The immune-related biomarker TEK inhibits the development of clear cell renal cell carcinoma (ccRCC) by regulating AKT phosphorylation

Based on the prognosis model of immunogenes, the prognosis model was constructed to predict the invasion of immune genes and immune cells related to primary liver cancer and its experimental validation

Background

Primary liver cancer (PLC) is a prevalent malignancy of the digestive system characterized by insidious symptom onset and a generally poor prognosis. Recent studies have highlighted a significant correlation between the initiation and prognosis of liver cancer and the immune function of PLC patients.

Purpose

Revealing the expression of PLC-related immune genes and the characteristics of immune cell infiltration provides assistance for the analysis of clinical pathological parameters and prognosis of PLC patients.

Methods

PLC-related differentially expressed genes (DEGs) with a median absolute deviation (MAD > 0.5) were identified from TCGA and GEO databases. These DEGs were intersected with immune-related genes (IRGs) from the ImmPort database to obtain PLC-related IRGs. The method of constructing a prognostic model through immune-related gene pairs (IRGPs) is used to obtain IRGPs and conduct the selection of central immune genes. The central immune genes obtained from the selection of IRGPs are validated in PLC. Subsequently, the relative proportions of 22 types of immune cells in different immune risk groups are evaluated, and the differential characteristics of PLC-related immune cells are verified through animal experiments.

Results

Through database screening and the construction of an IRGP prognosis model, 84 pairs of IRGPs (P < 0.001) were ultimately obtained. Analysis of these 84 IRGPs revealed 11 central immune genes related to PLC, showing differential expression in liver cancer tissues compared to normal liver tissues. Results from the CiberSort platform indicate differential expression of immune cells such as naive B cells, macrophages, and neutrophils in different immune risk groups. Animal experiments demonstrated altered immune cell proportions in H22 tumor-bearing mice, validating findings from peripheral blood and spleen homogenate analyses.

Conclusion

Our study successfully predicted and validated PLC-related IRGs and immune cells, suggesting their potential as prognostic indicators and therapeutic targets for PLC.

The cell death-related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatocellular malignancy. While previous investigations have evidenced a robust link with programmed cell death (PCD) and tumorigenesis, a comprehensive inquiry targeting the relationship between multiple PCDs and HCC remains scant. Our aim was to develop a predictive model for different PCD patterns in order to investigate their impact on survival rates, prognosis and drug response rates in HCC patients. We performed functional annotation and pathway analysis on identified PCD-related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GEO. Consensus clustering analysis was utilized to elucidate the correlation between diverse PCD clusters and pertinent clinical characteristics. To comprehensively uncover the distinct PCD regulatory patterns, our analysis integrated gene expression profiling, immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients, we established a PCD model. To enhance the clinical applicability for the model, we developed a highly accurate nomogram. To address the treatment of HCC, we identified several promising chemotherapeutic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC patients, we conducted an analysis of 12 different PCD mechanisms using eligible data obtained from public databases. Through this analysis, we were able to identify 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 1254 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was based on the expression patterns of the genes in the different clusters. Patients in PCD cluster B had better survival probabilities. This suggests that PCD mechanisms, as represented by the genes in cluster B, may have a protective effect against HCC progression. Furthermore, the expression of PCDRGs was significantly higher in PCD cluster A, indicating that this cluster may be more closely associated with PCD mechanisms. Furthermore, our observations indicate that patients exhibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortality, in comparison to those displaying low TMB and low-risk statuses, who are more likely to experience prolonged survival. In addition, we have investigated the potential distinctions in the susceptibility of diverse risk cohorts towards emerging targeted therapies, designed for the treatment of HCC. Moreover, our investigation has shown that AZD2014, SB505124, LJI308 and OSI-207 show a greater efficacy in patients in the low-risk category. Conversely, for the high-risk group patients, PD173074, ZM447439 and CZC24832 exhibit a stronger response. Our findings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furthermore, significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery underscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for those of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivity in HCC patients, driving a more personalized and efficacious treatment paradigm, elevating clinical outcomes. Nonetheless, additional research endeavours are required to confirm the model's precision and assess its potential to inform clinical decision-making for HCC patients.

