The inhibitory effects of COL1A2 on colorectal cancer cell proliferation, migration, and invasion

Identification and validation of key genes in gastric cancer: insights from in silico analysis, clinical samples, and functional assays

INTRODUCTION

The underlying mechanisms of gastric cancer (GC) remain unknown. Therefore, in this study, we employed a comprehensive approach, combining computational and experimental methods, to identify potential key genes and unveil the underlying pathogenesis and prognosis of GC.

METHODS

Gene expression profiles from GEO databases (GSE118916, GSE79973, and GSE29272) were analyzed to identify DEGs between GC and normal tissues. A PPI network was constructed using STRING and Cytoscape, followed by hub gene identification with CytoHubba. Investigations included expression and promoter methylation analysis, survival modeling, mutational and miRNA analysis, gene enrichment, drug prediction, and in vitro assays for cellular behaviors.

RESULTS

A total of 83 DEGs were identified in the three datasets, comprising 41 up-regulated genes and 42 down-regulated genes. Utilizing the degree and MCC methods, we identified four hub genes that were hypomethylated and up-regulated: COL1A1, COL1A2, COL3A1, and FN1. Subsequent validation of their expression and promoter methylation on clinical GC samples through targeted bisulfite sequencing and RT-qPCR analysis further confirmed the hypomethylation and overexpression of these genes in local GC patients. Furthermore, it was observed that these hub genes regulate tumor proliferation and metastasis in in vivo and exhibited mutations in GC patients.

CONCLUSION

We found four potential diagnostic and prognostic biomarkers, including COL1A1, COL1A2, COL3A1, and FN1 that may be involved in the occurrence and progression of GC.

Identification of CAF-related lncRNAs at the pan-cancer level represents a potential carcinogenic risk

Cancer-associated fibroblasts (CAFs) are increasingly recognized as playing a crucial role in regulating cancer progression and metastasis. These cells can be activated by long non-coding RNAs (lncRNAs), promoting the malignant biological processes of tumor cells. Therefore, it is essential to understand the regulatory relationship between CAFs and lncRNAs in cancers. Here, we identified CAF-related lncRNAs at the pan-cancer level to systematically predict their potential regulatory functions. The identified lncRNAs were also validated using various external data at both tissue and cellular levels. This study has revealed that these CAF-related lncRNAs exhibit expression perturbations in cancers and are highly correlated with the infiltration of stromal cells, particularly fibroblasts and endothelial cells. By prioritizing a list of CAF-related lncRNAs, we can further distinguish patient subtypes that show survival and molecular differences. In addition, we have developed a web server, CAFLnc (https://46906u5t63.zicp.fun/CAFLnc/), to visualize our results. In conclusion, CAF-related lncRNAs hold great potential as a valuable resource for comprehending lncRNA functions and advancing the identification of biomarkers for cancer progression and therapeutic targets in cancer treatment.

Unveiling Collagen's Role in Breast Cancer: Insights into Expression Patterns, Functions and Clinical Implications

Collagen, the predominant protein constituent of the mammalian extracellular matrix (ECM), comprises a diverse family of 28 members (I-XXVIII). Beyond its structural significance, collagen is implicated in various diseases or cancers, notably breast cancer, where it influences crucial cellular processes including proliferation, metastasis, apoptosis, and drug resistance, intricately shaping cancer progression and prognosis. In breast cancer, distinct collagens exhibit differential expression profiles, with some showing heightened or diminished levels in cancerous tissues or cells compared to normal counterparts, suggesting specific and pivotal biological functions. In this review, we meticulously analyze the expression of individual collagen members in breast cancer, utilizing Transcripts Per Million (TPM) data sourced from the GEPIA2 database. Through this analysis, we identify collagens that deviate from normal expression patterns in breast cancer, providing a comprehensive overview of their expression dynamics, functional roles, and underlying mechanisms. Our findings shed light on recent advancements in understanding the intricate interplay between these aberrantly expressed collagens and breast cancer. This exploration aims to offer valuable insights for the identification of potential biomarkers and therapeutic targets, thereby advancing the prospects of more effective interventions in breast cancer treatment.

Mapping the topography of spatial gene expression with interpretable deep learning

Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a novel quantity called the isodepth. Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.

ceRNA network-regulated COL1A2 high expression correlates with poor prognosis and immune infiltration in colon adenocarcinoma

Collagen type I α 2 (COL1A2) is a major component of collagen type I. Recently, abnormal COL1A2 expression has been reported in human cancers. However, the specific role and mechanism of COL1A2 in colon adenocarcinoma (COAD) remain unclear. We performed the pan-cancer analysis of COL1A2 expression in 33 types of human cancers from TIMER database and integrated data combined TCGA with GTEx. The prognostic values of COL1A2 for 17 cancer types of interest were estimated from GEPIA database. The results showed that COL1A2 was significantly upregulated in COAD tissues and that higher COL1A2 expression predicted unfavorable prognosis for patients with COAD. Next, COL1A2-related functional pathways in COAD were analyzed with TCGA data using R package. Additionally, we constructed a ceRNA network that LINC00638/hsa-miR-552-3p axis served as a potential regulatory pathway of COL1A2 in COAD. Furthermore, our findings showed that COL1A2 positively associated with immune infiltration and that tumor immune escape might be involved in COL1A2-mediated carcinogenesis in COAD. For the first time, we constructed a ceRNA prediction network of COL1A2 and explored the association of COL1A2 with tumor immune microenvironment remodeling. The findings may advance our understanding of the pathogenesis mechanism in COAD and paves the way for further cancer therapeutics.

Screening of the shared pathogenic genes of ulcerative colitis and colorectal cancer by integrated bioinformatics analysis

Ulcerative colitis (UC) is one of the high-risk pathogenic factors for colorectal cancer (CRC). However, the shared gene and signalling mechanisms between UC and CRC remain unclear. The goal of this study was to delve more into the probable causal relationship between UC and CRC. CRC and UC datasets were downloaded from the Gene Expression Omnibus database. Using R software and Perl, differentially expressed genes (DEGs) in both UC and CRC tissues were re-annotated and screened. The biological activities and signalling pathways involved in DEGs were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The STRING database and Cytoscape software were used to construct the gene interaction network. A total of 384 DEGs were selected for further investigation, and functional analysis revealed that inflammatory and immunological responses were crucial in the development of the two diseases. Moreover, the top 15 key genes involved in the UC and CRC were screened using cytoHubba, including IL1B, CXCL10, CCL20, MMP9, ICAM1, CCL4, CXCR1, MMP3, TLR2, PTGS2, IL1RN, IL6, COL1A2, TIMP1 and CXCL1. The identification of these genes in the present study may provide a novel perspective for the prediction, prevention and personalized medicine of UC and CRC patients.

A protein microarray-based serum proteomic investigation reveals distinct autoantibody signature in colorectal cancer

PURPOSE

Colorectal cancer (CRC) has been reported as the second leading cause of cancer death worldwide. The 5-year annual survival is around 50%, mainly due to late diagnosis, striking necessity for early detection. This study aims to identify autoantibody in patients' sera for early screening of cancer.

EXPERIMENTAL DESIGN

The study used a high-density human proteome array with approximately 17,000 recombinant proteins. Screening of sera from healthy individuals, CRC from Indian origin, and CRC from middle-east Asia origin were performed. Bio-statistical analysis was performed to identify significant autoantibodies altered. Pathway analysis was performed to explore the underlying mechanism of the disease.

RESULTS

The comprehensive proteomic analysis revealed dysregulation of 15 panels of proteins including CORO7, KCNAB1, WRAP53, NDUFS6, KRT30, and COLGALT2. Further biological pathway analysis for the top dysregulated autoantigenic proteins revealed perturbation in important biological pathways such as ECM degradation and cytoskeletal remodeling etc. CONCLUSIONS AND CLINICAL RELEVANCE: The generation of an autoimmune response against cancer-linked pathways could be linked to the screening of the disease. The process of immune surveillance can be detected at an early stage of cancer. Moreover, AAbs can be easily extracted from blood serum through the least invasive test for disease screening.

