Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

Hub genes identification and association of key pathways with hypoxia in cancer cells: A bioinformatics analysis

Three human cancer cell lines (A549, HCT116, and HeLa) were used to investigate the molecular mechanisms and potential prognostic biomarkers associated with hypoxia. We obtained gene expression data from Gene Expression Omnibus (GEO) datasets GSE11704, GSE147384, and GSE38061, which included 5 hypoxic and 8 control samples. Using the GEO2R tool and Venn diagram software, we identified common differentially expressed genes (cDEGs). The cDEGs were then subjected to Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis by employing DAVID. The hub genes were identified from critical PPI subnetworks through CytoHuba plugin and these genes' prognostic significance and expression were verified using Kaplan-Meier analysis and Gene Expression Profiling Interactive Analysis (GEPIA), respectively. The research showed 676 common DEGs (cDEGs), with 207 upregulated and 469 downregulated genes. The STRING analysis showed 673 nodes and 1446 edges in the PPI network. We identified 4 significant modules and 19 downregulated hub genes. GO analysis revealed all of them were majorly involved in ribosomal large subunit assembly and biogenesis, rRNA processing, ribosome biogenesis, translation, RNA & protein binding frequently at the sites of nucleolus and nucleoplasm while 11 were significantly associated with a better prognosis of hypoxic tumors. Our research sheds light on the molecular mechanisms that underpin hypoxia in human cancer cell lines and identifies potential prognostic biomarkers for hypoxic tumors.

Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma

Inflammatory response plays a crucial role in the development and progression of gliomas. Whereas the prognostic esteem of inflammatory response-related genes has never been comprehensively explored in glioma, the RNA-seq information and clinical data of patients with glioma were extracted from TCGA, CGGA, and Rembrandt databases. The differentially expressed genes (DEGs) were picked out between glioma tissue and non-tumor brain tissue (NBT). Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct the prognostic signature in the TCGA cohort and verified in other cohorts. Kaplan-Meier survival analyses were conducted to compare the overall survival (OS) between the high and low-risk groups. Univariate and multivariate Cox analyses were subsequently used to confirm the independent prognostic factors of OS, and then, the nomogram was established based them. Furthermore, immune infiltration, immune checkpoints, and immunotherapy were also probed and compared between high and low-risk groups. The four genes were also analyzed by qRT-PCR, immunohistochemistry, and western blot trials between glioma tissue and NBT. The 39 DEGs were identified between glioma tissue and NBT, of which 31 genes are associated to the prognosis of glioma. The 8 optimal inflammatory response-related genes were selected to construct the prognostic inflammatory response-related signature (IRRS) through the LASSO regression. The effectiveness of the IRRS was verified in the TCGA, CGGA, and Rembrandt cohorts. Meanwhile, a nomogram with better accuracy was established to predict OS based on the independent prognostic factors. The IRRS was highly correlated with clinicopathological features, immune infiltration, and genomic alterations in glioma patients. In addition, four selective genes also verified the difference between glioma tissue and NBT. A novel prognostic signature was associated with the prognosis, immune infiltration, and immunotherapy effect in patients with gliomas. Thus, this study could provide a perspective for glioma prognosis and treatment.

A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer

Non-small cell lung cancer (NSCLC), the primary histological form of lung cancer, accounts for about 25%-the highest-of all cancer deaths. As NSCLC is often undetected until symptoms appear in the late stages, it is imperative to discover more effective tumor-associated biomarkers for early diagnosis. Topological data analysis is one of the most powerful methodologies applicable to biological networks. However, current studies fail to consider the biological significance of their quantitative methods and utilize popular scoring metrics without verification, leading to low performance. To extract meaningful insights from genomic data, it is essential to understand the relationship between geometric correlations and biological function mechanisms. Through bioinformatics and network analyses, we propose a novel composite selection index, the C-Index, that best captures significant pathways and interactions in gene networks to identify biomarkers with the highest efficiency and accuracy. Furthermore, we establish a 4-gene biomarker signature that serves as a promising therapeutic target for NSCLC and personalized medicine. The C-Index and biomarkers discovered were validated with robust machine learning models. The methodology proposed for finding top metrics can be applied to effectively select biomarkers and early diagnose many diseases, revolutionizing the approach to topological network research for all cancers.