Development and validation of a combined hypoxia- and metabolism-related prognostic signature to predict clinical prognosis and immunotherapy responses in clear cell renal cell carcinoma

Background

Hypoxia and metabolism are closely correlated with the progression of cancer. We aimed to construct a combined hypoxia- and metabolism-related genes (HMRGs) prognostic signature to predict survival and immunotherapy responses in patients with clear cell renal cell carcinoma (ccRCC).

Methods

The RNA-seq profiles and clinical data of ccRCC were acquired from the TCGA and the ArrayExpress (E-MTAB-1980) databases. Least absolute shrinkage and selection operator (LASSO) and univariate and multivariate Cox regression analyses were applied to establish a prognostic signature. The E-MTAB-1980 cohort was selected for validation. The effectiveness and reliability of the signature were further evaluated by Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (ROC) curves. Further analyses, including functional enrichment, ssGSEA algorithm, CIBERSORT algorithm, and expression of immune checkpoints, were explored to investigate immune status and immunotherapy responses.

Results

We constructed a prognostic eight-gene signature with IRF6, TEK, PLCB2, ABCB1, TGFA, COL4A5, PLOD2, and TUBB6. Patients were divided into high-risk and low-risk groups based on the medium-risk score. The K-M analysis revealed that patients in the high-risk group had an apparently poor prognosis compared to those in the low-risk group in the TCGA (p < 0.001) and E-MTAB-1980 (p < 0.005). The area under ROC curve (AUC) of the prognostic signature was 0.8 at 1 year, 0.77 at 3 years, and 0.78 at 5 years in the TCGA, respectively, and was 0.82 at 1 year, 0.74 at 3 years, and 0.75 at 5 years in the E-MTAB-1980, respectively. Independent prognostic analysis confirmed the risk score as a separate prognostic factor in ccRCC patients (p < 0.001). The results of ssGSEA showed not only a high degree of immune cell infiltration but also high scores of immune-related functions in the high-risk group. The CIBERSORT analysis further confirmed that the abundance of immune cells was apparently different between the two risk groups. The risk score was significantly correlated with the expression of cytotoxic T lymphocyte-associated antigen-4 (CTLA4), lymphocyte-activation gene 3 (LAG3), and programmed cell death protein 1 (PD-1).

Conclusion

The HMRGs signature could be used to predict clinical prognosis, evaluate the efficacy of immunotherapy, and guide personalized immunotherapy in ccRCC patients.

Comprehensive analyses of genome-wide methylation and RNA epigenetics identify prognostic biomarkers, regulating the tumor immune microenvironment in lung adenocarcinoma

The aim of our study was to identify a signature of immune-regulated molecules and reveal its prognostic role in lung adenocarcinoma (LUAD). We downloaded RNA-Sequencing data and DNA methylation data from the Gene Expression Omnibus (GEO) database. GEO2R was used to analyze differentially expressed mRNAs (DEmRNAs). we used "factoextra" R package to do the principal component analysis (PCA) of DEmRNAs. "Limma" R package was used to identify DEmRNAs, differentially expressed miRNAs (DEmiRNAs), differentially expressed lncRNAs (DElncRNAs) from The Cancer Genome Atlas (TCGA) database. Three R packages "org.Hs.eg.db", "clusterProfiler", "ggplot2″ were used to show enrichment results. Considering about methylation and mutation data, TEK and SOX17 mediated cancer signaling pathways. Through tumor-immune system interactions database (TISIDB) and Tumor Immune Estimation Resource (TIMER), higher methylated and lower expressed TEK may act as a prognostic marker, regulating the tumor immunity in LUAD. Through four databases (MEXPRESS, DNMIVD, MethSurv, Firehose), we further verified the methylation (P = 2.33e-23) and mutation about TEK. A signature of immune-associated TEK to predict survival of LUAD patients was validated. Prognostic, methylation, immune microenvironment analysis showed new light on potential novel therapeutic targets in LUAD.