Profiling of Tumor Cell-Delivered Exosome by Surface Enhanced Raman Spectroscopy-Based Biosensor for Evaluation of Nasopharyngeal Cancer Radioresistance

Although the advancement of radiotherapy significantly improves the survival of nasopharyngeal cancer (NPC), radioresistance associated with recurrence and poor outcomes still remains a daunting challenge in the clinical scenario. Currently, effective biomarkers and convenient detection methods for predicting radioresistance have not been well established. Here, the surface-enhanced Raman spectroscopy combined with proteomics is used to firstly profile the characteristic spectral patterns of exosomes secreted from self-established NPC radioresistance cells, and reveals specific variations of proteins expression during radioresistance formation, including collagen alpha-2 (I) chain (COL1A2) that is associated with a favorable prognosis in NPC and is negatively associated with DNA repair scores and DNA repair-related genes via bioinformatic analysis. Furthermore, deep learning model-based diagnostic model is generated to accurately identify the exosomes from radioresistance group. This work demonstrates the promising potential of exosomes as a novel biomarker for predicting the radioresistance and develops a rapid and sensitive liquid biopsy method that will provide a personalized and precise strategy for clinical NPC treatment.

COL1A2 inhibition suppresses glioblastoma cell proliferation and invasion

OBJECTIVE

An extracellular matrix such as collagen is an essential component of the tumor microenvironment. Collagen alpha-2(I) chain (COL1A2) is a chain of type I collagen whose triple helix comprises two alpha-1 chains and one alpha-2 chain. The authors' proteomics data showed that COL1A2 is significantly higher in the blood of patients with glioblastoma (GBM) compared with healthy controls. COL1A2 has many different functions in various types of cancers. However, the functions of COL1A2 in GBM are poorly understood. In this study, the authors analyzed the functions of COL1A2 and its signaling pathways in GBM.

METHODS

Surgical specimens and GBM cell lines (T98, U87, and U251) were used. The expression level of COL1A2 was examined using GBM tissues and normal brain tissues by quantitative real-time polymerase chain reaction. The clinical significance of these levels was evaluated using Kaplan-Meier analysis. Small interfering RNA (siRNA) and small hairpin RNA of COL1A2 were transfected into GBM cell lines to investigate the function of COL1A2 in vitro and in vivo. Flow cytometry was introduced to analyze the alteration of cell cycles. Western blot and immunohistochemistry were performed to analyze the underlying mechanisms.

RESULTS

The expression level of COL1A2 was upregulated in GBM compared with normal brain tissues. A higher expression of COL1A2 was correlated with poor progression-free survival and overall survival. COL1A2 inhibition significantly suppressed cell proliferation in vitro and in vivo, likely due to G1 arrest. The invasion ability was notably deteriorated by inhibiting COL1A2. Cyclin D1, cyclin-dependent kinase 1, and cyclin-dependent kinase 4, which are involved in the cell cycle, were all downregulated after blockade of COL1A2 in vitro and in vivo. Phosphoinositide 3-kinase inhibitor reduced the expression of COL1A2. Although downregulation of COL1A2 decreased the protein kinase B (Akt) phosphorylation, Akt activator can phosphorylate Akt in siRNA-treated cells. This finding suggests that Akt phosphorylation is partially dependent on COL1A2.

CONCLUSIONS

COL1A2 plays an important role in driving GBM progression. COL1A2 inhibition attenuated GBM proliferation by promoting cell cycle arrest, indicating that COL1A2 could be a promising therapeutic target for GBM treatment.

Identification of gene profiles related to the development of oral cancer using a deep learning technique

BACKGROUND

Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them may significantly improve patient therapy and care. This study aimed to identify prognostic biomarkers that predict the time-to-development of OC and survival stratification for patients using state-of-the-art machine learning and deep learning.

METHODS

Gene expression profiles (29,096 probes) related to 86 patients from the GSE26549 dataset from the GEO repository were used. An autoencoder deep learning neural network model was used to extract features. We also used a univariate Cox regression model to select significant features obtained from the deep learning method (P < 0.05). High-risk and low-risk groups were then identified using a hierarchical clustering technique based on 100 encoded features (the number of units of the encoding layer, i.e., bottleneck of the network) from autoencoder and selected by Cox proportional hazards model and a supervised random forest (RF) classifier was used to identify gene profiles related to subtypes of OC from the original 29,096 probes.

RESULTS

Among 100 encoded features extracted by autoencoder, seventy features were significantly related to time-to-OC-development, based on the univariate Cox model, which was used as the inputs for the clustering of patients. Two survival risk groups were identified (P value of log-rank test = 0.003) and were used as the labels for supervised classification. The overall accuracy of the RF classifier was 0.916 over the test set, yielded 21 top genes (FUT8-DDR2-ATM-CD247-ETS1-ZEB2-COL5A2-GMAP7-CDH1-COL11A2-COL3A1-AHR-COL2A1-CHORDC1-PTP4A3-COL1A2-CCR2-PDGFRB-COL1A1-FERMT2-PIK3CB) associated with time to developing OC, selected among the original 29,096 probes.

CONCLUSIONS

Using deep learning, our study identified prominent transcriptional biomarkers in determining high-risk patients for developing oral cancer, which may be prognostic as significant targets for OC therapy. The identified genes may serve as potential targets for oral cancer chemoprevention. Additional validation of these biomarkers in experimental prospective and retrospective studies will launch them in OC clinics.

Development of a TGF-β signaling-related genes signature to predict clinical prognosis and immunotherapy responses in clear cell renal cell carcinoma

Background

Transforming growth factor (TGF)-β signaling is strongly related to the development and progression of tumor. We aimed to construct a prognostic gene signature based on TGF-β signaling-related genes for predicting clinical prognosis and immunotherapy responses of patients with clear cell renal cell carcinoma (ccRCC).

Methods

The gene expression profiles and corresponding clinical information of ccRCC were collected from the TCGA and the ArrayExpress (E-MTAB-1980) databases. LASSO, univariate and multivariate Cox regression analyses were conducted to construct a prognostic signature in the TCGA cohort. The E-MTAB-1980 cohort were used for validation. Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (ROC) were conducted to assess effectiveness and reliability of the signature. The differences in gene enrichments, immune cell infiltration, and expression of immune checkpoints in ccRCC patients showing different risks were investigated.

Results

We constructed a seven gene (PML, CDKN2B, COL1A2, CHRDL1, HPGD, CGN and TGFBR3) signature, which divided the ccRCC patients into high risk group and low risk group. The K-M analysis indicated that patients in the high risk group had a significantly shorter overall survival (OS) time than that in the low risk group in the TCGA (p < 0.001) and E-MTAB-1980 (p = 0.012). The AUC of the signature reached 0.77 at 1 year, 0.7 at 3 years, and 0.71 at 5 years in the TCGA, respectively, and reached 0.69 at 1 year, 0.72 at 3 years, and 0.75 at 5 years in the E-MTAB-1980, respectively. Further analyses confirmed the risk score as an independent prognostic factor for ccRCC (p < 0.001). The results of ssGSEA that immune cell infiltration degree and the scores of immune-related functions were significantly increased in the high risk group. The CIBERSORT analysis indicated that the abundance of immune cell were significantly different between two risk groups. Furthermore, The risk score was positively related to the expression of PD-1, CTLA4 and LAG3.These results indicated that patients in the high risk group benefit more from immunotherapy.

Conclusion

We constructed a novel TGF-β signaling-related genes signature that could serve as an promising independent factor for predicting clinical prognosis and immunotherapy responses in ccRCC patients.

