Multi-omics analysis of tumor angiogenesis characteristics and potential epigenetic regulation mechanisms in renal clear cell carcinoma

CLEC14A facilitates angiogenesis and alleviates inflammation in diabetic wound healing

BACKGROUND

Delayed wound healing is a serious complication of diabetic wounds, posing a significant challenge to the treatment of patients with diabetes. Diabetic wound healing is a complex dynamic process involving angiogenesis and inflammatory responses. Currently, there are limited targeted therapies to promote diabetic wound healing. This study aimed to reveal the role of CLEC14A in the process of diabetic wound healing, with the hope of identifying new therapeutic targets to accelerate the healing of diabetic wounds.

METHODS

In vivo, diabetic mice were generated by combined streptozotocin (STZ) and high-fat diet treatment. The wound healing model was established in wild-type and Clec14a-/- diabetic mice. The degree of wound healing, as well as angiogenesis and inflammation during the healing process, were evaluated through Hematoxylin and Eosin (H&E) staining, immunohistochemical staining, and immunofluorescence staining. In vitro, the angiogenic activities of Human Umbilical Vein Endothelial Cells (HUVECs) were assessed following treatment with high glucose and adenoviruses overexpressing CLEC14A, using scratch assays and tube formation assays. Interleukin-1β (IL-1β) and Tumor Necrosis Factor-α (TNF-α) were utilized to evaluate the levels of inflammation in HUVECs.

RESULTS

CLEC14A expression was suppressed in diabetic wounds. Deletion of the Clec14a inhibited angiogenesis and activated inflammatory responses in vivo. High-glucose treatment led to decreased CLEC14A expression, impaired angiogenic capacity, and elevated inflammatory levels in vitro. However, adenoviral-mediated overexpression of CLEC14A reversed the response induced by high glucose.

CONCLUSION

CLEC14A accelerates diabetic wound healing by promoting angiogenesis and reducing wound inflammation.

BOLA family genes are the drivers and potential biomarkers of survival in kidney renal clear cell carcinoma patients

OBJECTIVES

To analyze the diagnostic and prognostic potential of the BOLA gene family in kidney renal clear cell carcinoma (KIRC).

METHODS

This study is an observational study. The whole study was carried out at Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia from January to November 2023. As it involves in silico and in vitro methods, ethical consent was not required. Various in silico and molecular experimental techniques were employed in this study.

RESULTS

Significant up-regulation and hypomethylation of BOLA1, BOLA2, and BOLA3 were observed across 12 KIRC cell lines. Retrospective analysis of the cancer genome atlas program (TCGA)-KIRC cohorts confirmed hypomethylation of these genes in KIRC tissues. The cBioPortal analysis showed minimal genetic alterations, with amplification being the most common. Kaplan-Meier plotter data revealed that high BOLA1, BOLA2, and BOLA3 expression correlated with shorter overall survival and relapse-free survival in KIRC. Tumor-immune system interactions database analysis linked BOLA1 expression to immune and molecular subtypes. Gene silencing of BOLA1, BOLA2, and BOLA3 in 786-0 cells reduced cell growth and proliferation, enhancing wound healing capacity.

CONCLUSION

BOLA genes may serve as diagnostic and prognostic markers in KIRC, offering insights into therapeutic targets and disease progression.

Exploring the Immune Landscape of ccRCC: Prognostic Signatures and Therapeutic Implications

The tumour immunological microenvironment is involved in the development of clear cell renal cell carcinoma (ccRCC). Nevertheless, the role of the immunological microenvironment in ccRCC has not been thoroughly investigated. In this study, we combined six ccRCC cohorts into a large cohort and quantified the expression matrix into 53 immunological terms using the ssGSEA algorithm. Five immune terms related to prognosis were screened through 1000 iterations of L1-penalised (lasso) estimation and Cox regression analysis for immune-related risk score (IRS) calculation. The IRS showed satisfactory prognosis prediction efficacy in ccRCC. We then compared the clinical and genomic characteristics of two IRS subgroups. Patients with low IRS showed a high level of tumour mutational burden (TMB) and a low level of copy number variation (CNV), indicating that low IRS group patients have a higher probability of responding to immunotherapy. We employed TIDE and subclass mapping analyses to corroborate our results, and the findings demonstrated that patients with a low IRS had a significantly greater percentage of immunotherapy response. According to the Genomics of Drug Sensitivity in Cancer (GDSC), patients with a high IRS had a decreased IC50 for sunitinib, which is the first-line treatment for ccRCC patients. As a result, the immune characteristics of the microenvironment of ccRCC tumours have been explored, and a signature has been constructed. Analysis demonstrated that our signature could effectively predict prognosis and immunotherapy response rate.

