlighted its association with prognostic implications, pan-cancer genetic alterations, and immune signatures. Our data provide evidence for a diagnostic marker of LCK and the possible use of LCK as a target for the treatment of tumors.

HOXA1, a breast cancer oncogene

More than 25 years ago, the first literature records mentioned HOXA1 expression in human breast cancer. A few years later, HOXA1 was confirmed as a proper oncogene in mammary tissue. In the following two decades, molecular data about the mode of action of the HOXA1 protein, the factors contributing to activate and maintain HOXA1 gene expression and the identity of its target genes have accumulated and provide a wider view on the association of this transcription factor to breast oncogenesis. Large-scale transcriptomic data gathered from wide cohorts of patients further allowed refining the relationship between breast cancer type and HOXA1 expression. Several recent reports have reviewed the connection between cancer hallmarks and the biology of HOX genes in general. Here we take HOXA1 as a paradigm and propose an extensive overview of the molecular data centered on this oncoprotein, from what its expression modulators, to the interactors contributing to its oncogenic activities, and to the pathways and genes it controls. The data converge to an intricate picture that answers questions on the multi-modality of its oncogene activities, point towards better understanding of breast cancer aetiology and thereby provides an appraisal for treatment opportunities.

An aberrant DNA methylation signature for predicting the prognosis of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy worldwide with a poor prognosis. DNA methylation is an epigenetic modification that plays a critical role in the etiology and pathogenesis of HNSCC. The current study aimed to develop a predictive methylation signature based on bioinformatics analysis to improve the prognosis and optimize therapeutic outcome in HNSCC. Clinical information and methylation sequencing data of patients with HNSCC were downloaded from The Cancer Genome Atlas database. The R package was used to identify differentially methylated genes (DMGs) between HNSCC and adjacent normal tissues. We identified 22 DMGs associated with 246 differentially methylated sites. Patients with HNSCC were classified into training and test groups. Cox regression analysis was used to build a risk score formula based on the five methylation sites (cg26428455, cg13754259, cg17421709, cg19229344, and cg11668749) in the training group. The Kaplan–Meier survival curves showed that the overall survival (OS) rates were significantly different between the high‐ and low‐risk groups sorted by the signature in the training group (median: 1.38 vs. 1.57 years, log‐rank test, p < 0.001). The predictive power was then validated in the test group (median: 1.34 vs. 1.75 years, log‐rank test, p < 0.001). The area under the receiver operating characteristic curve (area under the curve) based on the signature for predicting the 5‐year survival rates, was 0.7 in the training and 0.73 in test groups, respectively. The results of multivariate Cox regression analysis showed that the riskscore (RS) signature based on the five methylation sites was an independent prognostic tool for OS prediction in patients. In addition, a predictive nomogram model that incorporated the RS signature and patient clinical information was developed. The innovative methylation signature‐based model developed in our study represents a robust prognostic tool for guiding clinical therapy and predicting the OS in patients with HNSCC.

New horizons in drug discovery of lymphocyte-specific protein tyrosine kinase (Lck) inhibitors: a decade review (2011-2021) focussing on structure-activity relationship (SAR) and docking insights

Lymphocyte-specific protein tyrosine kinase (Lck), a non-receptor Src family kinase, has a vital role in various cellular processes such as cell cycle control, cell adhesion, motility, proliferation, and differentiation. Lck is reported as a key factor regulating the functions of T-cell including the initiation of TCR signalling, T-cell development, in addition to T-cell homeostasis. Alteration in expression and activity of Lck results in numerous disorders such as cancer, asthma, diabetes, rheumatoid arthritis, atherosclerosis, and neuronal diseases. Accordingly, Lck has emerged as a novel target against different diseases. Herein, we amass the research efforts in literature and pharmaceutical patents during the last decade to develop new Lck inhibitors. Additionally, structure-activity relationship studies (SAR) and docking models of these new inhibitors within the active site of Lck were demonstrated offering deep insights into their different binding modes in a step towards the identification of more potent, selective, and safe Lck inhibitors.

