A network-based signature to predict the survival of non-smoking lung adenocarcinoma

Identification of hub genes in bladder transitional cell carcinoma through ceRNA network construction integrated with gene network analysis

Bladder transitional cell carcinoma (BTCC) forms more than 90% of bladder cancer cases. It brings challenges to the early diagnosis and therapy of BTCC, due to lack of efficient screening biomarkers. We used weighted gene co-expression network analysis (WGCNA) combined competing endogenous RNA (ceRNA) network construction depending on TCGA datasets to investigate potential hub genes and regulatory pathways associated with occurrence and progression of BTCC. We further used real-time polymerase chain reaction (RT-PCR) to validate the relative expression genes correlated with BTCC. By WGCNA, the gene co-expression module with 11 genes was found corelated with BTCC tumour stage and prognosis after survival analyses. Ultimately, we put 100 highly stage-related genes into the above constructed ceRNA network and then constructed another new network. Among them, all elements in AC112721.1/LINC00473/AC128709.1-hsa-mir-195-RECK and LINC00460-hsa-mir-429-ZFPM2 axes were simultaneously corelated with overall survival. RT-PCR showed that AKAP12 was downregulated in tumour tissues. The hub genes screened out in the present study may provide ideals for further treatment on BTCC.

A Functional Genomics Review of Non-Small-Cell Lung Cancer in Never Smokers

There is currently a dearth of information regarding lung cancer in never smokers (LCINS). Additionally, there is a difference in somatic mutations, tumour mutational burden, and chromosomal aberrations between smokers and never smokers (NS), insinuating a different disease entity in LCINS. A better understanding of actionable driver alterations prevalent in LCINS and the genomic landscape will contribute to identifying new molecular targets of relevance for NS that will drastically improve outcomes. Differences in treatment outcomes between NS and smokers, as well as sexes, with NSCLC suggest unique tumour characteristics. Epidermal growth factor receptor (EGFR) tyrosine kinase mutations and echinoderm microtubule-associated protein-like 4 anaplastic lymphoma kinase (EML4-ALK) gene rearrangements are more common in NS and have been associated with chemotherapy resistance. Moreover, NS are less likely to benefit from immune mediators including PD-L1. Unravelling the genomic and epigenomic underpinnings of LCINS will aid in the development of not only novel targeted therapies but also more refined approaches. This review encompasses driver genes and pathways involved in the pathogenesis of LCINS and a deeper exploration of the genomic landscape and tumour microenvironment. We highlight the dire need to define the genetic and environmental aspects entailing the development of lung cancer in NS.

A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome-Interactome Signature for Predicting Non-Small Cell Lung Cancer

The lack of precise molecular signatures limits the early diagnosis of non-small cell lung cancer (NSCLC). The present study used gene expression data and interaction networks to develop a highly accurate model with the least absolute shrinkage and selection operator (LASSO) for predicting NSCLC. The differentially expressed genes (DEGs) were identified in NSCLC compared with normal tissues using TCGA and GTEx data. A biological network was constructed using DEGs, and the top 20 upregulated and 20 downregulated hub genes were identified. These hub genes were used to identify signature genes with penalized logistic regression using the LASSO to predict NSCLC. Our model’s development involved the following steps: (i) the dataset was divided into 80% for training (TR) and 20% for testing (TD1); (ii) a LASSO logistic regression analysis was performed on the TR with 10-fold cross-validation and identified a combination of 17 genes as NSCLC predictors, which were used further for development of the LASSO model. The model’s performance was assessed on the TD1 dataset and achieved an accuracy and an area under the curve of the receiver operating characteristics (AUC-ROC) of 0.986 and 0.998, respectively. Furthermore, the performance of the LASSO model was evaluated using three independent NSCLC test datasets (GSE18842, GSE27262, GSE19804) and achieved high accuracy, with an AUC-ROC of >0.99, >0.99, and 0.95, respectively. Based on this study, a web application called NSCLCpred was developed to predict NSCLC.

Identification of collagen genes related to immune infiltration and epithelial-mesenchymal transition in glioma

Background

Gliomas account for the majority of fatal primary brain tumors, and there is much room for research in the underlying pathogenesis, the multistep progression of glioma, and how to improve survival. In our study, we aimed to identify potential biomarkers or therapeutic targets of glioma and study the mechanism underlying the tumor progression.

Methods

We downloaded the microarray datasets (GSE43378 and GSE7696) from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers or therapeutic targets related to the tumor progression. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm and TIMER (Tumor Immune Estimation Resource) database were used to analyze the correlation between the selected genes and the tumor microenvironment. Real-time reverse transcription polymerase chain reaction was used to measure the selected gene. Transwell and wound healing assays were used to measure the cell migration and invasion capacity. Western blotting was used to test the expression of epithelial-mesenchymal transition (EMT) related markers.

Results

We identified specific module genes that were positively correlated with the WHO grade but negatively correlated with OS of glioma. Importantly, we identified that 6 collagen genes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2) could regulate the immunosuppressive microenvironment of glioma. Moreover, we found that these collagen genes were significantly involved in the EMT process of glioma. Finally, taking COL3A1 as a further research object, the results showed that knockdown of COL3A1 significantly inhibited the migration, invasion, and EMT process of SHG44 and A172 cells.

Conclusions

In summary, our study demonstrated that collagen genes play an important role in regulating the immunosuppressive microenvironment and EMT process of glioma and could serve as potential therapeutic targets for glioma management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01982-0.