Integrative analysis of single-cell and bulk multi-omics data to reveal subtype-specific characteristics and therapeutic strategies in clear cell renal cell carcinoma patients

Background: Kidney renal clear cell carcinoma (KIRC) is the most prevalent subtype of malignant renal cell carcinoma and is well known as a common genitourinary cancer. Stratifying tumors based on heterogeneity is essential for better treatment options. Methods: In this study, consensus clusters were constructed based on gene expression, DNA methylation, and gene mutation data, which were combined with multiple clustering algorithms. After identifying two heterogeneous subtypes, we analyzed the molecular characteristics, immunotherapy response, and drug sensitivity differences of each subtype. And we further integrated bulk data and single-cell RNA sequencing (scRNA-Seq) data to infer the immune cell composition and malignant tumor cell proportion of subtype-related cell subpopulations. Results: Among the two identified consensus subtypes (CS1 and CS2), CS1 was enriched in more inflammation-related and oncogenic pathways than CS2. Simultaneously, CS1 showed a worse prognosis and we found more copy number variations and BAP1 mutations in CS1. Although CS1 had a high immune infiltration score, it exhibited high expression of suppressive immune features. Based on the prediction of immunotherapy and drug sensitivity, we inferred that CS1 may respond poorly to immunotherapy and be less sensitive to targeted drugs. The analysis of bulk data integrated with single-cell data further reflected the high expression of inhibitory immune features in CS1 and the high proportion of malignant tumor cells. And CS2 contained a large number of plasmacytoid B cells, presenting an activated immune microenvironment. Finally, the robustness of our subtypes was successfully validated in four external datasets. Conclusion: In summary, we conducted a comprehensive analysis of multi-omics data with 10 clustering algorithms to reveal the molecular characteristics of KIRC patients and validated the relevant conclusions by single-cell analysis and external data. Our findings discovered new KIRC subtypes and may further guide personalized and precision treatments.

Tumor microenvironment governs the prognostic landscape of immunotherapy for head and neck squamous cell carcinoma: A computational model-guided analysis

Immune checkpoint inhibition (ICI) has emerged as a critical treatment strategy for squamous cell carcinoma of the head and neck (HNSCC) that halts the immune escape of the tumor cells. Increasing evidence suggests that the onset, progression, and lack of/no response of HNSCC to ICI are emergent properties arising from the interactions within the tumor microenvironment (TME). Deciphering how the diversity of cellular and molecular interactions leads to distinct HNSCC TME subtypes subsequently governing the ICI response remains largely unexplored. We developed a cellular-molecular model of the HNSCC TME that incorporates multiple cell types, cellular states, and transitions, and molecularly mediated paracrine interactions. An exhaustive simulation of the HNSCC TME network shows that distinct mechanistic balances within the TME give rise to the five clinically observed TME subtypes such as immune/non-fibrotic, immune/fibrotic, fibrotic only and immune/fibrotic desert. We predict that the cancer-associated fibroblast, beyond a critical proliferation rate, drastically worsens the ICI response by hampering the accessibility of the CD8+ killer T cells to the tumor cells. Our analysis reveals that while an Interleukin-2 (IL-2) + ICI combination therapy may improve response in the immune desert scenario, Osteopontin (OPN) and Leukemia Inhibition Factor (LIF) knockout with ICI yields the best response in a fibro-dominated scenario. Further, we predict Interleukin-8 (IL-8), and lactate can serve as crucial biomarkers for ICI-resistant HNSCC phenotypes. Overall, we provide an integrated quantitative framework that explains a wide range of TME-mediated resistance mechanisms for HNSCC and predicts TME subtype-specific targets that can lead to an improved ICI outcome.

Transcriptomics and epigenetic data integration learning module on Google Cloud

Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

HIGHLIGHTS

LINC00908 attenuates LUAD tumorigenesis through DEAD-box helicase 54

Lung adenocarcinoma (LUAD) is one of the leading causes of cancer-related death worldwide. We identified a specific long non-coding RNA (LncRNA), LINC00908, which was downregulated in LUAD tissues and associated with good outcome. LINC00908 inhibited glycolysis by regulating the expression of the DEAD-box helicase 54 (DDX54), which was screened by a nine-gene risk signature, where DDX54 showed a positive correlation with several glycolysis-related genes. Experimental verification confirmed that DDX54 regulated nine key glycolytic enzymes, thereby affecting the level of glycolysis in LUAD. Further, the expression of LINC00908 in LUAD tumorigenesis was modulated by a transcription factor, regulatory factor X2 (RFX2). The RFX2/LINC00908/DDX54 axis regulated LUAD tumor growth, migration, invasion, cell apoptosis and glycolysis both in vitro and in vivo. These results demonstrate that this axis may serve as a novel mediator in LUAD progress and offer a novel therapeutic target for more precise diagnosis and treatment of LUAD.

Multiple databases analyzed the prognosis prediction of renin secretion pathway-related genes in renal clear cell carcinoma and immunotherapy

Background

Clear cell renal cell carcinoma (ccRCC) is a malignant kidney tumour and its progression is associated with the renin secretion pathway, so this study aimed to develop a prognostic model based on renin secretion pathway-related genes.

Methods

First, 453 renin secretion pathway-related genes were acquired [|log fold change (FC)| >1.5, false discovery rate (FDR) <0.05] from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The data were combined and further screened for 188 genes associated with ccRCC prognosis (P<0.05) by univariate independent prognostic analysis. These genes were subjected to least absolute shrinkage and selection operator regression to identify potential prognostic genes to construct the prognostic model. The stability of the model was externally validated. Combined risk scores and clinical information were used to create nomograms to accurately reflect patient survival. The model-related genes were further mined for subsequent analysis.

Results

A prognostic model of six renin secretion pathway genes (IGFBP3, PLAUR, CHKB-CPT1B, HOXA13, CDH13, and CDC20) was developed. Its reliability in predicting disease prognosis was confirmed by survival analysis, receiver operating characteristic (ROC) curve analysis and a risk curve. The nomogram and calibration curve showed good accuracy. The immune-related analyses revealed that the low-risk group would benefit more from immunotherapy.