DDRS: Detection of drug response SNPs specifically in patients receiving drug treatment

Detecting SNPs associated with drug efficacy or toxicity is helpful to facilitate personalized medicine. Previous studies usually find SNPs associated with clinical outcome only in patients received a specific treatment. However, without information from patients without drug treatment, it is possible that the detected SNPs are associated with patients' clinical outcome even without drug treatment. Here we aimed to detect drug response SNPs based on data from patients with and without drug treatment through combing the cox proportional-hazards model and pairwise Kaplan-Meier survival analysis. A pipeline named Detection of Drug Response SNPs (DDRS) was built and applied to TCGA breast cancer data including 363 patients with doxorubicin treatment and 321 patients without any drug treatment. We identified 548 doxorubicin associated SNPs. Drug response score derived from these SNPs were associated with drug-resistant level (indicated by IC₅₀) of breast cancer cell lines. Enrichment analyses showed that these SNPs were enriched in active epigenetic regulation markers (e.g., H3K27ac). Compared with random genes, the cis-eQTL genes of these SNPs had a shorter protein-protein interaction distance to doxorubicin associated genes. In addition, linear discriminant analysis showed that the eQTL gene expression levels could be used to predict clinical outcome for patients with doxorubicin treatment (AUC = 0.738). Specifically, we identified rs2817101 as a drug response SNP for doxorubicin treatment. Higher expression level of its cis-eQTL gene GSTA1 is associated with poorer survival. This approach can also be applied to identify new drug associated SNPs in other cancers.

The remodelling of actin composition as a hallmark of cancer

Actin is a key structural protein that makes up the cytoskeleton of cells, and plays a role in functions such as division, migration, and vesicle trafficking. It comprises six different cell-type specific isoforms: ACTA1, ACTA2, ACTB, ACTC1, ACTG1, and ACTG2. Abnormal actin isoform expression has been reported in many cancers, which led us to hypothesize that it may serve as an early biomarker of cancer. We show an overview of the different actin isoforms and highlight mechanisms by which they may contribute to tumorigenicity. Furthermore, we suggest how the aberrant expression of actin subunits can confer cells with greater proliferation ability, increased migratory capability, and chemoresistance through incorporation into the normal cellular F-actin network and altered actin binding protein interaction. Studying this fundamental change that takes place within cancer cells can further our understanding of neoplastic transformation in multiple tissue types, which can ultimately aid in the early-detection, diagnosis and treatment of cancer.

Characterization of the Relationship Between the Expression of Aspartate β-Hydroxylase and the Pathological Characteristics of Breast Cancer

Background

This study aimed to investigate the relationship between the expression of aspartate β-hydroxylase (ASPH) and the molecular mechanisms of ASPH-related genes in breast cancer (BC).

Material/Methods

ASPH expression was determined by immunohistochemistry and western blot analysis in samples of BC tissues and adjacent normal tissues. ASPH mRNA expression data and their clinical significance in BC were retrieved from the Oncomine and GEPIA datasets. Enrichment analysis of genes coexpressed with ASPH and annotation of potential pathways were performed with Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analysis. Hub genes were shown in an ASPH coexpression gene-interaction network. The expression of the hub genes associated with patient survival were analyzed to determine the role of ASPH in the progression of BC.

Results

ASPH levels were overexpressed in BC and correlated with cancer type, lymph node involvement, and TNM stage. Conversely, ASPH levels did not correlate with patient age, invasive carcinoma types, or molecular subtypes. Enrichment analysis showed the involvement of multiple pathways, including lipid metabolism and oxidation-reduction processes. Six hub genes, PPARG, LEP, PLIN1, AGPAT2, CAV1, and PNPLA2, were related to ASPH expression and had functional roles in the occurrence and progression of BC.

Conclusions

ASPH may be involved in the development of BC and may have utility as a prognostic biomarker in BC. The coexpression of ASPH-associated genes may also be beneficial in improving BC prognosis.

