• Diagnostic efficacy
• Drug sensitivity
• Invasive ductal breast carcinoma-associated stroma
• Molecular docking
• Prognostic-risk model

• Antibody
• Antibody-drug conjugates
• Cancer treatment
• Claudin
• chimeric antigen receptor T cells

• Humans
• Claudins/metabolism
• Claudins/antagonists & inhibitors
• Neoplasms/diagnosis
• Neoplasms/metabolism
• Neoplasms/drug therapy
• Biomarkers, Tumor/metabolism
• Tight Junctions/metabolism
• Molecular Targeted Therapy
• Animals

• Biomarker
• Cancer
• Claudins
• Expression
• Pathological diagnosis

• Adjacent normal tissues
• Breast cancer subtypes
• Differential correlations
• Escape velocity centrality
• Influential genes
• Normal breast tissues
• Switching genes.

• Female
• Humans
• Breast Neoplasms/genetics
• Gene Regulatory Networks

• bioinformatics
• gastric carcinoma
• microenvironment
• nomogram
• prognostic model

• National Natural Science Foundation of China

Despite increasingly sophisticated medical technology, the prognosis of patients with advanced gastric cancer is still not objectively certain. Therefore, it is urgent to identify new diagnostic and prognostic biomarkers. To identify potential critical genes related to gastric cancer’s staging mechanism and to the prognosis of gastric cancer.

Dynamic trend analysis was conducted to find genes with similar trends in gastric cancer staging in order to explore the differentially expressed genes in gastric cancer and identify the intersection of the results of the dynamic trend analysis. Functional predictive analysis were performed on the obtained genes to observe the expression of prognostic genes in gastric cancer and in gastric cancer stages as well as the correlation with tumor immune cell infiltration. Gastric cancer samples were collected and sequenced for follow-up analysis based on the results of the Cancer Genome Atlas (TCGA) database analysis.

The expression of genes enriched in module 0 had a similar trend in gastric cancer staging. 3213 differential genes were screened. A total of 50 intersection genes were obtained among genes with similar trends, of which only 10 genes have prognostic significance in gastric cancer. These 10 genes were correlated with macrophage infiltration in varying degrees. In addition, we found that AGT was significantly abnormally expressed in the results of sample sequencing. AGT was related to the occurrence of gastric cancer and interacted with brd9, golph3, nom1, klhl25, and psmd11.

AGT has prominent abnormal expression in gastric cancer and may promote gastric cancer progression. This study provides a new direction for further exploring potential biomarkers and molecular targeted gastric cancer therapy.

• RNA-seq
• TNM stage
• bioinformatics analysis
• gastric cancer
• tumor immune cell infiltration

• DNA methylation
• N-acetyltransferase 1
• breast cancer
• drug resistance
• long non-coding RNA H19

• HER2neu
• breast cancer
• expression
• methylation
• microRNA

Breast cancer, the most frequent malignancy in women worldwide, is a heterogeneous group of diseases, characterized by distinct molecular aberrations. In precision medicine, radiation oncology for breast cancer aims at tailoring treatment according to tumor biology and each patient’s clinical features and genetics. Although systemic therapies are personalized according to molecular sub-type [i.e. endocrine therapy for receptor-positive disease and anti-human epidermal growth factor receptor 2 (HER2) therapy for HER2-positive disease] and multi-gene assays, personalized radiation therapy has yet to be adopted in the clinical setting. Currently, attempts are being made to identify prognostic and/or predictive factors, biomarkers, signatures that could lead to personalized treatment in order to select appropriate patients who might, or might not, benefit from radiation therapy or whose radiation therapy might be escalated or de-escalated in dosages and volumes. This overview focuses on what has been achieved to date in personalized post-operative radiation therapy and individual patient radiosensitivity assessments by means of tumor sub-types and genetics.