Conclusions

The prognostic model of ccRCC based on six renin secretion pathway-related genes can be used to guide the precise treatment of ccRCC patients.

A novel prognostic predictor of immune microenvironment and therapeutic response in clear cell renal cell carcinoma based on angiogenesis-immune-related gene signature

Background

Clear cell renal cell carcinoma (ccRCC), the most common type of RCC, typically produces no symptoms initially. Patients with ccRCC are at increased risk of developing advanced metastatic disease due to the absence of dependable and effective prognostic biomarkers. Therefore, it is particularly urgent to find optimal stratification of patients with ccRCC to distinguish the clinical benefits of different malignant degrees. Angiogenesis has a profound impact on the malignant behavior of renal cancer cells, and anti-angiogenic drugs have been applied to metastatic renal cancer patients. Moreover, immune function dysregulation is also a significant factor in tumorigenesis. We aim to construct a predictive model that combines angiogenesis and immune-related genes (AIRGs) to aid clinicians in predicting ccRCC prognosis.

Methods

We gathered transcriptome and clinicopathology data from two datasets, the E-MTAB-1980 dataset and the Cancer Genome Atlas (TCGA). We utilized consensus clustering to find new molecular subgroups. A predictive model for the prognosis of angiogenesis-immune-associated genes (AIRGs) was conducted by the lasso and multivariate Cox regression analysis. The signature's predictive ability was then tested in different datasets. Meticulous scrutiny and comprehensive assessment were undertaken, both internally and externally, to establish the prognostic model. Analyses of immunogenomics were carried out to examine the relationship between risk scores and clinical/immune features, including immune cell infiltration, genomic alterations, and response to targeted and immunotherapy therapy.

Results

Our prognostic signature, comprising 4 AIRGs, stood as an independent prognostic factor for ccRCC, while risk scores emerged as a novel indicator for forecasting overall survival. Risk scores exhibited significant associations with various immunophenotypic factors, such as oncogenic pathways, antitumor response, different immune cell infiltration, antitumor immunity, and response to targeted and immunotherapy therapy.

Conclusions

AIRGs-based prognostic prediction model could effectively predict immunotherapy responses and survival outcomes of ccRCC.

Identification of hypoxia-related gene signatures based on multi-omics analysis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and one of the malignancies with the highest incidence rate and mortality worldwide. Hypoxia is a typical feature of tumour microenvironment (TME), which affects the progression of LUAD from multiple molecular levels. However, the underlying molecular mechanisms behind LUAD hypoxia are not fully understood. In this study, we estimated the level of hypoxia by calculating a score based on 15 hypoxia genes. The hypoxia scores were relatively high in LUAD patients with poor prognosis and were bound up with tumour node metastasis (TNM) stage, tumour size, lymph node, age and gender. By comparison of high hypoxia score group and low hypoxia score group, 1820 differentially expressed genes were identified, among which up-regulated genes were mainly about cell division and proliferation while down-regulated genes were primarily involved in cilium-related biological processes. Besides, LUAD patients with high hypoxia scores had higher frequencies of gene mutations, among which TP53, TTN and MUC16 had the highest mutation rates. As for DNA methylation, 1015 differentially methylated probes-related genes were found and may play potential roles in tumour-related neurobiological processes and cell signal transduction. Finally, a prognostic model with 25 multi-omics features was constructed and showed good predictive performance. The area under curve (AUC) values of 1-, 3- and 5-year survival reached 0.863, 0.826 and 0.846, respectively. Above all, our findings are helpful in understanding the impact and molecular mechanisms of hypoxia in LUAD.

RFX2 promotes tumor cell stemness through epigenetic regulation of PAF1 in spinal ependymoma

PURPOSE

Spinal ependymoma (SE) is a rare tumor that is most commonly low-grade and tends to recur when complete tumor resection is not feasible. We investigated the molecular mechanism induces stem cell features in SE.

METHODS

Immunohistochemical staining was conducted to analyze the expression of RFX2 in tumor tissues of SE patients at different stages. The expression of tumor stemness markers (Netsin and CD133) was analyzed using western blot analysis and IF, and the efficiency of sphere formation in SE cells was analyzed. The biological activities of SE cells were analyzed by EdU proliferation assay, TUNEL, wound healing, and Transwell assays. The regulatory relationship of RFX2 on PAF1 was verified by ChIP-qPCR and the dual-luciferase assay. SE cells were injected into the spinal cord of nude mice for in vivo assays.

RESULTS

RFX2 was higher in the tumor tissues of SE-III patients than in the tumor tissues of SE-I patients. RFX2 knockdown reduced the expression of tumor stemness markers in SE cells and inhibited the sphere formation efficiency. Moreover, RFX2 knockdown ameliorated the malignant progression of SE in nude mice, as manifested by prolonged survival and alleviated SE tumor infiltration. RFX2 bound to the PAF1 promoter to induce its transcription. Overexpression of PAF1 overturned the effects of RFX2 knockdown on stem cell features and biological activities of SE cells, thereby reducing survival in mice.

CONCLUSIONS

RFX2 activates PAF1 transcription, which promotes tumor stemness of SE cells and leads to the malignant progression of SE.

ELF5 drives angiogenesis suppression though stabilizing WDTC1 in renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is a common malignant tumor of the urinary system. Angiogenesis is a main contributing factor for tumorigenesis. E74-like transcription factor 5 (ELF5) has been verified to participate in the progression of different cancers and can regulate angiogenesis. This study was aimed to explore the functions of ELF5 in RCC.