Expression of PAWR predicts prognosis of ovarian cancer

Background

Ovarian cancer greatly threatens the general health of women worldwide. Implementation of predictive prognostic biomarkers aids in ovarian cancer management.

Methods

Using online databases, the general expression profile, target-disease associations, and interaction network of PAWR were explored. To identify the role of PAWR in ovarian cancer, gene correlation analysis, survival analysis, and combined analysis of drug responsiveness and PAWR expression were performed. The predictive prognostic value of PAWR was further validated in clinical samples.

Results

PAWR was widely expressed in normal and cancer tissues, with decreased expression in ovarian cancer tissues compared with normal tissues. PAWR was associated with various cancers including ovarian cancer. PAWR formed a regulatory network with a group of proteins and correlated with several genes, which were both implicated in ovarian cancer and drug responsiveness. High PAWR expression denoted better survival in ovarian cancer patients (OS: HR = 0.84, P = 0.0077; PFS, HR = 0.86, P = 0.049). Expression of PAWR could predict platinum responsiveness in ovarian cancer and there was a positive correlation between PAWR gene effect and paclitaxel sensitivity. In 12 paired clinical samples, the cancerous tissues exhibited significantly lower PAWR expression than matched normal fallopian tubes. The predictive prognostic value of PAWR was maintained in a cohort of 50 ovarian cancer patients.

Conclusions

High PAWR expression indicated better survival and higher drug responsiveness in ovarian cancer patients. PAWR could be exploited as a predictive prognostic biomarker in ovarian cancer.

Identification of the anti-breast cancer targets of triterpenoids in Liquidambaris Fructus and the hints for its traditional applications

BACKGROUND

Liquidambaris Fructus is the infructescences of Liquidambar formosana Hance and it has been used to treat some breast disease in Traditional Chinese Medicine. In the previous study we found the anti-breast cancer effect of triterpenoid in Liquidambaris Fructus. This study is a further investigation of the triterpenoids in Liquidambaris Fructus and aims to identify their anti-breast cancer targets, meanwhile, to estimate the rationality of the traditional applications of Liquidambaris Fructus.

METHODS

Triterpenoids in Liquidambaris Fructus were isolated and their structures were identified by NMR spectrums. Potential targets of these triterpenoids were predicted using a reverse pharmacophore mapping strategy. Associations between these targets and the therapeutic targets of breast cancer were analyzed by constructing protein-protein interaction network, and targets played important roles in the network were identified using Molecular Complex Detection method. Binding affinity between the targets and triterpenoids was studied using molecular docking method. Gene ontology enrichment analysis was conducted to reveal the biological process and signaling pathways that the identified targets were involved in.

RESULTS

Thirteen triterpenoids were identified and 6 of them were the first time isolated from Liquidambaris Fructus. Predicted ADME properties revealed a good druggability of these triterpenoids. We identified 18 protein targets which were closely related to breast cancer progression, especially triple-negative, basal-like or advanced stage breast cancers. The triterpenoids could bind with these targets as their inhibitors: hydrophobic skeleton is a favorable factor for them to stabilize at binding site and polar C17- or C3- substituent was necessary for binding. GO enrichment analysis indicated that inhibition of protein tyrosine kinases autophosphorylation might be the primary mechanism for the anti-breast cancer effect of the triterpenoids, and ErbB4 and EGFR were the most relevant targets.

CONCLUSIONS

The study revealed that triterpenoids from Liquidambaris Fructus might exert anti-breast cancer effect by directly inhibit multiple protein targets and signaling pathways, especially ErbB4 and EGFR and related pathways. This study also brings up another hint that the traditional applications of Liquidambaris Fructus on hypogalactia should be reassessed systematically because it might suppress rather than promote lactation by inhibiting the activity of ErbB4.