Discovery of drug targets and therapeutic agents based on drug repositioning to treat lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the one of the most common subtypes in lung cancer. Although various targeted therapies have been used in the clinical practice, the 5-year overall survival rate of patients is still low. Thus, it is urgent to identify new therapeutic targets and develop new drugs for the treatment of the LUAD patients.

METHODS

Survival analysis was used to identify the prognostic genes. Gene co-expression network analysis was used to identify the hub genes driving the tumor development. A profile-based drug repositioning approach was used to repurpose the potentially useful drugs for targeting the hub genes. MTT and LDH assay were used to measure the cell viability and drug cytotoxicity, respectively. Western blot was used to detect the expression of the proteins.

FINDINGS

We identified 341 consistent prognostic genes from two independent LUAD cohorts, whose high expression was associated with poor survival outcomes of patients. Among them, eight genes were identified as hub genes due to their high centrality in the key functional modules in the gene-co-expression network analysis and these genes were associated with the various hallmarks of cancer (e.g., DNA replication and cell cycle). We performed drug repositioning analysis for three of the eight genes (CDCA8, MCM6, and TTK) based on our drug repositioning approach. Finally, we repurposed five drugs for inhibiting the protein expression level of each target gene and validated the drug efficacy by performing in vitro experiments.

INTERPRETATION

We found the consensus targetable genes for the treatment of LUAD patients with different races and geographic characteristics. We also proved the feasibility of our drug repositioning approach for the development of new drugs for disease treatment.

Down-regulation of FBP1 in lung adenocarcinoma cells promotes proliferation and invasion through SLUG mediated epithelial mesenchymal transformation

Background

Metabolic reprogramming and epithelial-mesenchymal transformation (EMT) play an important role in lung cancer. In recent studies, metabolic enzymes such as Fructose-1,6-bisphosphatase 1 (FBP1) have shown potential functions beyond regulating metabolism.

Methods

Western blot assay was performed to detect glycolysis-related and EMT-related protein expression levels. The glucose uptake kit and adenosine triphosphate (ATP) detection kit were used to detect glucose uptake rate and ATP content. Transwell assay was used to determine the invasiveness of lung adenocarcinoma cells. Wound healing assay was used to determine the metastatic ability of lung adenocarcinoma cells. Methyl thiazolyl tetrazolium (MTT) assay and EdU staining were performed to investigate the effect of FBP1 overexpression on lung adenocarcinoma proliferation.

Results

Overexpression of FBP1 down-regulated glycolysis-related protein levels and inhibited glucose uptake and ATP production, while knockdown of FBP1 had the opposite effect. Overexpression of FBP1 reversed EMT and inhibited Slug expression. Meanwhile, overexpression of FBP1 impaired the invasion, metastasis and proliferation ability of lung adenocarcinoma cells. In contrast, FBP1 knockdown promoted the EMT process, up-regulated Slug expression and enhanced the invasion, metastasis and proliferation of lung adenocarcinoma cells.

Conclusions

Therefore, FBP1 can be used as one of the potential clinical targets through inhibiting glycolysis, cell invasion and proliferation by inhibiting Slug mediated EMT processes.

Interfering with CALCRL expression inhibits glioma proliferation, promotes apoptosis, and predicts prognosis in low-grade gliomas

Background

CALCRL is involved in a variety of key biological processes, including cell proliferation, apoptosis, angiogenesis, and inflammation. However, the role of CALCRL in glioma remains unknown. The purpose of this study was to investigate the effect of differential CALCRL expression on the malignant progression of glioma and its value in glioma prognosis.