METHODS

Bioinformatics tools were used to predict the expression of ELF5 in RCC. RT-qPCR was applied for testing ELF5 expression in RCC cells. Cell behaviors were evaluated by colony formation, CCK-8, and transwell assays. The tube formation assay was used for determining angiogenesis. Methylation-specific PCR (MSP) was utilized for measuring the methylation level of ELF5 in RCC cells. ChIP and luciferase reporter assays were applied for assessing the binding of ELF5 and ubiquitin-specific protease 3 (USP3). Co-IP and GST pull-down were utilized for detecting the interaction of WD40 and tetratricopeptide repeats 1 (WDTC1) and USP3. Ubiquitination level of WDTC1 was determined by ubiquitination assay.

RESULTS

ELF5 was lowly expressed in RCC cells and tissues. High expression of ELF5 expression notably suppressed RCC cell proliferative, migratory, and invasive capabilities, and inhibited angiogenesis. The tumor growth in mice was inhibited by ELF5 overexpression. ELF5 was highly methylated in RCC samples, and DNA methyltransferases (DNMTs) can promote hypermethylation level of ELF5 in RCC cells. ELF5 was further proved to transcriptionally activate USP3 in RCC. Moreover, USP3 inhibited WDTC1 ubiquitination. ELF5 can promote USP3-mediated WDTC1 stabilization. Additionally, WDTC1 silencing reversed the functions of ELF5 overexpression on RCC progression.

CONCLUSION

Downregulation of ELF5 due to DNA hypermethylation inhibits RCC development though the USP3/WDTC1axis in RCC.

Investigating Mechanical Properties of Alkali-Activated Slag Cementitious Material for Load-Bearing Layer of Sandwich Panels

The research presented in this paper is about the mechanical properties of fiber-reinforced alkali-activated slag cementitious sandwich panels with different types and amounts of admixtures. The mechanical properties, drying shrinkage properties, and micro-morphology were used to determine the optimal ratio of the admixtures. The results show that the alkali-activated slag sandwich panels have the characteristics of light weight, high strength and excellent thermal insulation, and the factors such as magnesium oxide, expansion agent and solution temperature have significant influence on their mechanical properties and dry shrinkage. This paper provides a theoretical basis and experimental data for the preparation process and application of alkali-activated slag sandwich panels.

Integrated high-throughput analysis identifies super enhancers in metastatic castration-resistant prostate cancer

Background: Metastatic castration-resistant prostate cancer (mCRPC) is a highly aggressive stage of prostate cancer, and non-mutational epigenetic reprogramming plays a critical role in its progression. Super enhancers (SE), epigenetic elements, are involved in multiple tumor-promoting signaling pathways. However, the SE-mediated mechanism in mCRPC remains unclear. Methods: SE-associated genes and transcription factors were identified from a cell line (C4-2B) of mCRPC by the CUT&Tag assay. Differentially expressed genes (DEGs) between mCRPC and primary prostate cancer (PCa) samples in the GSE35988 dataset were identified. What's more, a recurrence risk prediction model was constructed based on the overlapping genes (termed SE-associated DEGs). To confirm the key SE-associated DEGs, BET inhibitor JQ1 was applied to cells to block SE-mediated transcription. Finally, single-cell analysis was performed to visualize cell subpopulations expressing the key SE-associated DEGs. Results: Nine human TFs, 867 SE-associated genes and 5417 DEGs were identified. 142 overlapping SE-associated DEGs showed excellent performance in recurrence prediction. Time-dependent receiver operating characteristic (ROC) curve analysis showed strong predictive power at 1 year (0.80), 3 years (0.85), and 5 years (0.88). The efficacy of his performance has also been validated in external datasets. In addition, FKBP5 activity was significantly inhibited by JQ1. Conclusion: We present a landscape of SE and their associated genes in mCPRC, and discuss the potential clinical implications of these findings in terms of their translation to the clinic.

Intestinal transit amplifying cells require METTL3 for growth factor signaling, KRAS expression, and cell survival

Intestinal epithelial transit amplifying cells are essential stem progenitors required for intestinal homeostasis, but their rapid proliferation renders them vulnerable to DNA damage from radiation and chemotherapy. Despite their critical roles in intestinal homeostasis and disease, few studies have described genes that are essential to transit amplifying cell function. We report that the RNA methyltransferase, METTL3, is required for survival of transit amplifying cells in the murine small intestine. Transit amplifying cell death after METTL3 deletion was associated with crypt and villus atrophy, loss of absorptive enterocytes, and uniform wasting and death in METTL3-depleted mice. Ribosome profiling and sequencing of methylated RNAs in enteroids and in vivo demonstrated decreased translation of hundreds of unique methylated transcripts after METTL3 deletion, particularly transcripts involved in growth factor signal transduction such as Kras. Further investigation confirmed a novel relationship between METTL3 and Kras methylation and protein levels in vivo. Our study identifies METTL3 as an essential factor supporting the homeostasis of small intestinal tissue via direct maintenance of transit amplifying cell survival. We highlight the crucial role of RNA modifications in regulating growth factor signaling in the intestine, with important implications for both homeostatic tissue renewal and epithelial regeneration.