Beyond TCR Signaling: Emerging Functions of Lck in Cancer and Immunotherapy

In recent years, the lymphocyte-specific protein tyrosine kinase (Lck) has emerged as one of the key molecules regulating T-cell functions. Studies using Lck knock-out mice or Lck-deficient T-cell lines have shown that Lck regulates the initiation of TCR signaling, T-cell development, and T-cell homeostasis. Because of the crucial role of Lck in T-cell responses, strategies have been employed to redirect Lck activity to improve the efficacy of chimeric antigen receptors (CARs) and to potentiate T-cell responses in cancer immunotherapy. In addition to the well-studied role of Lck in T cells, evidence has been accumulated suggesting that Lck is also expressed in the brain and in tumor cells, where it actively takes part in signaling processes regulating cellular functions like proliferation, survival and memory. Therefore, Lck has emerged as a novel druggable target molecule for the treatment of cancer and neuronal diseases. In this review, we will focus on these new functions of Lck.

Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene Identification

OBJECTIVE

The aim of this study was to identify the molecular subtypes of chronic obstructive pulmonary disease (COPD) and to prioritize COPD candidate genes using bioinformatics methods.

MATERIALS AND METHODS

In this bioinformatics study, the gene expression dataset GSE76705 (including 229 COPD samples) and known COPD-related genes (candidate genes) were downloaded from the Gene Expression Omnibus (GEO) and the Online Mendelian Inheritance in Man (OMIM) databases respectively. Based on the expression values of the candidate genes, COPD samples were divided into molecular subtypes through hierarchical clustering analysis. Candidate genes were accordingly allocated into the defined molecular subtypes and functional enrichment analysis was undertaken. Pathway deviation scores were then analyzed, followed by the analysis of clinical indicators (FEV1, FEV1/FVC, age and gender) of COPD patients in each subtype, and prediction models were constructed. Furthermore, the gene expression dataset GSE71220 was used to bioinformatically validate our results.

RESULTS

A total of 213 COPD-related genes were identified, which divided samples into three subtypes based on the gene expression values. After intersection analysis, 160 common genes including transforming growth factor β1 (TGFB1), epidermal growth factor receptor (EGFR) and interleukin 13 (IL13) were obtained. Functional enrichment analysis identified 22 pathways such as 'hsa04060: cytokine-cytokine receptor interaction pathways, 'hsa04110: cell cycle' and 'hsa05222: small cell lung cancer'. Pathways in subtype 2 had higher deviation scores. Furthermore, three receiver operating characteristic (ROC) curves (accuracies >80%) were constructed. The three subtypes in COPD samples were also identified in the validation dataset GSE71220.

CONCLUSION

COPD may be further subdivided into several molecular subtypes, which may be useful in improving COPD therapy based on the molecular subtype of a patient.

Ceramide Metabolism Balance, a Multifaceted Factor in Critical Steps of Breast Cancer Development

Ceramides are key lipids in energetic-metabolic pathways and signaling cascades, modulating critical physiological functions in cells. While synthesis of ceramides is performed in endoplasmic reticulum (ER), which is altered under overnutrition conditions, proteins associated with ceramide metabolism are located on membrane arrangement of mitochondria and ER (MAMs). However, ceramide accumulation in meta-inflammation, condition that associates obesity with a chronic low-grade inflammatory state, favors the deregulation of pathways such as insulin signaling, and induces structural rearrangements on mitochondrial membrane, modifying its permeability and altering the flux of ions and other molecules. Considering the wide biological processes in which sphingolipids are implicated, they have been associated with diseases that present abnormalities in their energetic metabolism, such as breast cancer. In this sense, sphingolipids could modulate various cell features, such as growth, proliferation, survival, senescence, and apoptosis in cancer progression; moreover, ceramide metabolism is associated to chemotherapy resistance, and regulation of metastasis. Cell–cell communication mediated by exosomes and lipoproteins has become relevant in the transport of several sphingolipids. Therefore, in this work we performed a comprehensive analysis of the state of the art about the multifaceted roles of ceramides, specifically the deregulation of ceramide metabolism pathways, being a key factor that could modulate neoplastic processes development. Under specific conditions, sphingolipids perform important functions in several cellular processes, and depending on the preponderant species and cellular and/or tissue status can inhibit or promote the development of metabolic and potentially breast cancer disease.