Identification of Potential Diagnostic and Prognostic Biomarkers for Gastric Cancer Based on Bioinformatic Analysis

Gastric cancer (GC) ranks third for cancer-related fatalities worldwide. It is still unclear what causes GC to progress. Using integrated bioinformatics analysis, COL5A2 has been proved to be related to GC development, which may identify the likely pathogenic mechanism. Data from GC patients were gathered using The Cancer Gene Atlas (TCGA) and the gene expression omnibus (GEO). The level of COL5A2 expression was compared between paired GC and normal tissues. The differentially expressed genes (DEGs) in GC patients with high and low COL5A2 expression were identified using functional enrichment analysis to identify the signature pathways linked to the DEGs. The clinical pathologic traits connected to overall survival (OS) of GC patients were examined utilizing Cox regression and the Kaplan-Meier method. To assess the prognostic significance of COL5A2, receiver operating characteristic (ROC) curves was drawn. How the immune system infiltrate both normal gastric and GC tumor tissues was investigated. Using the human protein atlas (HPA) database, regression, and the Kaplan-Meier method, immunohistochemical analysis of DEG COL5A2 expression in GC tissues was carried out. The correlation between COL5A2 expression and the GC grouping was found to be highly significant. Functional annotations revealed that COL5A2 participates in extracellular matrix structure, collagen metabolism, and other biological processes (BPs). High COL5A2 expression was associated with poor prognostic and clinical features, such as clinical T, N, and M stages. ROC curves exhibited that COL5A2 might predict the occurrence of gastric cancer. The infiltration degree of 21 immune cell subsets, including activated dendritic cells (aDCs), CD8+ T cells, and cytotoxic cells, was found to be dramatically relevant to COL5A2. Immunohistochemical analysis indicated that the expression of COL5A2 in tumor tissues is higher than that in normal tissues. The COL5A2 gene may offer fresh perspectives on the pathogenic mechanism underlying GC, as well as potential biomarkers for estimating GC patient prognosis. As a result, COL5A2 may be a useful biomarker for predicting patient survival.

The Expression Pattern of Adhesion G Protein-Coupled Receptor F5 Is Related to Cell Adhesion and Metastatic Pathways in Colorectal Cancer-Comprehensive Study Based on In Silico Analysis

Adhesion G protein-coupled receptor F5 (ADGRF5) is involved inthe neoplastic transformation of some cancer types. However, the significance of ADGRF5 expression signature and the impact of signaling pathways mediated by ADGRF5 during neoplastic transformation of the colon and colorectal cancer (CRC) progression has been poorly examined. Using Gene Expression Omnibus and The Cancer Genome Atlas datasets, we showed that ADGRF5 is overexpressed in the colons of patients with CRC. In line, combined analysis of ADGRF5 expression with clinical characterization revealed an increased expression of ADGRF5 in patients with more advanced stages of CRC compared to patients with early stages of CRC. The Spearman correlation analysis documented numerous genes positively and negatively correlated with the expression pattern of ADGRF5 in the colon of patients with CRC. In the colon of CRC patients, the expression signature of ADGRF5 was associated with genes participating in phosphatidylinositol 3-kinase/Akt, focal adhesion, cell adhesion molecules, and ribosome signaling pathways. Of note, ADGRF5 expression correlated with the levels of tumor-infiltrating immune cells in the colon of CRC patients. Moreover, we found that CRC patients with high expression of ADGRF5 had a significantly lower probability of overall survival and disease-free survival. In conclusion, our results support the prognostic value of ADGRF5 and its potent therapeutic implication in CRC.

Policosanol suppresses tumor progression in a gastric cancer xenograft model

Gastric cancer (GC) is the most common cancer worldwide and the third leading cause of cancer death, with the fifth highest incidence. The development of effective chemotherapeutic agents is needed to decrease GC mortality. Policosanol (PC) extracted from Cuban sugar cane wax is a healthy functional food ingredient that helps improve blood cholesterol levels and blood pressure. Its various physiological activities, such as antioxidant, anti-inflammatory, and anticancer activities, have been reported recently. Nevertheless, the therapeutic efficacy of PC in gastric xenograft models is unclear. We aimed to investigate the anticancer effect of PC on human GC SNU-16 cells and a xenograft mouse model. PC significantly inhibited GC cell viability and delayed tumor growth without toxicity in the SNU-16-derived xenograft model. Therefore, we investigated protein expression levels in tumor tissues; the expression levels of Ki-67, a proliferation marker, and cdc2 were decreased. In addition, we performed proteomic analysis and found thirteen differentially expressed proteins. Our results suggested that PC inhibited GC progression via cdc2 suppression and extracellular matrix protein regulation. Notably, our findings might contribute to the development of novel and effective therapeutic strategies for GC.

Low expression of the metabolism-related gene SLC25A21 predicts unfavourable prognosis in patients with acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a heterogeneous disease associated with poor outcomes. To identify AML-specific genes with prognostic value, we analysed transcriptome and clinical information from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) datasets, and Genotype-Tissue Expression (GTEx) project. The metabolism-related gene, SLC25A21 was found to be significantly downregulated in AML, and was associated with high white blood cell (WBC) counts, high pretrial blood (PB) and bone marrow (BM) blast abundance, FLT3 mutation, NPM1 mutation, and death events (all p value <0.05). We validated the expression of SLC25A21 in our clinical cohort, and found that SLC25A21 was downregulated in AML. Moreover, we identified low expression of SLC25A21 as an independent prognostic factor by univariate Cox regression (hazard ratio [HR]: 0.550; 95% Confidence interval [CI]: 0.358–0.845; p value = 0.006) and multivariate Cox regression analysis (HR: 0.341; 95% CI: 0.209–0.557; p value <0.05). A survival prediction nomogram was established with a C-index of 0.735, which indicated reliable prognostic prediction. Subsequently, based on the median SLC25A21 expression level, patients in the TCGA-LAML cohort were divided into low- and high-expression groups. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs highlighted growth factor binding, extracellular structure organization, cytokine‒cytokine receptor interaction, etc. The results of gene set enrichment analysis (GSEA) indicated that the epithelial-mesenchymal transition, KRAS signalling, oxidative phosphorylation, and reactive oxygen species pathways were enriched. Through gene coexpression and protein‒protein interaction (PPI) network analysis, we identified two hub genes, EGFR and COL1A2, which were linked to worse clinical outcomes. Furthermore, we found that lower SLC25A21 expression was closely associated with a significant reduction in the levels of infiltrating immune cells, which might be associated with immune escape of AML cells. A similar trend was observed for the expression of checkpoint genes (CTLA4, LAG3, TIGIT, and HAVCR2). Finally, drug sensitivity testing suggested that the low-expression SLC25A21 group is sensitive to doxorubicin, mitomycin C, linifanib but resistant to JQ1, belinostat, and dasatinib. Hence, our study demonstrated that a low expression level of SLC25A21 predicts an unfavourable prognosis in patients with AML.

MicroRNA Associated with the Invasive Phenotype in Clear Cell Renal Cell Carcinoma: Let-7c-5p Inhibits Proliferation, Migration, and Invasion by Targeting Insulin-like Growth Factor 1 Receptor

Differential microRNA (miRNA) expression can portend clear cell renal cell carcinoma (ccRCC) progression. In a previous study, we identified a subset of dysregulated miRNA in small renal masses, pT1 ccRCC (≤5 cm) that are associated with an aggressive phenotype. The present study investigated miRNA expression in clinical stage I (cT1) tumors (≤5 cm), comparing pathologic stage I (pT1) tumors to those upstaged to pathologic stage 3 (pT3) after surgery following identification of renal vein invasion or invasion into adjacent fat tissue within Gerota’s fascia. Twenty cT1 tumors were examined in an miRNA screening, 10 pT1 and 10 pT3 tumors. The ccRCC cell lines 786-O and Caki-1 were used to assess the impact of let-7c-5p and its protein target insulin-like growth factor 1 receptor (IGF1R). Cells were transfected with pre-let-7c-5p and assessed through cell proliferation, migration, and invasion assays. IGF1R expression was evaluated through Simple Western, and interaction between let-7c-5p and IGF1R was confirmed via luciferase reporter assay. Screening identified 20 miRNA, including let-7c-5p, that were dysregulated between pT1 and pT3 upstaged tumors. This miRNA was also downregulated in our previous study of pT1 tumors that progressed to metastatic disease. Transfection of ccRCC cells with pre-let-7c-5p significantly inhibited proliferation, migration, invasion, and IGF1R expression. These findings suggest that miRNA dysregulation is involved in ccRCC progression, specifically through invasion, and that let-7c-5p downregulation contributes to the aggressiveness of small ccRCC tumors, in part, through its regulation of IGF1R.

