CCR2 and PTPRC are regulators of tumor microenvironment and potential prognostic biomarkers of lung adenocarcinoma

Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer

INTRODUCTION

Cross-talk among biological pathways is essential for normal biological function and plays a significant role in cancer progression. Through integrated network analysis, this study explores the significance of pathway cross-talk in colorectal cancer (CRC) development at both the pathway and gene levels.

METHODS

In this study, we integrated the gene expression data with domain knowledge to construct state-dependent pathway cross-talk networks. The significance of the genes involved in pathway cross-talk was assessed by analyzing their association with cancer hallmarks, disease-gene relation, genetic alterations, and survival analysis. We also analyzed the gene regulatory network to identify the dysregulated genes and their role in CRC progression.

RESULTS

Cross-talk was observed between immune-related pathways and pathways associated with cell communication and signaling. The PTPRC gene was identified as a mediator, facilitating interactions within the immune system and other signaling pathways. The rewired interactions of ITGA7 were identified as influential in the epithelial-mesenchymal transition in CRC. This study also highlighted the crucial link between cell communication and vascular smooth muscle contraction pathway in CRC progression. The survival analysis of identified gene clusters showed their significant prognostic value in distinguishing high-risk from low-risk CRC groups, and L1000CDS2 revealed seven potential drug molecules in CRC. Nine dysregulated genes (CTNNB1, EP300, JUN, MYC, NFKB1, RELA, SP1, STAT1, and TP53) emerge as transcription factors acting as common regulators across various pathways.

CONCLUSIONS

This study highlights the crucial role of pathway cross-talk in CRC progression and identified the potential prognostic biomarkers and potential drug molecules.

Big data analysis identified a telomere-related signature predicting the prognosis and drug sensitivity in lung adenocarcinoma

Telomeres exert a critical role in chromosome stability and aberrant regulation of telomerase may result in telomeres dysfunction and genomic instability, which are involved in the occurrence of cancers. However, limited studies have been performed to fully clarify the immune infiltration and clinical significance of telomeres-related genes (TRGs) in lung adenocarcinoma (LUAD). The number of clusters of LUAD was determined by consensus clustering analysis. The prognostic signature was constructed and verified using TCGA and GSE42127 dataset with Least Absolute Shrinkage and Selection Operator cox regression analysis. The correlation between different clusters and risk-score and drug therapy response was analyzed using TIDE and IMvigor210 dataset. Using several miRNA and lncRNA related databases, we constructed a lncRNA-miRNA-mRNA regulatory axis. We identified 2 telomeres-related clusters in LUAD, which had distinct differences in prognostic stratification, TMB score, TIDE score, immune characteristics and signal pathways and biological effects. A prognostic model was developed based on 21 TRGs, which had a better performance in risk stratification and prognosis prediction compared with other established models. TRGs-based risk score could serve as an independent risk factor for LUAD. Survival prediction nomogram was also developed to promote the clinical use of TRGs risk score. Moreover, LUAD patients with high risk score had a high TMB score, low TIDE score and IC50 value of common drugs, suggesting that high risk score group might benefit from receiving immunotherapy, chemotherapy and target therapy. We also developed a lncRNA KCNQ1QT1/miR-296-5p/PLK1 regulatory axis. Our study identified 2 telomeres-related clusters and a prognostic model in LUAD, which could be helpful for risk stratification, prognosis prediction and treatment approach selection.

Identification of useful biomolecular markers in kidney renal clear cell carcinoma: an in silico and in vitro analysis-based study

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is the most prevalent type of renal cell carcinoma (RCC), with a high incidence and mortality rate. There is a lack of sensitive biomarkers. Therefore, the discovery of accurate biomarkers for KIRC patients is critical to improve prognosis.

METHODS

We determined hub genes and their associated pathways involved in the pathogenesis of KIRC from the GSE66272 dataset consisting of KIRC (n = 26) and corresponding control (n = 26) samples and later validated the expression and methylation level of the identified hub genes on The Cancer Genomic Atlas (TCGA) datasets and Human RCC 786-O and normal HK-2 cell lines through RNA sequencing (RNA-seq), Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and targeted bisulfite sequencing (bisulfite-seq) analyses.