Methods

Sequencing data from glioma and normal tissues were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, and the downloaded data were statistically analyzed using bioinformatics tools and the corresponding R package. The expression of CALCRL in normal brain tissue and different grades of glioma tissue was detected by pathological and immunohistochemical staining of clinical glioma specimens. The expression of CALCRL in different glioma cell lines was detected by quantitative real-time polymerase chain reaction (qRT-PCR), and the U87 cell line with high expression was selected to construct the CALCRL knockdown model by transfection with short hairpin (shRNA). The cell proliferation ability was detected by Celigo assay and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, the ability of cell clone formation was detected by clone formation assay, and the level of apoptosis was detected by flow cytometry.

Results

The expression of CALCRL in glioma was significantly upregulated compared with that of normal tissue, especially in low-grade glioma (LGG) compared to glioblastoma, and the differential expression of CALCRL correlated significantly with the prognosis of LGG. Clinical pathology and immunohistochemistry showed that the expression of CALCRL was related to the pathological grade of glioma, and the highest expression was found in World Health Organization (WHO) grade Ⅲ glioma. The results of qRT-PCR showed that CALCRL expression was highest in the U87 cell line. After knockdown of CALCRL expression, the proliferation and clonogenic ability of U87 cells were significantly decreased, and the apoptosis rate was significantly increased.

Conclusions

CALCRL is highly expressed in LGG. Interfering with CALCRL expression inhibits glioma cell proliferation and promotes apoptosis, and thus has potential as a biomarker and therapeutic target for the prognosis of those with LGGs.

High intestinal vascular permeability in a murine model for Hirschsprung’s disease: implications for postoperative Hirschsprung-associated enterocolitis

PURPOSE

Intestinal vascular permeability (VP) in a murine model for Hirschsprung's disease (HD) and postoperative Hirschsprung-associated enterocolitis (HAEC) were investigated.

METHODS

Intestinal VP was determined using a Miles assay using 1% Evans blue injected into a superficial temporal vein of newborn endothelin receptor-B KO HD model (KO) and syngeneic wild-type (WT) mice (n = 5, respectively). Extravasated Evans blue in normoganglionic ileum (Ng-I), normoganglionic proximal colon (Ng-PC) and aganglionic distal colon (Ag-DC) was quantified by absorbance at 620 nm. Quantitative polymerase chain reaction (qPCR) for Vascular Endothelial Growth Factor A (VEGF-A), VEGF-B, CDH5, SELE and CD31, and immunofluorescence for CD31 were performed.

RESULTS

VP was significantly higher in Ng-I, Ng-PC, and Ag-DC from KO than WT (p < 0.01, p < 0.05, and p < 0.05, respectively). qPCR demonstrated upregulated VEGF-A in Ng-I and Ag-DC, VEGF-B in Ng-I, and SELE in Ng-I and Ng-PC (p < 0.05, p < 0.05, p < 0.05, p < 0.01 and p < 0.05, respectively), and downregulated CDH5 in Ng-I and Ng-PC from KO (p < 0.05, respectively). Expression of CD31 mRNA in Ng-I and Ag-DC from KO was significantly higher on qPCR (p < 0.05) but differences on immunofluorescence were not significant.

CONCLUSIONS

VP may be etiologic for postoperative HAEC throughout the intestinal tract even after excision of aganglionic bowel.

Characteristics of circadian rhythm-related genes and establishment of a prognostic scoring system for lung adenocarcinoma with experimental verification: a bioinformatics analysis

BACKGROUND

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors worldwide. Lung adenocarcinoma (LUAD), the main subtype of NSCLC, has a poor prognosis. In recent years, circadian rhythm (CR)-related genes (CRRGs) have demonstrated associations with tumor occurrence and development, but the relationship between CRRGs and LUAD is not clear. Because of the correlation between CRRGs and tumors, we have reason to believe that CRRGs will become an important prognostic indicator for LUAD and guide the treatment of LUAD prognosis.