Research Progress of Tumor Microenvironment Targeted Therapy for Clear Cell Renal Cell Carcinoma

Renal clear cell carcinoma (ccRCC) and the tumor microenvironment (TME) influence each other, leading to the tumor microenvironment that can guide the corresponding treatment. With the deepening of research, some treatment options have achieved good results, such as tyrosine kinase inhibitors, immune checkpoint inhibitors, and so on. As the link between TME and malignancy is constantly discovered, more targeted studies on different components of TME are increasing, and this targeted therapy is a new method for treating ccRCC, and also a current research hotspot. This review summarizes the characteristics of the ccRCC tumor microenvironment, the outcomes of different treatments, and some potential targets.

Computed tomography-detected extramural venous invasion-related gene signature: a potential negative biomarker of immune checkpoint inhibitor treatment in patients with gastric cancer

BACKGROUND

To investigate the association between computed tomography (CT)-detected extramural venous invasion (EMVI)-related genes and immunotherapy resistance and immune escape in patients with gastric cancer (GC).

METHODS

Thirteen patients with pathologically proven locally advanced GC who had undergone preoperative abdominal contrast-enhanced CT and radical resection surgery were included in this study. Transcriptome sequencing was multidetector performed on the cancerous tissue obtained during surgery, and EMVI-related genes (P value for association < 0.001) were selected. A single-sample gene set enrichment analysis algorithm was also used to divide all GC samples (n = 377) in The Cancer Genome Atlas (TCGA) database into high and low EMVI-immune related groups based on immune-related differential genes. Cluster analysis was used to classify EMVI-immune-related genotypes, and survival among patients was validated in TCGA and Gene Expression Omnibus (GEO) cohorts. The EMVI scores were calculated using principal component analysis (PCA), and GC samples were divided into high and low EMVI score groups. Microsatellite instability (MSI) status, tumor mutation burden (TMB), response rate to immune checkpoint inhibitors (ICIs), immune escape were compared between the high and low EMVI score groups. Hub gene of the model in pan-cancer analysis was also performed.

RESULTS

There were 17 EMVI-immune-related genes used for cluster analysis. PCA identified 8 genes (PCH17, SEMA6B, GJA4, CD34, ACVRL1, SOX17, CXCL12, DYSF) that were used to calculate EMVI scores. High EMVI score groups had lower MSI, TMB and response rate of ICIs, status but higher immune escape status. Among the 8 genes used for EMVI scores, CXCL12 and SOX17 were at the core of the protein-protein interaction (PPI) network and had a higher priority in pan-cancer analysis. Immunohistochemical analysis showed that the expression of CXCL12 and SOX17 was significantly higher in CT-detected EMVI-positive samples than in EMVI-negative samples (P < 0.0001).

CONCLUSION

A CT-detected EMVI gene signature could be a potential negative biomarker for ICIs treatment, as the signature is negatively correlated with TMB, and MSI, resulting in poorer prognosis.

TGFβ-derived immune modulatory vaccine: targeting the immunosuppressive and fibrotic tumor microenvironment in a murine model of pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is associated with very poor survival, making it the third and fourth leading cause of all cancer-related deaths in the USA and European Union, respectively. The tumor microenvironment (TME) in PDAC is highly immunosuppressive and desmoplastic, which could explain the limited therapeutic effect of immunotherapy in PDAC. One of the key molecules that contributes to immunosuppression and fibrosis is transforming growth factor-β (TGFβ). The aim of this study was to target the immunosuppressive and fibrotic TME in PDAC using a novel immune modulatory vaccine with TGFβ-derived peptides in a murine model of pancreatic cancer.

METHODS

C57BL/6 mice were subcutaneously inoculated with Pan02 PDAC cells. Mice were treated with TGFβ1-derived peptides (major histocompatibility complex (MHC)-I and MHC-II-restricted) adjuvanted with Montanide ISA 51VG. The presence of treatment-induced TGFβ-specific T cells was assessed by ELISpot (enzyme-linked immunospot). Changes in the immune infiltration and gene expression profile in tumor samples were characterized by flow cytometry, reverse transcription-quantitative PCR (RT-qPCR), and bulk RNA sequencing.

RESULTS

Treatment with immunogenic TGFβ-derived peptides was safe and controlled tumor growth in Pan02 tumor-bearing mice. Enlargement of tumor-draining lymph nodes in vaccinated mice positively correlated to the control of tumor growth. Analysis of immune infiltration and gene expression in Pan02 tumors revealed that TGFβ-derived peptide vaccine increased the infiltration of CD8⁺ T cells and the intratumoral M1/M2 macrophage ratio, it increased the expression of genes involved in immune activation and immune response to tumors, and it reduced the expression of myofibroblast-like cancer-associated fibroblast (CAF)-related genes and genes encoding fibroblast-derived collagens. Finally, we confirmed that TGFβ-derived peptide vaccine actively modulated the TME, as the ability of T cells to proliferate was restored when exposed to tumor-conditioned media from vaccinated mice compared with media from untreated mice.

CONCLUSION

This study demonstrates the antitumor activity of TGFβ-derived multipeptide vaccination in a murine tumor model of PDAC. The data suggest that the vaccine targets immunosuppression and fibrosis in the TME by polarizing the cellular composition towards a more pro-inflammatory phenotype. Our findings support the feasibility and potential of TGFβ-derived peptide vaccination as a novel immunotherapeutic approach to target immunosuppression in the TME.