Prognostic significance of microsatellite instability‑associated pathways and genes in gastric cancer

The aim of the present study was to reveal the potential molecular mechanisms of microsatellite instability (MSI) on the prognosis of gastric cancer (GC). The investigation was performed based on an RNAseq expression profiling dataset downloaded from The Cancer Genome Atlas, including 64 high‑level MSI (MSI‑H) GC samples, 44 low‑level MSI (MSI‑L) GC samples and 187 stable microsatellite (MSI‑S) GC samples. Differentially expressed genes (DEGs) were identified between the MSI‑H, MSI‑L and MSI‑S samples. Pathway enrichment analysis was performed for the identified DEGs and the pathway deviation scores of the significant enrichment pathways were calculated. A Multi‑Layer Perceptron (MLP) classifier, based on the different pathways associated with the MSI statuses was constructed for predicting the outcome of patients with GC, which was validated in another independent dataset. A total of 190 DEGs were selected between the MSI‑H, MSI‑L and MSI‑S samples. The MLP classifier was established based on the deviation scores of 10 significant pathways, among which antigen processing and presentation, and inflammatory bowel disease pathways were significantly enriched with HLA‑DRB5, HLA‑DMA, HLA‑DQA1 and HLA‑DRA; the measles, toxoplasmosis and herpes simplex infection pathways were significantly enriched with Janus kinase 2 (JAK2), caspase‑8 (CASP8) and Fas. The classifier performed well on an independent validation set with 100 GC samples. Taken together, the results indicated that MSI status may affect GC prognosis, partly through the antigen processing and presentation, inflammatory bowel disease, measles, toxoplasmosis and herpes simplex infection pathways. HLA‑DRB5, HLA‑DMA, HLA‑DQA1, HLA‑DRA, JAK2, CASP8 and Fas may be predictive factors for prognosis in GC.

Immunoglobulin superfamily genes are novel prognostic biomarkers for breast cancer

Breast cancer progression is associated with dysregulated expression of the immunoglobulin superfamily (IgSF) genes that are involved in cell-cell recognition, binding and adhesion. Despite widespread evidence that many IgSF genes could serve as effective biomarkers, this potential has not been realized because the studies have focused mostly on individual genes and not the entire network. To gain a global perspective of the IgSF-related biomarkers, we constructed an IgSF-directed neighbor network (IDNN) and an IgSF-directed driver network (IDDN) by integrating multiple levels of data, including IgSF genes, breast cancer driver genes, protein-protein interaction (PPI) networks and gene expression profiling data. Our study shows that IgSF genes in the PPI network have important topological features related to cancer. Most IgSF genes are either cancer driver genes themselves or associated with them. We also identified a 21-gene IgSF network module with enriched mutations that are associated with overall survival based on 450 breast cancer patient samples extracted from The Cancer Genome Atlas (TCGA) and multiple independent microarray validation datasets. These results highlight the potential of IgSF genes as novel diagnostic, prognostic and therapeutic targets for breast cancer.

Tumor microenvironment-mediated chemoresistance in breast cancer

Therapy resistance or tumor relapse in cancer is common. Tumors develop resistance to chemotherapeutic through a variety of mechanisms, with tumor microenvironment (TM) serving pivotal roles. Using breast cancer as a paradigm, we propose that responses of cancer cells to drugs are not exclusively determined by their intrinsic characteristics but are also controlled by deriving signals from TM. Affected microenvironment by chemotherapy is an avenue to promote phenotype which tends to resist on to be ruined. Therefore, exclusively targeting cancer cells does not demolish tumor recurrence after chemotherapy. Regardless of tumor-microenvironment pathways and their profound influence on the responsiveness of treatment, diversity of molecular properties of breast cancer also behave differently in terms of response to chemotherapy. And also it is assumed that there is cross-talk between phenotypic diversity and TM. Collectively, raising complex signal from TM in chemotherapy condition often encourages cancer cells are not killed but strengthen. Here, we summarized how TM modifies responses to chemotherapy in breast cancer. We also discussed successful treatment strategies have been considered TM in breast cancer treatment.