A Novel Lymphangiogenesis-Related Gene Signature can Predict Prognosis and Immunosuppressive Microenvironment in Patients with Clear Cell Renal Cell Carcinoma

Background: Lymphangiogenesis represents a key event in the progression and metastasis of patients with clear cell renal cell carcinoma (ccRCC). Nevertheless, the prognostic value of lymphangiogenesis-related genes (LRGs) in ccRCC patients remains unknown. Method: Differential analyses were performed to identify differentially expressed LRGs between normal and tumor tissues. A univariate Cox analysis was performed to identify differently expressed LRGs associated with overall survival (OS). LASSO and multivariate Cox analyses were performed to construct and optimize the LRG signature. To further explore the molecular characterization of the LRG signature, a functional enrichment analysis, immune signature, somatic mutations, and drug sensitivity were assessed. Immunohistochemistry (IHC) and immunofluorescence staining were performed to validate the relationship between lymphangiogenesis and immunity using our ccRCC samples. Results: Four candidate genes (IL4, CSF2, PROX1, and TEK) were eventually available to construct the LRG signature in the training set. Patients in the high-risk group had a shorter survival than those in the low-risk group. The LRG signature was an independent prognostic factor of OS. These results were confirmed in the validation group. The LRG signature was correlated with immunosuppressive cell infiltration, T cell exhaustion markers, somatic mutations, and drug sensitivity. The IHC and immunofluorescence staining results confirmed the correlation between lymphangiogenesis and CD163⁺ macrophages, exhausted CD8⁺PD-1⁺, and CD8⁺ LAG3⁺ T cells. Conclusion: A novel prognostic signature based on LRGs could provide insight into the prognostic evaluation and treatment of ccRCC patients.

Circ_0001058 represses the progression of lung adenocarcinoma through governing of the miR-486-5p/TEK signaling axis

The most common type of lung cancer is lung adenocarcinoma. Emerging views believe that circular RNA (circRNA) participates in its pathogenesis. The objective of this study is to find out the potential functions and mechanisms of circ_0001058 in lung adenocarcinoma pathogenesis. To detect circ_0001058, miR-486-5p and TEK tyrosine kinase (TEK) receptor tyrosine kinase expressions, real-time quantitative PCR (RT-qPCR) and western blotting were performed. Cell functions, including proliferation, apoptosis and invasion, were then evaluated using cell counting kit-8, caspase-3 activity and transwell assays, respectively. To establish the role of circ_0001058 in tumorigenesis, nude mice were utilized as in-vivo models. The predicted binding relationships of miR-486-5p to circ_0001058 or TEK were further verified by performing a dual-luciferase assay and ribonucleoprotein immunoprecipitation (RIP) analysis. Decreased circ_0001058 expression was observed in lung adenocarcinoma cells and tissue specimens. Circ_0001058 was predominantly situated in the cytoplasm and was greatly resistant to RNase R digestion. Circ_0001058 overexpression restrained A549 and PC9 cells' abilities to proliferate, survive and invade, and it also repressed tumorigenesis in the animal models. Circ_0001058 directly targeted miR-486-5p and depleted its expression. Restoring miR-486-5p could invert the inhibitory effects of circ_0001058 in the cancer cell phenotypes. Furthermore, miR-486-5p targeted TEK, so the inhibitory effects of TEK overexpression on the malignant behaviors of A549 and PC9 cells could also be abolished by miR-486-5p restoration. Circ_0001058 overexpression blocked the malignant development of lung adenocarcinoma via modulation of the miR-486-5p/TEK pathway. These results contribute new insights on the pathogenesis of lung adenocarcinoma.

Multi-omics analysis revealed TEK and AXIN2 are potential biomarkers in multifocal papillary thyroid cancer

BACKGROUND

Papillary thyroid carcinoma (PTC), the most common endocrine cancer, accounts for 80-85% of all malignant thyroid tumors. This study focused on identifying targets that affect the multifocality of PTC. In a previous study, we determined 158 mRNAs related to multifocality in BRAF-mutated PTC using The Cancer Genome Atlas.

METHODS

We used multi-omics data (miRNAs and mRNAs) to identify the regulatory mechanisms of the investigated mRNAs. miRNA inhibitors were used to determine the relationship between mRNAs and miRNAs. We analyzed the target protein levels in patient sera using ELISA and immunohistochemical staining of patients' tissues.