RESULTS

The identified up-regulated four hub genes include TYROBP (Transmembrane Immune Signaling Adaptor TYROBP), PTPRC (Protein tyrosine phosphatase, receptor type, C), LCP2 (Lymphocyte cytosolic protein 2), and ITGB2 (Integrin Subunit Beta 2). Moreover, the higher expression of TYROBP, PTPRC, LCP2, and ITGB2 in KIRC patients insignificantly correlates with a poor prognosis in KIRC patients. In addition, hub genes were involved in the "Fc epsilon RI signaling pathway, asthma, natural cell killer mediated cytotoxicity, T cell receptor signaling pathway, primary immunodeficiency, Fc gamma R-mediated phagocytosis, malaria, leukocyte transendothelial migration, and legionellosis" pathways and associated with the infiltration level of CD8+ T, CD4+ T, and macrophage cells.

CONCLUSION

Our integrated in silico and in vitro analysis identified important hub genes (TYROBP, PTPRC, LCP2, and ITGB2) involved in the pathogenesis of KIRC as possible diagnostic biomarkers.

Revealing the role of regulatory T cells in the tumor microenvironment of lung adenocarcinoma: a novel prognostic and immunotherapeutic signature

Background

Regulatory T cells (Tregs), are a key class of cell types in the immune system. In the tumor microenvironment (TME), the presence of Tregs has important implications for immune response and tumor development. Relatively little is known about the role of Tregs in lung adenocarcinoma (LUAD).

Methods

Tregs were identified using but single-cell RNA sequencing (scRNA-seq) analysis and interactions between Tregs and other cells in the TME were investigated. Next, we used multiple bulk RNA-seq datasets to construct risk models based on marker genes of Tregs and explored differences in prognosis, mutational landscape, immune cell infiltration and immunotherapy between high- and low-risk groups, and finally, qRT-PCR and cell function experiments were performed to validate the model genes.

Results

The cellchat analysis showed that MIF-(CD74+CXCR4) pairs play a key role in the interaction of Tregs with other cell subpopulations, and the Tregs-associated signatures (TRAS) could well classify multiple LUAD cohorts into high- and low-risk groups. Immunotherapy may offer greater potential benefits to the low-risk group, as indicated by their superior survival, increased infiltration of immune cells, and heightened expression of immune checkpoints. Finally, the experiment verified that the model genes LTB and PTTG1 were relatively highly expressed in cancer tissues, while PTPRC was relatively highly expressed in paracancerous tissues. Colony Formation assay confirmed that knockdown of PTTG1 reduced the proliferation ability of LUAD cells.

Conclusion

TRAS were constructed using scRNA-seq and bulk RNA-seq to distinguish patient risk subgroups, which may provide assistance in the clinical management of LUAD patients.

The identification of key genes and pathways in glioblastoma by bioinformatics analysis

GBM is the most common and aggressive type of brain tumor. It is classified as a grade IV tumor by the WHO, the highest grade. Prognosis is generally poor, with most patients surviving only about a year. Only 5% of patients survive longer than 5 years. Understanding the molecular mechanisms that drive GBM progression is critical for developing better diagnostic and treatment strategies. Identifying key genes involved in GBM pathogenesis is essential to fully understand the disease and develop targeted therapies. In this study two datasets, GSE108474 and GSE50161, were obtained from the Gene Expression Omnibus (GEO) to compare gene expression between GBM and normal samples. Differentially expressed genes (DEGs) were identified and analyzed. To construct a protein-protein interaction (PPI) network of the commonly up-regulated and down-regulated genes, the STRING 11.5 and Cytoscape 3.9.1 were utilized. Key genes were identified through this network analysis. The GEPIA database was used to confirm the expression levels of these key genes and their association with survival. Functional and pathway enrichment analyses on the DEGs were conducted using the Enrichr server. In total, 698 DEGs were identified, consisting of 377 up-regulated genes and 318 down-regulated genes. Within the PPI network, 11 key up-regulated genes and 13 key down-regulated genes associated with GBM were identified. NOTCH1, TOP2A, CD44, PTPRC, CDK4, HNRNPU, and PDGFRA were found to be important targets for potential drug design against GBM. Additionally, functional enrichment analysis revealed the significant impact of Epstein-Barr virus (EBV), Cell Cycle, and P53 signaling pathways on GBM.