Prognostic value and immunological characteristics of a novel cuproptosis-related long noncoding RNAs risk signature in kidney renal clear cell carcinoma

Background: Cuproptosis has been found as a novel cell death mode significantly associated with mitochondrial metabolism, which may be significantly associated with the occurrence and growth of tumors. LncRNAs take on critical significance in regulating the development of kidney renal clear cell carcinoma (KIRC), whereas the correlation between cuproptosis-related LncRNAs (CRLs) and KIRC is not clear at present. Therefore, this study built a prognosis signature based on CRLs, which can achieve accurate prediction of the outcome of KIRC patients. Methods: The TCGA database provided the expression profile information and relevant clinical information of KIRC patients. Univariate Cox, Lasso, and multivariate Cox were employed for building a risk signature based on CRLs. Kaplan-Meier (K-M) survival analysis and time-dependent receiver operating characteristic (ROC) curve were employed for the verification and evaluation of the reliability and accuracy of risk signature. Then, qRT-PCR analysis of risk LncRNAs was conducted. Finally, the possible effect of the developed risk signature on the microenvironment for tumor immunization was speculated in accordance with ssGSEA and ESTIMATE algorithms. Results: A prognosis signature composed of APCDD1L-DT, MINCR, AL161782.1, and AC026401.3 was built based on CRLs. As revealed by the results of the K-M survival study, the OS rate and progression-free survival rate of highrisk KIRC patients were lower than those of lowrisk KIRC patients, and the areas under ROC curves of 1, 3, and 5 years were 0.828, 0.780, and 0.794, separately. The results of the immune analysis showed that there were significant differences in the status of immunization and the microenvironment of tumor between groups at low-risk and at high-risk. The qRT-PCR results showed that the relative expression level of MINCR and APCDD1L-DT were higher in 786-O and 769-P tumor cells than in HK-2 cells, which were normal renal tubular epithelial cells. Conclusion: The developed risk signature takes on critical significance in the prediction of the prognosis of patients with KIRC, and it can bring a novel direction for immunotherapy and clinical drug treatment of KIRC. In addition, 4 identified risk LncRNAs (especially APCDD1L-DT and MINCR) can be novel targets for immunotherapy of KIRC patients.

Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma

We aimed to develop and validate a pyradiomics model for preoperative prediction of initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). To this end, computed tomography (CT) images were acquired from multi-centers. Numerous pyradiomics features were extracted and machine learning approach was used to build a model for predicting initial response of TACE treatment. The predictive accuracy, overall survival (OS), and progression-free survival (PFS) were analyzed. Gene Set Enrichment Analysis (GSEA) was further used to explore signaling pathways in The Cancer Genome Atlas (TCGA)-HCC cohort. Overall, 24 of the 1,209 pyradiomic features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. The pyradiomics signature showed high predictive accuracy across the discovery set (AUC: 0.917, 95% confidence interval [CI]: 86.93-96.39), validation set 1 (AUC: 0.902, 95% CI: 84.81-95.59), and validation set 2 (AUC: 0.911; 95% CI: 83.26-98.98). Based on the classification of pyradiomics model, we found that a group with high values base on pyramidomics score showed good PFS and OS (both P<0.001) and was negatively correlated with glycolysis pathway. The proposed pyradiomics signature could accurately predict initial treatment response and prognosis, which may be helpful for clinicians to better screen patients who are likely to benefit from TACE.

CDH13 is a prognostic biomarker and a potential therapeutic target for patients with clear cell renal cell carcinoma

CDH13 is an atypical member of the cadherin family and is closely related to the clinicopathological factors and prognosis of many types of cancer. However, the role of CDH13 in clear cell renal cell carcinoma (ccRCC) remains unknown. Therefore, we comprehensively analyzed the expression level, diagnostic efficacy, clinical significance, prognostic value, immune infiltration, methylation status, genetic alteration, and biological functions of CDH13 in ccRCC patients. The results showed that CDH13 was significantly upregulated in ccRCC and strongly correlated with better survival, lower cancer stages, and lower tumor grades of ccRCC patients. Additionally, the immune infiltration analysis indicated that CDH13 might play a crucial role in regulating the tumor microenvironment of ccRCC. The results of methylation analysis showed that the epigenetic status of CDH13 was altered, and the prognosis of ccRCC patients was related not only to DNA methylation but also to m6A modification of CDH13. Finally, the results based on clinical samples further elucidated the expression pattern of CDH13 in ccRCC. In conclusion, CDH13 might be a novel prognostic biomarker and therapeutic target for patients with ccRCC. And our study provides new insights into the potential molecular changes and strategies for the treatment of ccRCC.

The clinical significance, immune infiltration, and tumor mutational burden of angiogenesis-associated lncRNAs in kidney renal clear cell carcinoma

Background

Poor prognosis of kidney renal clear cell carcinoma (KIRC) is often related to angiogenesis. The lncRNAs that regulate angiogenesis could also affect the prognosis of KIRC. It is meaningful for us to use lncRNAs related to angiogenesis to construct a generic, individualized prognostic signature for patients with KIRC.

Methods

We identified eight angiogenesis-associated genes (AAGs) by differential expression analysis and univariate Cox regression from The Cancer Genome Atlas dataset, including 537 KIRC samples and 72 normal samples. In total, 23 prognostic lncRNAs were screened out after Pearson correlation analysis and univariate Cox regression analysis. Then, we performed least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression to establish a four-AAG-related lncRNA prognostic signature.