Evaluation of aliphatic acid metabolism in bladder cancer with the goal of guiding therapeutic treatment

Urothelial bladder cancer (BLCA) is a common internal malignancy with a poor prognosis. The re-programming of lipid metabolism is necessary for cancer cell growth, proliferation, angiogenesis and invasion. However, the role of aliphatic acid metabolism genes in bladder cancer patients has not been explored. The samples’ gene expression and clinicopathological data were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Univariate, multivariate, and LASSO Cox regression were used to develop a BLCA prognostic model. GSVA was used to assess function, whereas pRRophetic was used to assess chemotherapeutic drug sensitivity. The twelve-gene signature may define the tumor immune milieu, according to the risk score model. We compared the expression of aliphatic acid metabolism genes in malignant and non-cancerous tissues and chose 90 with a false discovery rate of 0.05 for The Cancer Genome Atlas cohort. The prognostic risk score model can effectively predict BLCA OS. A nomogram including age, clinical T stage, gender, grade, pathological stage, and clinical M stage was developed as an independent BLCA prognostic predictor. The halfmaximal inhibitory concentration (IC50) was used to assess chemotherapeutic medication response. Sorafenib and Pyrimethamine were used to treat patients with low risk scores more sensitively than patients with high risk scores. Immunotherapy candidates with CMS1 exhibited higher risk ratings. The aliphatic acid prognostic risk score model can assess metabolic trends. Clinical stage and molecular subtype may be used to categorize individuals using the risk score.With this new paradigm, future cancer treatment and immunotherapy may be tailored to the patient’s exact requirements.

To investigate the internal association between SARS-CoV-2 infections and cancer through bioinformatics

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), also known as COVID-19, is currently prevalent worldwide and poses a significant threat to human health. Individuals with cancer may have an elevated risk for SARS-CoV-2 infections and adverse outcomes. Therefore, it is necessary to explore the internal relationship between these two diseases. In this study, transcriptome analyses were performed to detect mutual pathways and molecular biomarkers in three types of common cancers of the breast, liver, colon, and COVID-19. Such analyses could offer a valuable understanding of the association between COVID-19 and cancer patients. In an analysis of RNA sequencing datasets for three types of cancers and COVID-19, we identified a sum of 38 common differentially expressed genes (DEGs). A variety of combinational statistical approaches and bioinformatics techniques were utilized to generate the protein-protein interaction (PPI) network. Subsequently, hub genes and critical modules were found using this network. In addition, a functional analysis was conducted using ontologies keywords, and pathway analysis was also performed. Some common associations between cancer and the risk and prognosis of COVID-19 were discovered. The datasets also revealed transcriptional factors-gene interplay, protein-drug interaction, and a DEGs-miRNAs coregulatory network with common DEGs. The potential medications discovered in this investigation could be useful in treating cancer and COVID-19.

Single-cell transcriptomics identifies Col1a1 and Col1a2 as hub genes in obesity-induced cardiac fibrosis

Obesity is a risk factor for cardiovascular disease, leading to ventricular dysfunction and cardiac fibrosis, in which non-cardiomyocytes (nonCMs) play an important role. Early detection and treatment of heart illness may help to limit its progression. We screened for key markers of obesity-induced cardiac fibrosis using single-cell transcriptomics techniques. To begin, an obese mouse model was constructed using a high-fat diet. From a pathogenic perspective, pathological alterations in the obesity-induced heart were found. Differentially expressed genes (DEGs) were identified and functional enrichment analysis was performed. Then, to look for hub genes, key modules of DEGs were built. Finally, the cellular location of the hub genes was investigated. In mice, a high-fat diet raised body weight, messed up myocardial shape, and increased cardiac collagen content. NonCMs transcriptome data revealed 15 different cell types, including fibroblasts, immunological cells, and endothelial cells. There were a total of 33 DEGs found, with 22 up-regulated genes and 11 down-regulated genes. DEGs have a high connection with collagen and extracellular matrix (ECM), according to functional enrichment analysis. Col1a1 and Col1a2 scored well in module analysis and hub gene screening, and were chosen as hub genes. Col1a1 and Col1a2 were shown to be mostly expressed by fibroblasts after localization study. As a result, we believe Col1a1 and Col1a2 may be important markers of obesity-induced cardiac fibrosis, in which fibroblasts play a critical role.

Multiple transcriptome analysis of Piwil2-induced cancer stem cells, including piRNAs, mRNAs and miRNAs reveals the mechanism of tumorigenesis and development

BACKGROUND

Cancer stem cells play important roles in the process of tumorigenesis. Our research group obtained cancer stem cell-like cells named Piwil2-iCSCs by reprogramming human preputial fibroblasts (FBs) with the PIWIL2 gene, but the mechanism of Piwil2-iCSCs is still unclear.

METHODS

We sequenced the piRNAs, miRNAs and mRNAs of Piwil2-iCSCs and FBs, and analyzed the differences. Gene Ontology (GO) and, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and gene set enrichment analysis (GSEA) were performed on the differentially expressed (DE) mRNAs. In addition, we analyzed the variable shear events and fusion genes in the Piwil2-iCSCs. Target gene prediction and functional enrichment analysis were performed for the DE miRNAs.

RESULTS

A total of 1119 DE mRNAs, 220 DE piRNAs, and 440 DE miRNAs were obtained between the Piwil2-iCSCs and FBs. Functional enrichment analysis showed that the genes with upregulated expression were mainly involved in DNA repair, mismatch repair, base excision repair, and nucleotide excision repair. Genes with downregulated expression were mainly involved in the TGF-β receptor signaling pathway, senescence and autophagy in cancer. More frequent shear events occurred in Piwil2-iCSCs and FBs, especially in intron retention (IR) events. We also identified three fusion genes MCM3AP-C21orf58, LRRFIP2-CAV3 and TMEM184B-DMC1. Enrichment analysis of DE miRNAs showed that they were associated with apoptosis, the TGF-β signaling pathway, and the stem cell regulatory signaling pathway. In particular, target gene prediction of the top three miRNAs with upregulated expression showed that they targeted SMAD, GREM1 and other genes to participate in the regulation of TGF-β and other pathways.

CONCLUSION

PIWIL2-induced cancer stem cells have significantly altered levels of miRNAs, piRNAs and mRNAs.TGF-β, autophagy, apoptosis and other pathways may play an important role in stem cell development. The occurrence of alternative splicing and fusion genes may be related to the occurrence of cancer stem cells.

Bioreactors for Vocal Fold Tissue Engineering

It is estimated that almost one-third of the United States population will be affected by a vocal fold (VF) disorder during their lifespan. Promising therapies to treat VF injury and scarring are mostly centered on VF tissue engineering strategies such as the injection of engineered biomaterials and cell therapy. VF tissue engineering, however, is a challenging field as the biomechanical properties, structure, and composition of the VF tissue change upon exposure to mechanical stimulation. As a result, the development of long-term VF treatment strategies relies on the characterization of engineered tissues under a controlled mechanical environment. In this review, we highlight the importance of bioreactors as a powerful tool for VF tissue engineering with a focus on the current state of the art of bioreactors designed to mimic phonation in vitro. We discuss the influence of the phonatory environment on the development, function, injury, and healing of the VF tissue and its importance for the development of efficient therapeutic strategies. A concise and comprehensive overview of bioreactor designs, principles, operating parameters, and scalability are presented. An in-depth analysis of VF bioreactor data to date reveals that mechanical stimulation significantly influences cell viability and the expression of proinflammatory and profibrotic genes in vitro. Although the precision and accuracy of bioreactors contribute to generating reliable results, diverse gene expression profiles across the literature suggest that future efforts should focus on the standardization of bioreactor parameters to enable direct comparisons between studies. Impact statement We present a comprehensive review of bioreactors for vocal fold (VF) tissue engineering with a focus on the influence of the phonatory environment on the development, function, injury, and healing of the VFs and the importance of mimicking phonation on engineered VF tissues in vitro. Furthermore, we put forward a strong argument for the continued development of bioreactors in this area with an emphasis on the standardization of bioreactor designs, principles, operating parameters, and oscillatory regimes to enable comparisons between studies.

Transcriptomic landscape of sodium butyrate-induced growth inhibition of human colorectal cancer organoids

Organoid, a novel model, is used to explore the deeper mechanism of sodium butyrate (NaB) in CRC by RNA-seq analysis.