• biomarkers
• breast cancer
• gene profiles
• genetic assay
• molecular subtypes
• personalized medicine
• precision medicine
• radiation oncology

Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input variables of survival model, is larger than the amount of available cancer patient samples, deep-learning models are prone to overfitting. To address the issue, we introduce a new deep-learning architecture called VAECox. VAECox uses transfer learning and fine tuning.

We pre-trained a variational autoencoder on all RNA-seq data in 20 TCGA datasets and transferred the trained weights to our survival prediction model. Then we fine-tuned the transferred weights during training the survival model on each dataset. Results show that our model outperformed other previous models such as Cox Proportional Hazard with LASSO and ridge penalty and Cox-nnet on the 7 of 10 TCGA datasets in terms of C-index. The results signify that the transferred information obtained from entire cancer transcriptome data helped our survival prediction model reduce overfitting and show robust performance in unseen cancer patient samples.

Our implementation of VAECox is available at https://github.com/dmis-lab/VAECox.

Supplementary data are available at Bioinformatics online.

• antitumor therapy
• biomineralization
• calcification
• macromolecular drug
• polysialic acid

Breast cancer (BC) is the leading cause of cancer death among women worldwide. The molecular mechanisms of its pathogenesis are still to be investigated. In our study, differentially expressed genes (DEGs) were screened between BC and normal tissues. Based on the DEGs, a weighted gene co-expression network analysis (WGCNA) was performed in 683 BC samples, and eight co-expressed gene modules were identified. In addition, by relating the eight co-expressed modules to clinical information, we found the blue module and pathological stage had a significant correlation (r = 0.24, p = 1e–10). Validated by multiple independent datasets, using one-way ANOVA, survival analysis and expression level revalidation, we finally screened 12 hub genes that can predict BC progression and prognosis. Functional annotation analysis indicated that the hub genes were enriched in cell division and cell cycle regulation. Importantly, higher expression of the 12 hub genes indicated poor overall survival, recurrence-free survival, and disease-free survival in BC patients. In addition, the expression of the 12 hub genes showed a significantly positive correlation with the expression of cell proliferation marker Ki-67 in BC. In summary, our study has identified 12 hub genes associated with the progression and prognosis of BC; these hub genes might lead to poor outcomes by regulating the cell division and cell cycle. These hub genes may serve as a biomarker and help to distinguish different pathological stages for BC patients.

• WGCNA
• breast cancer
• cell cycle
• prognosis
• progression

Gastric carcinoma (GC) is one of the most aggressive primary digestive cancers. It has unsatisfactory therapeutic outcomes and is difficult to diagnose early.

To identify prognostic biomarkers for GC patients using comprehensive bioinformatics analyses.

Differentially expressed genes (DEGs) were screened using gene expression data from The Cancer Genome Atlas and Gene Expression Omnibus databases for GC. Overlapping DEGs were analyzed using univariate and multivariate Cox regression analyses. A risk score model was then constructed and its prognostic value was validated utilizing an independent Gene Expression Omnibus dataset (GSE15459). Multiple databases were used to analyze each gene in the risk score model. High-risk score-associated pathways and therapeutic small molecule drugs were analyzed and predicted, respectively.

A total of 95 overlapping DEGs were found and a nine-gene signature (COL8A1, CTHRC1, COL5A2, AADAC, MAMDC2, SERPINE1, MAOA, COL1A2, and FNDC1) was constructed for the GC prognosis prediction. Receiver operating characteristic curve performance in the training dataset (The Cancer Genome Atlas-stomach adenocarcinoma) and validation dataset (GSE15459) demonstrated a robust prognostic value of the risk score model. Multiple database analyses for each gene provided evidence to further understand the nine-gene signature. Gene set enrichment analysis showed that the high-risk group was enriched in multiple cancer-related pathways. Moreover, several new small molecule drugs for potential treatment of GC were identified.

The nine-gene signature-derived risk score allows to predict GC prognosis and might prove useful for guiding therapeutic strategies for GC patients.