Unraveling the heterogeneity of cholangiocarcinoma and identifying biomarkers and therapeutic strategies with single-cell sequencing technology

Cholangiocarcinoma (CCA) is a common malignant tumor of the biliary tract that carries a high burden of morbidity and a poor prognosis. Due to the lack of precise diagnostic methods, many patients are often diagnosed at advanced stages of the disease. The current treatment options available are of varying efficacy, underscoring the urgency for the discovery of more effective biomarkers for early diagnosis and improved treatment. Recently, single-cell sequencing (SCS) technology has gained popularity in cancer research. This technology has the ability to analyze tumor tissues at the single-cell level, thus providing insights into the genomics and epigenetics of tumor cells. It also serves as a practical approach to study the mechanisms of cancer progression and to explore therapeutic strategies. In this review, we aim to assess the heterogeneity of CCA using single-cell sequencing technology, with the ultimate goal of identifying possible biomarkers and potential treatment targets.

Pan-cancer analysis identifies proteasome 26S subunit, ATPase (PSMC) family genes, and related signatures associated with prognosis, immune profile, and therapeutic response in lung adenocarcinoma

Background: Proteasome 26S subunit, ATPase gene (PSMC) family members play a critical role in regulating protein degradation and are essential for tumor development. However, little is known about the integrative function and prognostic significance of the PSMC gene family members in lung cancer. Methods: First, we assessed the expression and prognostic features of six PSMC family members in pan-cancer from The Cancer Genome Atlas (TCGA) dataset. Hence, by focusing on the relationship between PSMC genes and the prognostic, genomic, and tumor microenvironment features in lung adenocarcinoma (LUAD), a PSMC-based prognostic signature was established using consensus clustering and multiple machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO) Cox regression, CoxBoost, and survival random forest analysis in TCGA and GSE72094. We then validated it in three independent cohorts from GEO and estimated the correlation between risk score and clinical features: genomic features (alterations, tumor mutation burden, and copy number variants), immune profiles (immune score, TIDE score, tumor-infiltrated immune cells, and immune checkpoints), sensitivity to chemotherapy (GDSC, GSE42127, and GSE14814), and immunotherapy (IMvigor210, GSE63557, and immunophenoscore). Twenty-one patients with LUAD were included in our local cohort, and tumor samples were submitted for evaluation of risk gene and PD-L1 expression. Results: Nearly all six PSMC genes were overexpressed in pan-cancer tumor tissues; however, in LUAD alone, they were all significantly correlated with overall survival. Notably, they all shared a positive association with increased TMB, TIDE score, expression of immune checkpoints (CD276 and PVR), and more M1 macrophages but decreased B-cell abundance. A PSMC-based prognostic signature was established based on five hub genes derived from the differential expression clusters of PSMC genes, and it was used to dichotomize LUAD patients into high- and low-risk groups according to the median risk score. The area under the curve (AUC) values for predicting survival at 1, 3, and 5 years in the training cohorts were all >.71, and the predictive accuracy was also robust and stable in the GSE72094, GSE31210, and GSE13213 datasets. The risk score was significantly correlated with advanced tumor, lymph node, and neoplasm disease stages as an independent risk factor for LUAD. Furthermore, the risk score shared a similar genomic and immune feature as PSMC genes, and high-risk tumors exhibited significant genomic and chromosomal instability, a higher TIDE score but lower immune score, and a decreased abundance of B and CD8⁺ T cells. Finally, high-risk patients were suggested to be less sensitive to immunotherapy but had a higher possibility of responding to platinum-based chemotherapy. The LUAD samples from the local cohort supported the difference in the expression levels of these five hub genes between tumor and normal tissues and the correlation between the risk score and PD-L1 expression. Conclusion: Overall, our results provide deep insight into PSMC genes in LUAD, especially the prognostic effect and related immune profile that may predict therapeutic responses.