Identification of potential core genes in colorectal carcinoma and key genes in colorectal cancer liver metastasis using bioinformatics analysis

Colorectal carcinoma (CRC) is one of the most prevalent malignant tumors worldwide. Meanwhile, the majority of CRC related deaths results from liver metastasis. Gene expression profile of CRC patients with liver Metastasis was identified using 4 datasets. The data was analyzed using GEO2R tool. GO and KEGG pathway analysis were performed. PPI network of the DEGs between 1 and 2 gene sets was also constructed. The set 1 is named between primary CRC tissues and metastatic CRC tissues. The set 2 is named between primary CRC tissues and normal tissues. Finally, the prognostic value of hub genes was also analyzed. 35 DEGs (set 1) and 142 DEGs (set 2) were identified between CRC liver metastatic cancer patients. The PPI network was constructed using the top 10 set 1 hub genes which included AHSG, SERPINC1, FGA, F2, CP, ITIH2, APOA2, HPX, PLG, HRG and set 2 hub genes which included TIMP1, CXCL1, COL1A2, MMP1, AURKA, UBE2C, CXCL12, TOP2A, ALDH1A1 and PRKACB. Therefore, ITIH2 might represent the potential core gene for colon cancer liver metastasis. COL1A2 behaves as a key gene in colorectal carcinoma.

Expression of cornulin in tongue squamous cell carcinoma

The aim of the study is to identify cornulin (CRNN) protein expression associated with advancement of tongue squamous cell carcinoma (TSCC). A comparison of addictive (containing potential carcinogens) versus non-addiction causative agents was expected to allow detection of differences in CRNN expression associated with TSCC. Bespoke tissue microarrays (TMAs) were prepared and immunohistochemistry (IHC) performed to determine the changes in CRNN expression in epithelial cells of node-negative (pN-), node-positive (pN+) TSCC and non-cancer patients’ oral tissues. TMAs were validated by performing IHC on whole diagnostic tissues. Chi-square test or Fisher’s-exact tests were used to establish significant expression differences. Analogous analyses were performed for biomarkers previously associated with TSCC, namely collagen I alpha 2 (COL1A2) and decorin (DCN) to compare the significance of CRNN. Keratinisation and its level (low, extensive) were studied in relation to CRNN so that the extent of squamous differentiation could better be assessed.

IHC immunoreactive score (IRS) clustered the patients based on weak/moderate (Low (IRS ≤ +3)) or strong (High (IRS ≥ +4)) expression groups. A low expression was observed in a larger number of patients in control proteins COL1A2 (77.3%), DCN (87.5%) and target protein CRNN (52.3%), respectively. Low CRNN expression was observed in TSCC where nodes were involved (pN+: mean 1.4 ± 2.1) (p = 0.248). Keratinisation (%) was low (0% ≤ 50%) in 42.2% and extensive (1% ≥ 50.0%) in 57.8% patients. In conclusion, our study suggested that Low CRNN expression was associated with grade and lymph node metastasis in TSCC. CRNN expression is independent of addiction, however potentially carcinogenic addictive substances might be aiding in the disease progression.

Identification of four genes and biological characteristics of esophageal squamous cell carcinoma by integrated bioinformatics analysis

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) has become one of the most serious diseases affecting populations worldwide and is the primary subtype of esophageal cancer (EC). However, the molecular mechanisms governing the development of ESCC have not been fully elucidated.

METHODS

The robust rank aggregation method was performed to identify the differentially expressed genes (DEGs) in six datasets (GSE17351, GSE20347, GSE23400, GSE26886, GSE38129 and GSE77861) from the Gene Expression Omnibus (GEO). The Search Tool for the Retrieval of Interacting Genes (STRING) database was utilized to extract four hub genes from the protein-protein interaction (PPI) network. Module analysis and disease free survival analysis of the four hub genes were performed by Cytoscape and GEPIA. The expression of hub genes was analyzed by GEPIA and the Oncomine database and verified by real-time quantitative PCR (qRT-PCR).

RESULTS

In total, 720 DEGs were identified in the present study; these genes consisted of 302 upregulated genes and 418 downregulated genes that were significantly enriched in the cellular component of the extracellular matrix part followed by the biological process of the cell cycle phase and nuclear division. The primary enriched pathways were hsa04110:Cell cycle and hsa03030:DNA replication. Four hub genes were screened out, namely, SPP1, MMP12, COL10A1 and COL5A2. These hub genes all exhibited notably increased expression in ESCC samples compared with normal samples, and ESCC patients with upregulation of all four hub genes exhibited worse disease free survival.

CONCLUSIONS

SPP1, MMP12, COL10A1 and COL5A2 may participate in the tumorigenesis of ESCC and demonstrate the potential to serve as molecular biomarkers in the early diagnosis of ESCC. This study may help to elucidate the molecular mechanisms governing ESCC and facilitate the selection of targets for early treatment and diagnosis.

20(S)-Protopanaxadiol inhibits epithelial-mesenchymal transition by promoting retinoid X receptor alpha in human colorectal carcinoma cells

Colorectal carcinoma (CRC) recurrence is often accompanied by metastasis. Most metastasis undergo through epithelial‐mesenchymal transition (EMT). Studies showed that retinol X receptor alpha (RXRα) and 20(S)‐Protopanaxadiol (PPD) have anti‐tumour effects. However, the anti‐metastasis effect of 20(S)‐PPD and the effect of RXRα on EMT‐induced metastasis are few studies on. Therefore, the role of RXRα and 20(S)‐PPD in CRC cell metastasis remains to be fully elucidated. RXRα with clinicopathological characteristics and EMT‐related expression in clinical samples were examined. Then, RXRα and EMT level in SW480 and SW620 cells, overexpressed and silenced RXRα in SW620 cells and SW480 cells, respectively, were evaluated. Finally, 20(S)‐PPD effect on SW620 and SW480 cells was evaluated. The results showed that a lower RXRα expression in cancer tissues, and a moderate negative correlation between RXRα and N stage, and tended to higher level of EMT. SW480 and SW620 cells had the highest and lowest RXRα expression among four CRC cell lines. SW480 had lower EMT level than SW620. Furthermore, 20(S)‐PPD increased RXRα and inhibited EMT level in SW620 cell. Finally, 20(S)‐PPD cannot restore SW480 cells EMT level to normal when RXRα silencing. These findings suggest that 20(S)‐PPD may inhibit EMT process in CRC cells by regulating RXRα expression.

Long non-coding RNA ATB is associated with metastases and promotes cell invasion in colorectal cancer via sponging miR-141-3p

Long non-coding RNAs (lncRNAs) serve crucial roles in cancer development and progression. lncRNA-activated by transforming growth factor-β (lncRNA-ATB) mediates cell proliferation. However, the association between lncRNA-ATB and human colorectal cancer (CRC) is not completely understood. Therefore, the present study aimed to investigate the role of lncRNA-ATB in CRC, as well as the underlying mechanism. 50 pairs of tumor tissues and adjacent normal tissues from patients with primary CRC were collected. The expression of lncRNA-ATB and microRNA (miR)-141-3p in CRC tissues, adjacent normal tissues and cell lines was detected using reverse transcription-quantitative PCR. CCK-8, colony formation, Transwell, western blot, dual luciferase reporter gene, RNA immunoprecipitation and immunohistochemistry staining assays were conducted to assess the biological function of lncRNA-ATB and miR-141-3p in CRC progression. lncRNA-ATB was upregulated in CRC tissues and cell lines compared with healthy tissues and cells, respectively. Moreover, high expression of lncRNA-ATB was significantly associated with advanced TNM stage and metastasis in CRC. In addition, the results indicated that lncRNA-ATB expression predicted the prognosis and overall survival of patients with CRC. Compared with small interfering RNA-negative control, lncRNA-ATB knockdown inhibited CRC cell proliferation, migration and invasion, whereas, compared with vector, lncRNA-ATB overexpression promoted CRC cell proliferation, migration and invasion. Furthermore, the in vivo experiment suggested that lncRNA-ATB knockdown inhibited tumor growth. The results also indicated that lncRNA-ATB may contribute to CRC progression via binding to tumor suppressor microRNA-141-3p. Collectively, the present study suggested a crucial role of lncRNA-ATB in CRC tumorigenesis, suggesting that lncRNA-ATB may serve as an important marker for the diagnosis and development of CRC.