RESULTS

We identified 44 miRNAs that showed a negative correlation with mRNA expression. Using in vitro experiments, we identified four miRNAs that inhibit TEK and/or AXIN2 among the target mRNAs. We also showed that the downregulation of TEK and AXIN2 decreased the proliferation and migration of BRAF ( +) PTC cells. To evaluate the diagnostic ability of multifocal PTC, we examined serum TEK or AXIN2 in unifocal and multifocal PTC patients using ELISA, and showed that the serum TEK in multifocal PTC patients was higher than that in the unifocal PTC patients. The immunohistochemical study showed higher TEK and AXIN2 expression in multifocal PTC than unifocal PTC.

CONCLUSIONS

Both TEK and AXIN2 play a potential role in the multifocality of PTC, and serum TEK may be a diagnostic marker for multifocal PTC.

DLK2 Acts as a Potential Prognostic Biomarker for Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis

Clear cell renal cell carcinoma (ccRCC) is the most common RCC subtype with a high mortality. It has been reported that delta-like 1 homologue (DLK1) participates in the tumor microenvironmental remodeling of ccRCC, but the relationship between delta-like 2 homologue (DLK2, a DLK1 homologue) and ccRCC is still unclear. Thus, this study aims to investigate the role of DLK2 in the biological function and disease prognosis of ccRCC using bioinformatics analysis. The TNMplot database showed that DLK2 was upregulated in ccRCC tissues. From the UALCAN analysis, the overexpression of DLK2 was associated with advanced stage and high grade in ccRCC. Moreover, the Kaplan-Meier plotter (KM Plotter) database showed that DLK2 upregulation was associated with poor survival outcome in ccRCC. By the LinkedOmics analysis, DLK2 signaling may participated in the modulation of ccRCC extracellular matrix (ECM), cell metabolism, ribosome biogenesis, TGF-β signaling and Notch pathway. Besides, Tumor Immune Estimation Resource (TIMER) analysis showed that the macrophage and CD8⁺ T cell infiltrations were associated with good prognosis in ccRCC patients. Finally, DLK2 overexpression was associated with the reduced macrophage recruitments and the M1–M2 polarization of macrophage in ccRCC tissues. Together, DLK2 may acts as a novel biomarker, even therapeutic target in ccRCC. However, this study lacks experimental validation, and further studies are required to support this viewpoint.

Construction and Verification of a Combined Hypoxia and Immune Index for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most aggressive malignancies in humans. Hypoxia-related genes are now recognized as a reflection of poor prognosis in cancer patients with cancer. Meanwhile, immune-related genes play an important role in the occurrence and progression of ccRCC. Nevertheless, reliable prognostic indicators based on hypoxia and immune status have not been well established in ccRCC. The aims of this study were to develop a new gene signature model using bioinformatics and open databases and to validate its prognostic value in ccRCC. The data used for the model structure can be accessed from The Cancer Genome Atlas database. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the hypoxia- and immune-related genes associated with prognostic risk, which were used to develop a characteristic model of prognostic risk. Kaplan-Meier and receiver-operating characteristic curve analyses were performed as well as independent prognostic factor analyses and correlation analyses of clinical characteristics in both the training and validation cohorts. In addition, differences in tumor immune cell infiltrates were compared between the high and low risk groups. Overall, 30 hypoxia- and immune-related genes were identified, and five hypoxia- and immune-related genes (EPO, PLAUR, TEK, TGFA, TGFB1) were ultimately selected. Survival analysis showed that the high-risk score on the hypoxia- and immune-related gene signature was significantly associated with adverse survival outcomes. Furthermore, clinical ccRCC samples from our medical center were used to validate the differential expression of the five genes in tumor tissue compared to normal tissue through quantitative real-time polymerase chain reaction (qRT-PCR). However, more clinical trials are needed to confirm these results, and future experimental studies must verify the potential mechanism behind the predictive value of the hypoxia- and immune-related gene signature.