• Gastric carcinoma
• Bioinformatic analysis
• Prognosis
• Overall survival
• Differentially expressed genes

Cancer is a complex and heterogeneous disease with many possible genetic and environmental causes. The same treatment for patients of the same cancer type often results in different outcomes in terms of efficacy and side effects of the treatment. Thus, the molecular characterization of individual cancer patients is increasingly important to find an effective treatment. Recently a few methods have been developed to construct cancer sample-specific gene networks based on the difference in the mRNA expression levels between the cancer sample and reference samples.

We constructed a patient-specific network with multi-omics data based on the difference between a reference network and a perturbed reference network by the patient. A network specific to a group of patients was obtained using the average change in correlation coefficients and node degree of patient-specific networks of the group.

In this paper, we present a new method for constructing cancer patient-specific and group-specific gene networks with multi-omics data. The main differences of our method from previous ones are as follows: (1) networks are constructed with multi-omics (mRNA expression, copy number variation, DNA methylation and microRNA expression) data rather than with mRNA expression data alone, (2) background networks are constructed with both normal samples and cancer samples of the specified type to extract cancer-specific gene correlations, and (3) both patient individual-specific networks and patient group-specific networks can be constructed. The results of evaluating our method with several types of cancer show that it constructs more informative and accurate gene networks than previous methods.

The results of evaluating our method with extensive data of seven cancer types show that the difference of gene correlations between the reference samples and a patient sample is a more predictive feature than mRNA expression levels and that gene networks constructed with multi-omics data show a better performance than those with single omics data in predicting cancer for most cancer types. Our approach will be useful for finding genes and gene pairs to tailor treatments to individual characteristics.

• Cancer
• Group-specific gene network
• Individual-specific gene network
• Multi-omics data

• Emerging targets
• Gastroenteropancreatic neuroendocrine tumors
• Limitation
• Management
• Novel Agents
• Pancreatic neuroendocrine tumors

• Carcinoma, Pancreatic Ductal
• Humans
• Neuroendocrine Tumors/epidemiology
• Neuroendocrine Tumors/therapy
• Pancreas
• Pancreatic Neoplasms/epidemiology
• Pancreatic Neoplasms/therapy
• Quality of Life
• United States

• R01 CA240607 NCI NIH HHS
• T32 CA009531 NCI NIH HHS

The aim of this study was to discover new potential biomarkers of breast cancer and investigate their cellular functions.

We analysed the gene expression profiles of matched pairs of breast tumour and normal tissues from 24 breast cancer patients. Tetracycline‐inducible MAMDC2 expression system was established and used to evaluate cell proliferation in vitro and in vivo. MAMDC2‐mediated signalling was determined using immunoblot analysis.

We identified MAMDC2 as a down‐regulated gene showing significant prognostic capability. Overexpression of MAMDC2 or treatment with MAMDC2‐containing culture medium significantly inhibited the cell proliferation of T‐47D cells. Furthermore, MAMDC2 expression reduced in vivo growth of T‐47D xenograft tumours. MAMDC2 may exert its growth‐inhibitory functions by attenuating the MAPK signalling pathway.

We report that MAMDC2 has a tumour‐suppressive role and, as a secretory protein, it might be useful as a biomarker for breast cancer treatment.

• biomarkers
• breast cancer
• cell proliferation
• tumour suppressor

• Ductal carcinoma in situ
• Oncomine analysis
• breast cancer
• differentially expressed genes
• invasive ductal carcinoma
• metastasis
• recurrence

• Angiotensin II
• Autophagy
• Bioinformatics analysis
• Cardiomyocytes
• NEDD4

• Angiotensin II/pharmacology
• Animals
• Animals, Newborn
• Autophagy/drug effects
• Gene Expression Profiling
• Gene Ontology
• Microarray Analysis/methods
• Myocytes, Cardiac/cytology
• Myocytes, Cardiac/drug effects
• Myocytes, Cardiac/metabolism
• Myocytes, Cardiac/ultrastructure
• Nedd4 Ubiquitin Protein Ligases/metabolism
• Protein Interaction Maps/drug effects
• Rats, Sprague-Dawley
• Signal Transduction/drug effects