Results

The risk score was calculated for each KIRC patients by using a four-AAG-related lncRNA prognostic signature. We divided the KIRC patients into high- and low-risk groups by the median of the risk score. It was confirmed that the AAG-related lncRNA prognostic signature has good prognostic value for KIRC patients by time-dependent receiver operating characteristic and Kaplan–Meier survival analysis. We identified 3,399 differentially expressed genes between the high- and low-risk groups and performed their functional enrichment analyses. The AAG-related lncRNA prognostic signature was an independent prognostic predictor for KIRC patients and was used to perform a combined nomogram. We reevaluated them in terms of survival, clinic characteristics, tumor-infiltrating immune cells and tumor mutation burden.

Conclusion

Our research indicates that the AAG-related lncRNA prognostic signature is a promising and potential independent prognostic indicator for KIRC patients. Then, it could offer new insights into the prognosis assessment and potential treatment strategies of KIRC patients.

The molecular mechanisms of CTHRC1 in gastric cancer by integrating TCGA, GEO and GSA datasets

Collagen triple helix repeat containing-1 (CTHRC1), highly expressed in multiple human solid tumors, has been identified as a tumor associated protein. However, its specific role and mechanism with immune infiltrates in gastric cancer are still unclear. In this study, we systematically explored and validated the expression and prognostic value of CTHRC1 in gastric cancer by integrating the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Genome Sequence Archive (GSA) datasets. Compared to adjacent normal tissues, we observed that CTHRC1 was highly overexpressed in tumor sample of multiple cancers. It was revealed that CTHRC1 overexpression was positively correlated with the T stage in gastric cancer but not lymph nodes metastasis from TCGA dataset. In addition, CTHRC1 expression may induce tumor associated macrophage infiltration though GRN/TNFRSF1A and AnxA1/FPR1 pathways and also tumor angiogenesis in gastric cancer. In this context, our results indicate that CTHRC1 plays a pivotal role in regulating the angiogenesis and macrophage infiltration in tumor microenvironment, and also can predict poor prognosis in gastric cancer, suggesting that CTHRC1 might be a promising novel immunotherapy and angiogenesis target for gastric cancer.

Bioinformatics evaluation of a novel angiogenesis related genes-based signature for predicting prognosis and therapeutic efficacy in patients with gastric cancer

OBJECTIVE

Tumor angiogenesis plays a pivotal role in the development and metastasis of tumors. This study aimed to elucidate the association between angiogenesis-related genes (ARGs) and the prognosis of patients with gastric cancer (GC).

METHODS

Transcriptomics and clinical data of GC samples were obtained from The Cancer Genome Atlas (TCGA) as the training group and those from Gene Expression Omnibus (GEO, including GSE26253, GSE26091 and GSE66229) as the validation groups. Single-sample gene set enrichment analysis (ssGSEA) was performed for gene set enrichment analysis on the gene set of angiogenesis and divided patients into high- or low-ARG group. Subsequently, to improve the availability of the ARG signature, a ARGs subtype predictor was then constructed by integrating of four machine learning methods, including support vector machine (SVM), least absolute shrinkage and selection operator (LASSO) regression, Random Forest and Boruta (RFB) and extreme gradient boosting (XGBoost). Kaplan-Meier and receiver operating characteristic curves were used to evaluate the performance of prognosis prediction. The EPIC and xCELL method were used to calculate the profile of tumor-infiltrated immune cells.

RESULTS

The expression levels of a total of 36 ARGs that correlated with the survival of patients with GC were identified and utilized to establish an ARG-related prognosis signature. The area under the curve for predicting overall survival (OS) in the training group at the 1-, 3- and 5-year was 0.61, 0.64 and 0.76, respectively, and this was further validated using three independent GEO datasets. Moreover, the ARG signatures were significantly correlated with cancer-associated fibroblasts (CAFs), and GC patients that exhibited both high ARG expression level and matrix CAFs level had the most inferior outcomes. The multiple machine learning algorithms were applied to establish a 10-gene ARG subtype predictor, and notably, a high ARG-subtype predictor score was associated with reduced efficacy of immunotherapy, and potential anti-HER2 or FGFR4 therapy, but an increased sensitivity to anti-angiogenesis-related therapy.

CONCLUSION

The novel ARGs-based classification may act as a potential prognostic predictor for GC and be used as a guidance for clinicians in selecting potential responders for immunotherapy and targeted therapy.

Construction of a Six-Gene Prognostic Risk Model Related to Hypoxia and Angiogenesis for Cervical Cancer

Background: The prognosis of cervical cancer (CC) is poor and not accurately reflected by the primary tumor node metastasis staging system. Our study aimed to develop a novel survival-prediction model.

Methods: Hallmarks of CC were quantified using single-sample gene set enrichment analysis and univariate Cox proportional hazards analysis. We linked gene expression, hypoxia, and angiogenesis using weighted gene co-expression network analysis (WGCNA). Univariate and multivariate Cox regression was combined with the random forest algorithm to construct a prognostic model. We further evaluated the survival predictive power of the gene signature using Kaplan-Meier analysis and receiver operating characteristic (ROC) curves.