External Validation of the Prognostic Value of an Immune-Associated Gene Panel for Clear Cell Renal Cell Carcinomas

Clear cell renal cell carcinomas (ccRCCs) are highly immune infiltrates, and many of them respond to immunotherapy with checkpoint inhibitors including anti-PD-L1 or anti-PD1 agents. However, the effect of immune genes on clinical outcomes in ccRCCs has not been fully studied. Here, we show in this study that an immune-associated gene panel has a prognostic value for clear cell renal cell carcinomas. We performed single-sample gene set enrichment analysis (ssGSEA) and cell type identification by estimating subsets of RNA transcripts (CIBERSORT) algorithms on patient-matched normal renal and RCC tissues to characterize two immunophenotypes and immunological characteristic subpopulations. Furthermore, LASSO Cox regression was applied to develop a novel prognosis-associated model for ccRCC patients based on an immune-gene panel. The results were verified by the Gene Expression Omnibus (GEO) dataset and coordinated with the clinicopathological characteristics of ccRCCs, along with genomic signatures. Finally, based on the above perspectives, we generated a nomogram with a high prognostic efficiency for ccRCC patients. Overall, this study offers a unique perspective that can contribute to improving the accuracy of prognosis prediction and treatment with immunotherapy.

Identification of Mast Cell-Based Molecular Subtypes and a Predictive Signature in Clear Cell Renal Cell Carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. Surgery is the preferred treatment option; however, the rate of distant metastasis is high. Mast cells in the tumor microenvironment promote or inhibit tumorigenesis depending on the cancer type; however, their role in KIRC is not well-established. Here, we used a bioinformatics approach to evaluate the roles of mast cells in KIRC.

Methods: To quantify mast cell abundance based on gene sets, a single-sample gene set enrichment analysis (ssGSEA) was utilized to analyze three datasets. Weighted correlation network analysis (WGCNA) was used to identify the genes most closely related to mast cells. To identify new molecular subtypes, the nonnegative matrix factorization algorithm was used. GSEA and least absolute shrinkage and selection operator (LASSO) Cox regression were used to identify genes with high prognostic value. A multivariate Cox regression analysis was performed to establish a prognostic model based on mast cell-related genes. Promoter methylation levels of mast cell-related genes and relationships between gene expression and survival were evaluated using the UALCAN and GEPIA databases.

Results: A prolonged survival in KIRC was associated with a high mast cell abundance. KIRC was divided into two molecular subtypes (cluster 1 and cluster 2) based on mast cell-related genes. Genes in Cluster 1 were enriched for various functions related to cancer development, such as the TGFβ signaling pathway, renal cell carcinoma, and mTOR signaling pathway. Based on drug sensitivity predictions, sensitivity to doxorubicin was higher for cluster 2 than for cluster 1. By a multivariate Cox analysis, we established a clinical prognostic model based on eight mast cell-related genes.

Conclusion: We identified eight mast cell-related genes and constructed a clinical prognostic model. These results improve our understanding of the roles of mast cells in KIRC and may contribute to personalized medicine.

Kidney cancer biomarkers and targets for therapeutics: survivin (BIRC5), XIAP, MCL-1, HIF1α, HIF2α, NRF2, MDM2, MDM4, p53, KRAS and AKT in renal cell carcinoma

The incidence of renal cell carcinoma (RCC) is increasing worldwide with an approximate 20% mortality rate. The challenge in RCC is the therapy-resistance. Cancer resistance to treatment employs multiple mechanisms due to cancer heterogeneity with multiple genetic and epigenetic alterations. These changes include aberrant overexpression of (1) anticancer cell death proteins (e.g., survivin/BIRC5), (2) DNA repair regulators (e.g., ERCC6) and (3) efflux pump proteins (e.g., ABCG2/BCRP); mutations and/or deregulation of key (4) oncogenes (e.g., MDM2, KRAS) and/or (5) tumor suppressor genes (e.g., TP5/p53); and (6) deregulation of redox-sensitive regulators (e.g., HIF, NRF2). Foci of tumor cells that have these genetic alterations and/or deregulation possess survival advantages and are selected for survival during treatment. We will review the significance of survivin (BIRC5), XIAP, MCL-1, HIF1α, HIF2α, NRF2, MDM2, MDM4, TP5/p53, KRAS and AKT in treatment resistance as the potential therapeutic biomarkers and/or targets in RCC in parallel with our analized RCC-relevant TCGA genetic results from each of these gene/protein molecules. We then present our data to show the anticancer drug FL118 modulation of these protein targets and RCC cell/tumor growth. Finally, we include additional data to show a promising FL118 analogue (FL496) for treating the specialized type 2 papillary RCC.