• National Natural Science Foundation of China
• Project of the Science and Technology Committee of Shanghai
• Science of Foundation of Shanghai Municipal Health Planning Commission

Breast cancer is one of the most common malignancies. The molecular mechanisms of its pathogenesis are still to be investigated. The aim of this study was to identify the potential genes associated with the progression of breast cancer. Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify candidate biomarkers. The gene expression profiles of GSE1561 were selected from the Gene Expression Omnibus (GEO) database. RNA-seq data and clinical information of breast cancer from TCGA were used for validation. A total of 18 modules were identified via the average linkage hierarchical clustering. In the significant module (R² = 0.48), 42 network hub genes were identified. Based on the Cancer Genome Atlas (TCGA) data, 5 hub genes (CCNB2, FBXO5, KIF4A, MCM10, and TPX2) were correlated with poor prognosis. Receiver operating characteristic (ROC) curve validated that the mRNA levels of these 5 genes exhibited excellent diagnostic efficiency for normal and tumor tissues. In addition, the protein levels of these 5 genes were also significantly higher in tumor tissues compared with normal tissues. Among them, CCNB2, KIF4A, and TPX2 were further upregulated in advanced tumor stage. In conclusion, 5 candidate biomarkers were identified for further basic and clinical research on breast cancer with co-expression network analysis.

• GEO
• TCGA
• breast cancer
• prognosis
• weighted gene co-expression network analysis (WGCNA)

• bioinformatics analysis
• differentially expressed genes
• invasive ductal carcinoma

• Lymphoma and Hodgkin disease
• cell cycle and apoptosis changes
• drug resistance
• lymphocytes
• molecular genetics

• HER2
• breast cancer
• in silico
• microRNA
• receptors of estrogen and progesterone

• Breast cancer
• Complex neural network
• Computational intelligence
• Medical computing
• Medical diagnosis
• SOM

Claudin-11 (CLDN11) is frequently silenced by its promoter hypermethylation. Previous studies have shown that CLDN11 promoter hypermethylation is a potential biomarker for diagnosing various cancers. The aim of this study was to investigate CLDN11 promoter methylation and its potential relevance to clinicopathologic features and prognosis of patients with laryngeal squamous cell carcinoma (LSCC).

Using the quantitative methylation-specific polymerase chain reaction (qMSP), CLDN11 promoter methylation was measured in 91 tumor tissues and their paired adjacent normal tissues, and the relationship between CLDN11 methylation and clinicopathologic features was evaluated. A receiver operating characteristic (ROC) curve was created to assess diagnostic values, and the Kaplan-Meier survival analysis was used to evaluate the association between CLDN11 methylation and prognosis of patients with LSCC.

Our results showed significantly elevated promoter methylation of CLDN11 in tumor tissues compared to their adjacent tissues (p=1.227E-16). CLDN11 promoter methylation also increased in patients with lymph node metastasis (p=0.009), advanced clinical stage (p=9.26E-06) and higher T classification (p=0.003). The area under the ROC curve (AUC) of CLDN11 was 0.884 (95% CI=0.835–0.932, p<0.01). The Kaplan-Meier analysis indicated that high CLDN11 promoter methylation levels were associated with poor overall survival of LSCC patients (log-rank test, p=0.007).

We demonstrated that CLDN11 promoter hypermethylation is a frequent event in LSCC, and contributes to metastasis and progression of LSCC. Thus, CLDN11 could be a potential biomarker for diagnosis and prognosis of LSCC patients.

• cardiovascular disease
• meta-analysis
• neurogenic hypertension
• subnetwork enrichment
• transcriptomics

• Biomarkers
• Breast cancer
• Database