METHODS

Based on data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we explored the biological function and immune cell infiltration of LUAD in different CR clusters and quantified CR genes using principal component analysis (PCA). Then, we built a CR scoring system (CRscore) to explore the relationship between CRRGs and LUAD prognosis.

RESULTS

Patients were divided into three clusters (A, B, and C). Biological characteristics, such as survival, immune cell infiltration, and gene enrichment, were significantly different among the three clusters (P<0.001). We then established the usefulness of the CR score, which could probably predict the prognosis of LUAD. Specifically, patients with a high CR score had a better prognosis and were more sensitive to chemotherapy than those with a low CR score.

CONCLUSIONS

It is possible to use CRRGs to assess the prognosis of patients with LUAD. Quantification of CR using the CRscore tool in patients with LUAD maybe help to guide personalized cancer immunotherapy strategies in the future. Thus, the CRscore may be a prognostic tool for LUAD.

Identification of differentially expressed genes at the single-cell level and prognosis prediction through bulk RNA sequencing data in breast cancer

Background: The invention and development of single-cell technologies have contributed a lot to the understanding of tumor heterogeneity. The objective of this research was to investigate the differentially expressed genes (DEGs) between normal and tumor cells at the single-cell level and explore the clinical application of these genes with bulk RNA-sequencing data in breast cancer.

Methods: We collected single-cell, bulk RNA sequencing (RNA-seq) and microarray data from two public databases. Through single-cell analysis of 23,909 mammary gland cells from seven healthy donors and 33,138 tumor cells from seven breast cancer patients, cell type-specific DEGs between normal and tumor cells were identified. With these genes and the bulk RNA-seq data, we developed a prognostic signature and validated the efficacy in two independent cohorts. We also explored the differences of immune infiltration and tumor mutational burden (TMB) between the different risk groups.

Results: A total of 6,175 cell-type-specific DEGs were obtained through the single-cell analysis between normal and tumor cells in breast cancer, of which 1,768 genes intersected with the bulk RNA-seq data. An 18-gene signature was constructed to assess the outcomes in breast cancer patients. The efficacy of the signature was notably prominent in two independent cohorts. The low-risk group showed higher immune infiltration and lower TMB. Among the 18 genes in the signature, 16 were also differentially expressed in the bulk RNA-seq dataset.

Conclusion: Cell-type-specific DEGs between normal and tumor cells were identified through single-cell transcriptome data. The signature constructed with these DEGs could stratify patients efficiently. The signature was also closely correlated with immune infiltration and TMB. Nearly all the genes in the signature were also differentially expressed at the bulk RNA-seq level.

Integrated Bioinformatics Analysis for Identifying the Significant Genes as Poor Prognostic Markers in Gastric Adenocarcinoma

Gastric adenocarcinoma (GAC) is the most common histological type of gastric cancer and imposes a considerable health burden globally. The purpose of this study was to identify significant genes and key pathways participated in the initiation and progression of GAC. Four datasets (GSE13911, GSE19826, GSE54129, and GSE79973) including 171 GAC and 77 normal tissues from Gene Expression Omnibus (GEO) database were collected and analyzed. Through integrated bioinformatics analysis, we obtained 69 commonly differentially expressed genes (DEGs) among the four datasets, including 20 upregulated and 49 downregulated genes. The prime module in protein-protein interaction network of DEGs, including ADAMTS2, COL10A1, COL1A1, COL1A2, COL8A1, BGN, and SPP1, was enriched in protein digestion and absorption, ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and amoebiasis. Furthermore, expression and survival analysis found that all seven hub genes were highly expressed in GAC tissues and 6 of them (except for SPP1) were able to predict poor prognosis of GAC. Finally, we verified the 6 high-expressed hub genes in GAC tissues via immunohistochemistry, Western blot, and RNA quantification analysis. Altogether, we identified six significantly upregulated DEGs as poor prognostic markers in GAC based on integrated bioinformatical methods, which could be potential molecular markers and therapeutic targets for GAC patients.