Identification of Potential Core Genes Associated With the Progression of Stomach Adenocarcinoma Using Bioinformatic Analysis

Purpose

Stomach adenocarcinoma (STAD) is one of the most frequently diagnosed cancer in the world with both high mortality and high metastatic capacity. Therefore, the present study aimed to investigate novel therapeutic targets and prognostic biomarkers that can be used for STAD treatment.

Materials and Methods

We acquired four original gene chip profiles, namely GSE13911, GSE19826, GSE54129, and GSE65801 from the Gene Expression Omnibus (GEO). The datasets included a total of 114 STAD tissues and 110 adjacent normal tissues. The GEO2R online tool and Venn diagram software were used to discriminate differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) enriched pathways were also performed for annotation and visualization with DEGs. The STRING online database was used to identify the functional interactions of DEGs. Subsequently, we selected the most significant DEGs to construct the protein-protein interaction (PPI) network and to reveal the core genes involved. Finally, the Kaplan-Meier Plotter online database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to analyze the prognostic information of the core DEGs.

Results

A total of 114 DEGs (35 upregulated and 79 downregulated) were identified, which were abnormally expressed in the GEO datasets. GO analysis demonstrated that the majority of the upregulated DEGs were significantly enriched in collagen trimer, cell adhesion, and identical protein binding. The downregulated DEGs were involved in extracellular space, digestion, and inward rectifier potassium channel activity. Signaling pathway analysis indicated that upregulated DEGs were mainly enriched in receptor interaction, whereas downregulated DEGs were involved in gastric acid secretion. A total of 80 DEGs were screened into the PPI network complex, and one of the most important modules with a high degree was detected. Furthermore, 10 core genes were identified, namely COL1A1, COL1A2, FN1, COL5A2, BGN, COL6A3, COL12A1, THBS2, CDH11, and SERPINH1. Finally, the results of the prognostic information further demonstrated that all 10 core genes exhibited significantly higher expression in STAD tissues compared with that noted in normal tissues.

Conclusion

The multiple molecular mechanisms of these novel core genes in STAD are worthy of further investigation and may reveal novel therapeutic targets and biomarkers for STAD treatment.

Identification of candidate biomarkers and therapeutic drugs of colorectal cancer by integrated bioinformatics analysis

Most colorectal cancer (CRC) patients are diagnosed with advanced stages and low prognosis. We aimed to identify potential diagnostic and prognostic biomarkers, as well as active small molecules of CRC. Microarray data (GSE9348, GSE35279, and GSE106582) were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by the GEO2R platform. Common DEGs were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Cytoscape software was used to construct protein-protein interaction networks and identify hub genes. Hub genes were evaluated by Kaplan-Meier survival analysis in the GEPIA database and validated in two independent microarray data (GSE74602 and GSE83889). Common DEGs were used to select active small molecules by the connectivity map database. A total of 166 DEGs were identified as common DEGs. GO analysis demonstrated that common DEGs were significantly enriched in the apoptotic process, cell proliferation, and cell adhesion. KEGG analysis indicated that the most enriched pathways were the PI3K-Akt signaling pathway and extracellular matrix-receptor interaction. COL1A2, THBS2, TIMP1, and CXCL8 significantly upregulated in colorectal tumor. High expressions of COL1A2, THBS2, and TIMP1 were associated with poor survival, while high expressions of CXCL8 were associated with better survival. We selected 11 small molecules for CRC therapy. In conclusion, we found key dysregulated genes associated with CRC and potential small molecules to reverse them. COL1A2, THBS2, TIMP1, and CXCL8 may act as diagnostic and prognostic biomarkers of CRC.

Common gene signatures and key pathways in hypopharyngeal and esophageal squamous cell carcinoma: Evidence from bioinformatic analysis

BACKGROUND

Hypopharyngeal and esophageal squamous cell carcinoma (ESCC) are the most common double primary tumors with poor prognosis. Intensive work has been made to illuminate the etiology, but the common carcinogenic mechanism remains unclear. Thus, we conducted the study to seek to find the common gene signatures and key functional pathways associated with oncogenesis and treatment in hypopharyngeal squamous cell carcinoma (HSCC) and ESCC by bioinformatic analysis.

METHODS

Three independent datasets (GSE2379, GSE20347, and GSE75241) were screened out from the Gene Expression Omnibus (GEO) database and the overlapping differentially expressed genes (DEGs) were identified using GEO2R online platform. Subsequently, the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted using database for annotation, visualization and integrated discovery (DAVID). Meanwhile, the protein-protein interaction network (PPI) constructed by search tool for the retrieval of interacting genes (STRING) was visualized using Cytoscape. Afterwards, the most key module and hub genes were extracted from the PPI network using the Molecular Complex Detection plugin. Moreover, the gene expression profiling interactive analysis (GEPIA) was applied to verify the expression differences and conduct the survival analyses of hub genes. Finally, the interaction network of miRNAs and hub genes constructed by encyclopedia of RNA interactomes (ENCORI) was visualized using Cytoscape.

RESULTS

A total of 43 DEGs were identified, comprising 25 upregulated genes and 18 downregulated genes, which were mainly involved in the extracellular matrix-receptor interaction, collagen metabolic, epidermis development, cell adhesion, and PI3K/Akt signaling pathways. Subsequently, 12 hub genes were obtained and survival analysis demonstrated SERPINE1 and SPP1 were closely related to poor prognosis of patients with HSCC and ESCC. Finally, hsa-miR-29c-3p, hsa-miR-29a-3p, and hsa-miR-29b-3p were confirmed as the top 3 interactive miRNAs that target the most hub genes according to the interaction network of miRNAs and hub genes.

CONCLUSION

The common gene signatures and functional pathways identified in the study may contribute to understanding the molecular mechanisms involved in the carcinogenesis and progression of HSCC and ESCC, and provide potential diagnostic and therapeutic targets.

Enrichment of Up-regulated and Down-regulated Gene Clusters Using Gene Ontology, miRNAs and lncRNAs in Colorectal Cancer

AIM AND OBJECTIVE

It is interesting to find the gene signatures of cancer stages based on the omics data. The aim of study was to evaluate and to enrich the array data using gene ontology and ncRNA databases in colorectal cancer.

METHODS

The human colorectal cancer data were obtained from the GEO databank. The downregulated and up-regulated genes were identified after scoring, weighing and merging of the gene data. The clusters with high-score edges were determined from gene networks. The miRNAs related to the gene clusters were identified and enriched. Furthermore, the long non-coding RNA (lncRNA) networks were predicted with a central core for miRNAs.

RESULTS

Based on cluster enrichment, genes related to peptide receptor activity (1.26E-08), LBD domain binding (3.71E-07), rRNA processing (2.61E-34), chemokine (4.58E-19), peptide receptor (1.16E-19) and ECM organization (3.82E-16) were found. Furthermore, the clusters related to the non-coding RNAs, including hsa-miR-27b-5p, hsa-miR-155-5p, hsa-miR-125b-5p, hsa-miR-21-5p, hsa-miR-30e-5p, hsa-miR-588, hsa-miR-29-3p, LINC01234, LINC01029, LINC00917, LINC00668 and CASC11 were found.

CONCLUSION

The comprehensive bioinformatics analyses provided the gene networks related to some non-coding RNAs that might help in understanding the molecular mechanisms in CRC.

Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes

As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medicine provides an opportunity to improve the treatment of lung cancer. Subtyping lung cancer plays an important role in performing a specific treatment. Here, we developed a framework that combines k-means clustering, t-test, sensitivity analysis, self-organizing map (SOM) neural network, and hierarchical clustering methods to classify LUAD into four subtypes. We determined that 24 differentially expressed genes could be used as therapeutic targets, and five genes (i.e., RTKN2, ADAM6, SPINK1, COL3A1, and COL1A2) could be potential novel markers for LUAD. Multivariate analysis showed that the four subtypes could serve as prognostic subtypes. Representative genes of each subtype were also identified, which could be potentially targetable markers for the different subtypes. The function and pathway enrichment analyses of these representative genes showed that the four subtypes have different pathological mechanisms. Mutations associated with the subtypes, e.g., epidermal growth factor receptor (EGFR) mutations in subtype 4 and tumor protein p53 (TP53) mutations in subtypes 1 and 2, could serve as potential markers for drug development. The four subtypes provide a foundation for subtype-specific therapy of LUAD.