CC chemokine receptor 2 (CCR2) expression promotes diffuse large B-Cell lymphoma survival and invasion

In recent years, CC chemokine receptor 2 (CCR2) has been found to be involved in tumor growth, angiogenesis, epithelial mesenchymal transition, metastasis, and immune escape. CCR2 overexpression was first identified as a poor prognostic predictor in diffuse large B-cell lymphoma (DLBCL) in our published article, but the mechanisms involved remain unknown. In this work, we collected data from another 138 patients with DLBCL data and verified the CCR2 expression level and its relationship to clinicopathological characteristics. Furthermore, we explored the possible mechanisms via in vitro and in vivo experiments. We showed that CCR2 overexpression was an independent prognostic marker and predicted shorter overall survival (OS) and progression-free survival (PFS) in patients with DLBCL. Blockade of CCR2 expression with a CCR2 antagonist inhibited tumor cell proliferation, migration, and anti-apoptosis ability in vitro by affecting the PI3K/Akt signaling pathway and the p38 MAPK signaling pathway. Furthermore, administration of a CCR2 antagonist decreased tumor growth and dissemination of DLBCL cells and increased survival time in the xenograft model. Our study demonstrates that CCR2 expression plays an important role in the development of DLBCL by stimulating cell proliferation, migration, and anti-apoptosis. Therefore, the inhibition of CCR2 may be a potential target for anticancer therapy in DLBCL.

Proteomes of Residual Tumors in Curcumin-Treated Rats Reveal Changes in Microenvironment/Malignant Cell Crosstalk in a Highly Invasive Model of Mesothelioma

Curcumin exhibits both immunomodulatory properties and anticarcinogenic effects which have been investigated in different experimental tumor models and cancer types. Its interactions with multiple signaling pathways have been documented through proteomic studies on malignant cells in culture; however, in vivo approaches are scarce. In this study, we used a rat model of highly invasive peritoneal mesothelioma to analyze the residual tumor proteomes of curcumin-treated rats in comparison with untreated tumor-bearing rats (G1) and provide insights into the modifications in the tumor microenvironment/malignant cell crosstalk. The cross-comparing analyses of the histological sections of residual tumors from two groups of rats given curcumin twice on days 21 and 26 after the tumor challenge (G2) or four times on days 7, 9, 11 and 14 (G3), in comparison with G1, identified a common increase in caveolin-1 which linked with significant abundance changes affecting 115 other proteins. The comparison of G3 vs. G2 revealed additional features for 65 main proteins, including an increase in histidine-rich glycoprotein and highly significant abundance changes for 22 other proteins regulating the tumor microenvironment, linked with the presence of numerous activated T cells. These results highlight new features in the multiple actions of curcumin on tumor microenvironment components and cancer cell invasiveness.

The prognostic and immune infiltration role of ITGB superfamily members in non-small cell lung cancer

PURPOSE

We aimed to explore the prognostic value of integrin-β superfamily members (ITGBs) and their role in immune cell infiltration in non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

Study cases were acquired from The Cancer Genome Atlas database and The Human Protein Atlas. We then used R package and several online tools to analyze and visualize the roles of ITGBs in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).

RESULTS

We found that ITGBs were differentially expressed in NSCLC. In LUAD, high expression of ITGB1 and ITGB4 was an independent risk factor for poor prognosis, and ITGB7 was an independent protective factor for overall survival; in LUSC, high expression of ITGB1, 3, 5, and 6 was associated with poor prognosis, and ITGB8 was an independent protective factor for disease-specific survival. Protein-protein interaction networks for the most associated co-expressed genes revealed the following target genes of ITGBs: PTPRC, ITGAM, and ITGB2 in LUAD and FN1, PTPRC, and ITGB2 in LUSC. Gene ontology analysis revealed that functions related to adhesion, junction, and binding were highly enriched in LUAD and LUSC. ITGBs were significantly associated with immune cell infiltration and the expression of immunomodulation-related genes in LUAD and LUSC.

CONCLUSION

ITGBs were differentially expressed in NSCLC. ITGB1, 4, and 7 and ITGB1, 3, 5, 6, and 8 were found as prognostic markers in LUAD and LUSC, respectively. ITGBs were significantly associated with immune cell infiltration and the expression of immunomodulation-related genes.

Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer’s Disease Based on Least Absolute Shrinkage and Selection Operator

Neurodegenerative diseases such as Alzheimer’s disease (AD) are an increasing public health challenge. There is an urgent need to shift the focus to accurate detection of clinical AD at the physical examination stage. The purpose of this study was to identify biomarkers for AD diagnosis. Differential expression analysis was performed on a dataset including prefrontal cortical samples and peripheral blood samples of AD to identify shared differentially expressed genes (DEGs) shared between the two datasets. In addition, a minimum absolute contraction and selection operator (LASSO) model based on shared-DEGs identified nine signature genes (MT1X, IGF1, DLEU7, TRIM36, PTPRC, WNK2, SPG20, C8orf59, and BRWD1) that accurately predict AD occurrence. Enrichment analysis showed that the signature gene was significantly associated with the AD-related p53 signaling pathway, T-cell receptor signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. Thus, our results identify not only biomarkers for diagnosing AD but also potentially specific pathways. The AD biomarkers proposed in this study could serve as indicators for prevention and diagnosis during physical examination.

Bioinformatics Analysis of the Mechanisms of Diabetic Nephropathy via Novel Biomarkers and Competing Endogenous RNA Network

Diabetic nephropathy (DN) is one of the common chronic complications of diabetes with unclear molecular mechanisms, which is associated with end-stage renal disease (ESRD) and chronic kidney disease (CKD). Our study intended to construct a competing endogenous RNA (ceRNA) network via bioinformatics analysis to determine the potential molecular mechanisms of DN pathogenesis. The microarray datasets (GSE30122 and GSE30529) were downloaded from the Gene Expression Omnibus database to find differentially expressed genes (DEGs). GSE51674 and GSE155188 datasets were used to identified the differentially expressed microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), respectively. The DEGs between normal and DN renal tissues were performed using the Linear Models for Microarray (limma) package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to reveal the mechanisms of DEGs in the progression of DN. The protein-protein interactions (PPI) of DEGs were carried out by STRING database. The lncRNA-miRNA-messenger RNA (mRNA) ceRNA network was constructed and visualized via Cytoscape on the basis of the interaction generated through the miRDB and TargetScan databases. A total of 94 significantly upregulated and 14 downregulated mRNAs, 31 upregulated and 121 downregulated miRNAs, and nine upregulated and 81 downregulated lncRNAs were identified. GO and KEGG pathways enriched in several functions and expression pathways, such as inflammatory response, immune response, identical protein binding, nuclear factor kappa b (NF-κB) signaling pathway, and PI3K-Akt signaling pathway. Based on the analysis of the ceRNA network, five differentially expressed lncRNAs (DElncRNAs) (SNHG6, KCNMB2-AS1, LINC00520, DANCR, and PCAT6), five DEmiRNAs (miR-130b-5p, miR-326, miR-374a-3p, miR-577, and miR-944), and five DEmRNAs (PTPRC, CD53, IRF8, IL10RA, and LAPTM5) were demonstrated to be related to the pathogenesis of DN. The hub genes were validated by using receiver operating characteristic curve (ROC) and real-time PCR (RT-PCR). Our research identified hub genes related to the potential mechanism of DN and provided new lncRNA-miRNA-mRNA ceRNA network that contributed to diagnostic and potential therapeutic targets for DN.

Exploration of genes and tumor infiltrating lymphocytes in female lung adenocarcinoma microenvironment that predicted prognosis

The tumor microenvironment has an important impact on tumor growth, invasion, metastasis, anti-tumor immune tolerance, and prognosis. The present study aimed to explore female lung adenocarcinoma microenvironment-associated tumor infiltrating lymphocytes (TILs) and genes that predict prognosis in The Cancer Genome Atlas (TCGA) database. Gene expression profiles of female patients with lung adenocarcinoma were downloaded from TCGA. Base on the CIBERSORT algorithm, we determined the fractions of TILs. By applying the ESTIMATE algorithm, immune scores and stromal scores were derived. According to the immune and stromal scores, we categorized the female patients with lung adenocarcinoma into high and low score groups. We also identified the fractions of TILs and differentially expressed genes (DEGs) that were significantly related with prognosis. The proportion of M1 macrophages was significantly negatively related to overall survival in female patients with lung adenocarcinoma. There were 269 upregulated genes and 35 downregulated genes both in immune scores and stromal scores. PTPRC (protein tyrosine phosphatase receptor type C) and GIMAP6 (GTPase, IMAP family member 6) were not only hub genes, but also were significantly related to overall survival in the Kaplan-Meier Plotter online and TCGA databases. In summary, our study provided new insight into the tumor microenvironment-related cellular and molecular mechanisms of women with lung adenocarcinoma. The results will be useful for future clinical studies.