Autophagy characteristics and establishment of autophagy prognostic models in lung adenocarcinoma and lung squamous cell carcinoma

Background

Non-small cell lung cancer (NSCLC), which makes up the majority of lung cancers, remains one of the deadliest malignancies in the world. It has a poor prognosis due to its late detection and lack of response to chemoradiaiton. Therefore, it is urgent to find a new prognostic marker.

Methods

We evaluated biological function and immune cell infiltration in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients from TCGA and GEO databases between different clusters based on autophagy related hub genes. Autophagy scores were used to assess the degree of autophagy in each individual by using component analysis.

Results

Three different clusters were obtained. Gene set variation analysis, single-sample gene set enrichment analysis and survive analysis showed differences among these three clusters. We demonstrated that the autophagy score of each patient could predict tumor stage and prognosis. Patients with a high autophagy score had a better prognosis, higher immune infiltration, and were more sensitive to immunotherapy and conventional chemotherapy.

Conclusion

It was uncovered that autophagy played an irreplaceable role in NSCLC. Quantified autophagy scores for each NSCLC patient would help guide effective treatment strategies.

Matrisome provides a supportive microenvironment for oral squamous cell carcinoma progression

Oral squamous cell carcinoma (OSCC) is a common pernicious tumor in the head and neck regions. However, the function of tumor extracellular matrix (ECM) has not been elucidated. A tissue engineering method was applied for remodeling ECM through decellularization. The cellular components were removed, and the biological composition was mostly preserved. Proteomics was performed to analyze the characterization between normal and tumor ECM. According to LC-MS/MS results, 26 proteins just showed in tumor ECM, and 14 proteins only showed in late-stage tumor ECM. KEGG pathway analysis showed that most variant proteins were linked to metabolic regulation and tumor immunity (such as SCC-Ag1, LOX). To affirm the influence of tumor ECM on the progression of OSCC, tumor cells and macrophages were co-cultured with ECM scaffold. Marked differences in proliferation, apoptosis, and migration of OSCC cells were observed between tumor and normal ECM. Tumor ECM polarized macrophages towards an anti-inflammatory phenotype (higher IL-10 and CD68, and relatively lower CD86 and IL1-β). Collectively, these findings suggest that tumor ECM served as a permissive role in OSCC progression. SIGNIFICANCE: The variation between OSCC ECM and normal ECM confirm tumor ECM plays a significant role in OSCC deterioration, which is conducive to exploring the occurrence and progression mechanisms of OSCC, and further improving the curative effect of this disease.

miR-425 regulates lipophagy via SIRT1 to promote sorafenib resistance in liver cancer

Liver cancer is one of the most malignant cancer, with poor outcomes and a high incidence rate, and current treatment approaches to prevent tumor progression and development remain unsatisfactory. Therefore, it is urgent to explore novel methods to inhibit tumor growth and metastasis. Autophagy is a highly conserved process associated with metastasis and drug resistance. Lipids are selectively recognized and degraded via autophagy; thus, autophagy is a crucial process to maintain tumor self-protection. MicroRNA (miR)-425 is a tumor-associated gene involved in liver cancer development that can induce cell proliferation and drug resistance. Using Cell Counting Kit-8 assays, western blot analysis and immunofluorescence assays, the present study revealed that inhibition of miR-425 promoted lipophagy by mediating the autophagy process, which in turn helps to promote sorafenib resistance. Using a bioinformatics website, it was revealed that autophagy promoted lipophagy by targeting silent information regulator 2 homolog 1 (SIRT1). The results of luciferase reporter assays supported this finding, and rescue experiments provided additional evidence. Overall, the current results suggested that inhibition of miR-425 expression increased SIRT1 expression to promote lipophagy, leading to the inhibition of liver cancer cell proliferation.