Insights into Aflatoxin B1 Toxicity in Cattle: An In Vitro Whole-Transcriptomic Approach

Aflatoxins, and particularly aflatoxin B1 (AFB1), are toxic mycotoxins to humans and farm animal species, resulting in acute and chronic toxicities. At present, AFB1 is still considered a global concern with negative impacts on health, the economy, and social life. In farm animals, exposure to AFB1-contaminated feed may cause several untoward effects, liver damage being one of the most devastating ones. In the present study, we assessed in vitro the transcriptional changes caused by AFB1 in a bovine fetal hepatocyte-derived cell line (BFH12). To boost the cellular response to AFB1, cells were pre-treated with the co-planar PCB 3,3′,4,4′,5-pentachlorobiphenyl (PCB126), a known aryl hydrocarbon receptor agonist. Three experimental groups were considered: cells exposed to the vehicle only, to PCB126, and to PCB126 and AFB1. A total of nine RNA-seq libraries (three replicates/group) were constructed and sequenced. The differential expression analysis showed that PCB126 induced only small transcriptional changes. On the contrary, AFB1 deeply affected the cell transcriptome, the majority of significant genes being associated with cancer, cellular damage and apoptosis, inflammation, bioactivation, and detoxification pathways. Investigating mRNA perturbations induced by AFB1 in cattle BFH12 cells will help us to better understand AFB1 toxicodynamics in this susceptible and economically important food-producing species.

Novel Insights Into Triple-Negative Breast Cancer Prognosis by Comprehensive Characterization of Aberrant Alternative Splicing

Background

Alternative splicing (AS) is important in the regulation of gene expression and aberrant AS is emerging as a major factor in the pathogenesis of human conditions, including cancer. Triple-negative breast cancer (TNBC) is the most challenging subtype of breast cancer with strong invasion, high rate of metastasis, and poor prognosis. Here we report a systematic profiling of aberrant AS in TNBC.

Methods

The percent spliced in (PSI) values for AS events in 151 TNBC patients were obtained from The Cancer Genome Atlas (TCGA) SpliceSeq database. Univariate Cox and stepwise Multivariate Cox regression analyses were conducted to find the best prognostic AS model. Splicing regulatory networks were constructed by prognosis-related spliceosome and aberrant AS events. Additionally, pathway enrichment and gene set enrichment analysis (GSEA) were further employed to reveal the significant pathways for prognosis-related AS genes. Finally, splicing regulatory networks were constructed via Spearman's rank correlation coefficients between prognosis-related AS events and splicing factor expressions.

Results

A total of 1,397 prognosis-associated AS events were identified in TNBC. The majority of the parent genes of prognostic AS events exhibited direct interactions to each other in the STRING gene network. Pathways of focal adhesion (p < 0.001), RNA splicing (p = 0.007), homologous recombination (p = 0.042) and ECM-receptor interaction (p = 0.046) were found to be significantly enriched for prognosis-related AS. Additionally, the area under curve (AUC) of the best AS prognostic predictor model reached 0.949, showing a powerful capability to predict outcomes. The Exon Skip (ES) type of AS events displayed more robust and efficient capacity in predicting performance than any other specific AS events type in terms of prognosis. The ES AS signature might confer a strong oncogenic phenotype in the high-risk group with elevated activities in cell cycle and SUMOylating pathways of tumorigenesis, while programmed cell death and metabolism pathways were found to be enriched in the low-risk group of TNBC. The splicing correlation network also revealed a regulatory mode of prognostic splicing factors (SFs) in TNBC.

Conclusion

Our analysis of AS events in TNBC could not only contribute to elucidating the tumorigenesis mechanism of AS but also provide clues to uncovering underlying prognostic biomarkers and therapeutic targets for further study.

Identification of hub genes and construction of transcriptional regulatory network for the progression of colon adenocarcinoma hub genes and TF regulatory network of colon adenocarcinoma

The aim of this study was to identify key genes related to the progression of colon adenocarcinoma (COAD), and to investigate the regulatory network of hub genes and transcription factors (TFs). Dataset GSE20916 including 44 normal colon, 55 adenoma, and 36 adenocarcinoma tissue samples was used to construct co‐expression networks via weighted gene co‐expression network. Gene Ontology annotation and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis for the objective module were performed using the online Database for Annotation, Visualization and Integrated Discovery. Hub genes were identified by taking the intersection of differentially expressed genes between dataset GSE20916 and GSE39582 and validated using The Cancer Genome Atlas (TCGA) database. The correlations between microRNA (miRNA) and hub genes were analyzed using the online website StarBase. Cytoscape was used to establish a regulatory network of TF‐miRNA‐target gene. We found that the orange module was a key module related to the tumor progression in COAD. In datasets GSE20916 and GSE39582, a total of eight genes (BGN, SULF1, COL1A1, FAP, THBS2, CTHRC1, COL5A2, and COL1A2) were selected, which were closely related with patients’ survivals in TCGA database and dataset GSE20916. COAD patients with higher expressions of each hub gene had a worse prognosis than those with lower expressions. A regulatory network of TF‐miRNA‐target gene with 144 TFs, 26 miRNAs, and 7 hub genes was established, including model KLF11‐miR149‐BGN, TCEAL6‐miR29B2‐COL1A1, and TCEAL6‐miR29B2‐COL1A2. In conclusion, during the progression of COAD, eight core genes (BGN, SULF1, COL1A1, FAP, THBS2, CTHRC1, COL5A2, and COL1A2) play vital roles. Regulatory networks of TF‐miRNA‐target gene can help to understand the disease progression and optimize treatment strategy.

A qualitative transcriptional signature for determining the grade of colorectal adenocarcinoma

Histological grading (HG) is an important prognostic factor of colorectal adenocarcinoma (CRAC): the high-grade CRAC patients have poorer prognosis after tumor resection. Especially, the high-grade stage II CRAC patients are recommended to receive adjuvant chemotherapy. Due to the subjective nature of HG assessment, it is difficult to achieve consistency among pathologists, which brings patients uncertain grading outcomes and inappropriate treatments. We developed a qualitative transcriptional signature based on the within-sample relative expression orderings (REOs) of gene pairs to discriminate high-grade and low-grade CRAC. Using the stage II-III CRAC samples, we detected gene pairs with stable REOs in the high-grade samples and reversal stable REOs in the low-grade samples, and retained the gene pairs whose specific REO patterns were significantly associated with the disease-free survival of patients by univariate Cox regression model. Then, we used a forward-backward searching procedure to extract gene pairs with the highest concordance index as the final grading signature. Finally, 9 gene pairs (9-GPS) were developed to divide CRAC patients into high-grade and low-grade groups. With the signature, there were more differential expression characteristics between reclassified high-grade and low-grade groups. Significant difference of prognosis between the classified two group patients could be seen in four independent datasets. Additionally, genomic analyses showed that the classified high-grade groups were characterized by hypermutation while classified low-grade groups were characterized by frequent copy number alternations. In conclusion, the 9-GPS can provide an objective and robust grading assessment for CRAC patients, which could assist clinical treatment decision.

The role of collagen in cancer: from bench to bedside

Collagen is the major component of the tumor microenvironment and participates in cancer fibrosis. Collagen biosynthesis can be regulated by cancer cells through mutated genes, transcription factors, signaling pathways and receptors; furthermore, collagen can influence tumor cell behavior through integrins, discoidin domain receptors, tyrosine kinase receptors, and some signaling pathways. Exosomes and microRNAs are closely associated with collagen in cancer. Hypoxia, which is common in collagen-rich conditions, intensifies cancer progression, and other substances in the extracellular matrix, such as fibronectin, hyaluronic acid, laminin, and matrix metalloproteinases, interact with collagen to influence cancer cell activity. Macrophages, lymphocytes, and fibroblasts play a role with collagen in cancer immunity and progression. Microscopic changes in collagen content within cancer cells and matrix cells and in other molecules ultimately contribute to the mutual feedback loop that influences prognosis, recurrence, and resistance in cancer. Nanoparticles, nanoplatforms, and nanoenzymes exhibit the expected gratifying properties. The pathophysiological functions of collagen in diverse cancers illustrate the dual roles of collagen and provide promising therapeutic options that can be readily translated from bench to bedside. The emerging understanding of the structural properties and functions of collagen in cancer will guide the development of new strategies for anticancer therapy.