Identification of unique long non-coding RNAs as putative biomarkers for chromophobe renal cell carcinoma

Aim: To investigate whether long non-coding RNAs (lncRNAs) can be utilized as molecular biomarkers in predicting the occurrence and progression of chromophobe renal cell carcinoma. Methods & results: Genetic and related clinical traits of chromophobe renal cell carcinoma were downloaded from the Cancer Genome Atlas and used to construct modules using weighted gene coexpression network analysis. In total, 44,889 genes were allocated into 21 coexpression modules depending on intergenic correlation. Among them, the green module was the most significant key module identified by module-trait correlation calculations (R² = 0.43 and p = 4e-04). Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses demonstrated that genes in the green module were enriched in many pathways. Coexpression, protein-protein interaction networks, screening for differentially expressed genes, and survival analysis were used to select hub lncRNAs. Five hub lncRNAs (TTK, CENPE, KIF2C, BUB1, and RAD51AP1) were selected out. Conclusion: Our findings suggest that the five lncRNAs may act as potential biomarkers for chromophobe renal cell carcinoma progression and prognosis.

CYP3A5 suppresses metastasis of lung adenocarcinoma through ATOH8/Smad1 axis

Cytochrome P450 3A5 (CYP3A5) maintains primary roles in toxic metabolism, catalyzes redox reaction, and contributes to chemotherapeutic resistance. However, the mechanism of CYP3A5 in carcinogenesis remains largely undefined. Here, we investigated a novel role of CYP3A5 inhibiting the metastasis in lung adenocarcinoma (LUAD) via ATOH8/Smad1 axis. We found that CYP3A5 was generally down-regulated in LUAD by RT-PCR, western blot and immunohistochmeistry (IHC) in tissues and cell lines. Low expression of CYP3A5 was significantly associated with poor prognosis of LUAD patients. Functionally, ectopic expression of CYP3A5 could substantially inhibit the migration and invasion in vitro. Consistently, up-regulation of CYP3A5 dramatically suppressed metastatic ability in vivo. Mechanistically, high-throughput phosphorylation chip indicated that CYP3A5 significantly decreased the phosphorylation of Smad1, resulting in suppression of metastasis. Furthermore, bioinformatics analysis and co-immunoprecipitation (Co-IP) experiments uncovered that CYP3A5 interacted with ATOH8, and the interaction, in turn, mediated in-activation in the Smad1 pathway. The combined IHC panel, including CYP3A5 and phosphorylation of Smad1, exhibited a better prognostic value for LUAD patients than any of these components individually. Taken together, CYP3A5 repressed activation of Smad1 to inhibit LUAD metastasis via interacting with ATOH8, indicating a novel potential mechanism of CYP3A5 in LUAD progression.

Survival-related risk score of lung adenocarcinoma identified by weight gene co-expression network analysis

The present study aimed to identify the novel biomarkers and underlying molecular mechanisms of lung adenocarcinoma (LAC) to aid in its diagnosis, prognosis, prediction, disease monitoring and emerging therapies. Data from a total of 498 LAC samples were collected from The Cancer Genome Atlas and divided into two sets by stratified randomization based on pathological Tumor-Node-Metastasis stage. The training set was comprised of 348 samples and the validation set was comprised of 150 samples. A total of 123 samples from the training set for patients who completed follow-up were analyzed by weighted gene co-expression network analysis. A module was identified that contained 113 protein-coding genes that were positively associated with overall survival (OS). A least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed and four survival-associated genes (OPN3, GALNT2, FAM83A and KYNU) were retained. Risk score, calculated by the linear combination of each gene expression multiplied by the LASSO coefficient, could successfully discriminate between patients with LAC exhibiting low and high OS time in both sets. The results from the present study indicate that this risk score may contribute to potential diagnostic and therapeutic strategies for LAC management.

Identification of biomarkers of chromophobe renal cell carcinoma by weighted gene co-expression network analysis

Background

Chromophobe renal cell carcinoma (ChRCC) is the second common subtype of non-clear cell renal cell carcinoma (nccRCC), which accounting for 4–5% of renal cell carcinoma (RCC). However, there is no effective bio-marker to predict clinical outcomes of this malignant disease. Bioinformatic methods may provide a feasible potential to solve this problem.

Methods

In this study, differentially expressed genes (DEGs) of ChRCC samples on The Cancer Genome Atlas database were filtered out to construct co-expression modules by weighted gene co-expression network analysis and the key module were identified by calculating module-trait correlations. Functional analysis was performed on the key module and candidate hub genes were screened out by co-expression and MCODE analysis. Afterwards, real hub genes were filter out in an independent dataset GSE15641 and validated by survival analysis.

Results

Overall 2215 DEGs were screened out to construct eight co-expression modules. Brown module was identified as the key module for the highest correlations with pathologic stage, neoplasm status and survival status. 29 candidate hub genes were identified. GO and KEGG analysis demonstrated most candidate genes were enriched in mitotic cell cycle. Three real hub genes (SKA1, ERCC6L, GTSE-1) were selected out after mapping candidate genes to GSE15641 and two of them (SKA1, ERCC6L) were significantly related to overall survivals of ChRCC patients.

Conclusions

In summary, our findings identified molecular markers correlated with progression and prognosis of ChRCC, which might provide new implications for improving risk evaluation, therapeutic intervention, and prognosis prediction in ChRCC patients.

Electronic supplementary material

The online version of this article (10.1186/s12935-018-0703-z) contains supplementary material, which is available to authorized users.