Identification of mechanisms conferring an enhanced immune response in mice induced by CVC1302-adjuvanted killed serotype O foot-and-mouth virus vaccine

The adjuvant CVC1302 was previously shown to efficiently enhance the immunogenicity of killed foot-and-mouth disease virus (FMDV) in mice and piglets. However, the underlining mechanism of action of CVC1302 remains unclear, especially at local injection sites and draining lymph nodes. Since the FMDV vaccine is administrated intramuscularly in field settings, we studied local immune responses to FMDV following intramuscular injection in mice, and found that CVC1302-adjuvanted killed FMDV (KV-CVC1302) induced secretion of several chemokines in murine muscle tissues, including MCP-1, MIP-1α, and MIP-1β. The number of monocytes recruited to the site of injection was significantly higher in mice immunized with KV-CVC1302 compared with mice immunized with killed FMDV alone (KV). iTAQ-based quantitative proteomic assays were additionally employed to explore the molecular mechanisms of CVC1302 action in the draining lymph nodes. A total of 35 proteins were identified as being differentially expressed among the control group, KV-immunized group and KV-CVC1302-immunized group at 10 days post immunization (dpi). Proteins exhibiting differential expression were mainly involved in signal transduction, apoptosis, endocytosis and innate immune responses. Pathway analysis demonstrated that AMPK, phospholipase D, cAMP, Rap1, and MAPK signaling pathways were potentially induced by the immunopotentiator CVC1302. Understanding the local mechanism of CVC1302 action at injection sites and draining lymph nodes will provide new insights into the development of FMDV vaccines.

Overexpressed pseudogenes, DUXAP8 and DUXAP9, promote growth of renal cell carcinoma and serve as unfavorable prognostic biomarkers

BACKGROUND

Growing studies have reported that pseudogenes play key roles in multiple human cancers. However, expression and roles of pseudogenes in renal cell carcinoma remains absent.

RESULTS

31 upregulated and 16 downregulated pseudogenes were screened. Higher expression of DUXAP8 and DUXAP9 indicated poorer prognosis of kidney cancer. 33 and 5 miRNAs were predicted to potentially binding to DUXAP8 and DUXAP9, respectively. miR-29c-3p was identified as the most potential binding miRNAs of DUXAP8 and DUXAP9 based on expression, survival and correlation analyses. 254 target genes of miR-29c-3p were forecast. 47 hub genes with node degree >= 10 were identified. Subsequent analysis for the top 10 hub genes demonstrated that COL1A1 and COL1A2 may be two functional targets of DUXAP8 and DUXAP9. Expression of DUXAP8, DUXAP9, COL1A1 and COL1A2 were significantly increased in cancer samples compared to normal controls while miR-29c-3p expression was decreased. Luciferase reporter assay revealed that miR-29c-3p could directly bind to DUXAP8, DUXAP9, COL1A1 and COL1A2. Functional experiments showed that DUXAP8 and DUXAP9 enhanced but miR-29c-3p weakened growth of renal cell carcinoma.

CONCLUSIONS

In conclusion, upregulated DUXAP8 and DUXAP9 promote growth of renal cell carcinoma and serve as two promising prognostic biomarkers.

METHODS

Dysregulated pseudogenes were obtained by dreamBase and GEPIA. The binding miRNAs of pseudogene and targets of miRNA were predicted using starBase and miRNet. Kaplan-Meier plotter was utilized to perform survival analysis, and Enrichr database was introduced to conduct functional enrichment analysis. Hub genes were identified through STRING and Cytoscape. qRT-PCR, luciferase reporter assay, cell counting assay and colony formation assay were performed to validate in silico analytic results.

Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy

Background

Esophageal squamous cell carcinoma (ESCC) is one of leading malignant cancers of gastrointestinal tract worldwide. Until now, the involved mechanisms during the development of ESCC are largely unknown. This study aims to explore the driven-genes and biological pathways in ESCC.

Methods

mRNA expression datasets of GSE29001, GSE20347, GSE100942, and GSE38129, containing 63 pairs of ESCC and non-tumor tissues data, were integrated and deeply analyzed. The bioinformatics approaches include identification of differentially expressed genes (DEGs) and hub genes, gene ontology (GO) terms analysis and biological pathway enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, and miRNA-gene network construction. Subsequently, GEPIA2 database and qPCR assay were utilized to validate the expression of hub genes. DGIdb database was performed to search the candidate drugs for ESCC.

Results

Finally, 120 upregulated and 26 downregulated DEGs were identified. The functional enrichment of DEGs in ESCC were mainly correlated with cell cycle, DNA replication, deleted in colorectal cancer (DCC) mediated attractive signaling pathway, and Netrin-1 signaling pathway. The PPI network was constructed using STRING software with 146 nodes and 2392 edges. The most significant three modules in PPI were filtered and analyzed. Totally ten genes were selected and considered as the hub genes and nuclear division cycle 80 (NDC80) was closely related to the survival of ESCC patients. DGIdb database predicted 33 small molecules as the possible drugs for treating ESCC.

Conclusions

In summary, the data may provide new insights into ESCC pathogenesis and treatments. The candidate drugs may improve the efficiency of personalized therapy in future.

Quantitative label-free mass spectrometry using contralateral and adjacent breast tissues reveal differentially expressed proteins and their predicted impacts on pathways and cellular functions in breast cancer

Proteins play an essential role in the biological processes associated with cancer. Their altered expression levels can deregulate critical cellular pathways and interactive networks. In this study, the mass spectrometry-based label-free quantification followed by functional annotation was performed to investigate the most significant deregulated proteins among tissues of primary breast tumor (PT) and axillary metastatic lymph node (LN) and corresponding non-tumor tissues contralateral (NCT) and adjacent (ANT) from patients diagnosed with invasive ductal carcinoma. A total of 462 proteins was observed as differentially expressed (DEPs) among the groups analyzed. A high level of similarity was observed in the proteome profile of both non-tumor breast tissues and DEPs (n = 12) were mainly predicted in the RNA metabolism. The DEPs among the malignant and non-tumor breast tissues [n = 396 (PTxNCT) and n = 410 (LNxNCT)] were related to pathways of the LXR/RXR, NO, eNOS, eIF2 and sirtuins, tumor-related functions, fatty acid metabolism and oxidative stress. Remarkable similarity was observed between both malignant tissues, which the DEPs were related to metastatic capabilities. Altogether, our findings revealed differential proteomic profiles that affected cancer associated and interconnected signaling processes. Validation studies are recommended to demonstrate the potential of individual proteins and/or pathways as biological markers in breast cancer. SIGNIFICANCE: The proteomic analysis of this study revealed high similarity in the proteomic profile of the contralateral and adjacent non-tumor breast tissues. Significant differences were identified among the proteome of the malignant and non-tumor tissue groups of the same patients, providing relevant insights into the hallmarks, signaling pathways, biological functions, and interactive protein networks that act during tumorigenesis and breast cancer progression. These proteins are suggested as targets of relevant interest to be explored as potential biological markers related to tumor development and metastatic progression in the breast cancer disease.

deepDriver: Predicting Cancer Driver Genes Based on Somatic Mutations Using Deep Convolutional Neural Networks

With the advances in high-throughput technologies, millions of somatic mutations have been reported in the past decade. Identifying driver genes with oncogenic mutations from these data is a critical and challenging problem. Many computational methods have been proposed to predict driver genes. Among them, machine learning-based methods usually train a classifier with representations that concatenate various types of features extracted from different kinds of data. Although successful, simply concatenating different types of features may not be the best way to fuse these data. We notice that a few types of data characterize the similarities of genes, to better integrate them with other data and improve the accuracy of driver gene prediction, in this study, a deep learning-based method (deepDriver) is proposed by performing convolution on mutation-based features of genes and their neighbors in the similarity networks. The method allows the convolutional neural network to learn information within mutation data and similarity networks simultaneously, which enhances the prediction of driver genes. deepDriver achieves AUC scores of 0.984 and 0.976 on breast cancer and colorectal cancer, which are superior to the competing algorithms. Further evaluations of the top 10 predictions also demonstrate that deepDriver is valuable for predicting new driver genes.