Results: Hypoxia and angiogenesis were the leading risk factors contributing to poor overall survival (OS) of patients with CC. We identified 109 candidate genes using WGCNA and univariate Cox regression. Our established prognostic model contained six genes (MOCSI, PPP1R14A, ESM1, DES, ITGA5, and SERPINF1). Kaplan-Meier analysis indicated that high-risk patients had worse OS (hazard ratio = 4.63, p < 0.001). Our model had high predictive power according to the ROC curve. The C-index indicated that the risk score was a better predictor of survival than other clinicopathological variables. Additionally, univariate and multivariate Cox regressions indicated that the risk score was the only independent risk factor for poor OS. The risk score was also an independent predictor in the validation set (GSE52903). Bivariate survival prediction suggested that patients exhibited poor prognosis if they had high z-scores for hypoxia or angiogenesis and high risk scores.

Conclusions: We established a six-gene survival prediction model associated with hypoxia and angiogenesis. This novel model accurately predicts survival and also provides potential therapeutic targets.

Proteomic profiling of concurrently isolated primary microvascular endothelial cells, pericytes, and vascular smooth muscle cells from adult mouse heart

The microcirculation serves crucial functions in adult heart, distinct from those carried out by epicardial vessels. Microvessels are governed by unique regulatory mechanisms, impairment of which leads to microvessel-specific pathology. There are few treatment options for patients with microvascular heart disease, primarily due to limited understanding of underlying pathology. High throughput mRNA sequencing and protein expression profiling in specific cells can improve our understanding of microvessel biology and disease at the molecular level. Understanding responses of individual microvascular cells to the same physiological or pathophysiological stimuli requires the ability to isolate the specific cell types that comprise the functional units of the microcirculation in the heart, preferably from the same heart, to ensure that different cells have been exposed to the same in-vivo conditions. We developed an integrated process for simultaneous isolation and culture of the main cell types comprising the microcirculation in adult mouse heart: endothelial cells, pericytes, and vascular smooth muscle cells. These cell types were characterized with isobaric labeling quantitative proteomics and mRNA sequencing. We defined microvascular cell proteomes, identified novel protein markers, and confirmed established cell-specific markers. Our results allow identification of unique markers and regulatory proteins that govern microvascular physiology and pathology.

Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma

Purpose

The present study aimed to establish a hypoxia related genes model to predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data accessed from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database.

Methods

Patients’ data were downloaded from the TCGA and ICGC databases, and hypoxia related genes were accessed from the Molecular Signatures Database. The differentially expressed genes were evaluated and then the differential expressions hypoxia genes were screened. The TCGA cohort was randomly divided into a discovery TCGA cohort and a validation TCGA cohort. The discovery TCGA cohort was used for constructing the hypoxia genes risk model through Lasso regression, univariate and multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were used to assess the reliability and sensitivity of our model. Then, we established a nomogram to predict the probable one-, three-, and five-year overall survival rates. Lastly, the Tumor Immune Dysfunction and Exclusion (TIDE) score of patients was calculated.

Results

We established a six hypoxia-related gene prognostic model of KIRC patients in the TCGA database and validated in the ICGC database. The patients with high riskscore present poorer prognosis than those with low riskscore in the three TCGA cohorts and ICGC cohort. ROC curves show our six-gene model with a robust predictive capability in these four cohorts. In addition, we constructed a nomogram for KIRC patients in the TCGA database. Finally, the high risk-group had a high TIDE score than the patients with low riskscore.

Conclusions

We established a six hypoxia-related gene risk model for independent prediction of the prognosis of KIRC patients was established and constructed a robust nomogram. The different riskscores might be a biomarker for immunotherapy strategy.

Metabolic Understanding of the Genetic Dysregulation in the Tumor Microenvironment of Kidney Renal Clear Cell Carcinoma

The metabolic dysregulation is a hallmark of cancers including KIRC, specifically caused by alterations in metabolic genes. Currently, a lack of consensus exists between metabolic signatures in the tumor microenvironment. Here, in this study, we observed the significant correlations of differentially expressed metabolic genes (DEmGs) between KIRC and the related normal samples. Briefly, we collected sets of metabolic genes through RNA-seq data of KIRC and normal tissues from TCGA, followed by the identification of KIRC-related DEmGs. Next, patients were classified into three clusters, and using WGCNA, we identified metabolic genes involved in the survival among different clusters. Furthermore, we investigated survival and clinical parameters along with immune infiltration in the clusters. At the same time, we constructed and validated a prediction model based on these DEmGs. These analyses revealed that the patients having high expression of DEmGs showed poor survival, while infiltration of less-immune cells was associated with the metastasis of KIRC. In the end, we identified NUDT1 as a hub gene as it showed significantly high expression in KIRC samples as well as associated with the survival and prognosis of the patients. Further analysis revealed the oncogenic role of NUDT1 in 786-O and ACHN cells. Thus, we conclude that NUDT1 could be a potential diagnostic and prognostic marker for KIRC.

Systems biology of angiogenesis signaling: Computational models and omics

Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases.

This article is categorized under:

Cardiovascular Diseases > Computational Models

Cancer > Computational Models

Angiogenesis-Related Molecular Subtypes and a Novel Prognostic Signature in Clear Cell Renal Cell Carcinoma Patients

Background

This study aimed to develop and validate a novel angiogenesis-related gene (ARG) signature and molecular subtypes by bioinformatics analysis.

Materials and Methods

The transcriptome data and clinical data were obtained from TCGA and ICGC database. We performed consensus clustering analysis to identify angiogenesis molecular subtypes for ccRCC. Univariate and multivariate Cox regression analyses were used to develop a novel ARG-related signature as a prognostic biomarker for ccRCC. Internal and external validation were then performed in TCGA and ICGC cohort, respectively.

Results

We identified a total of two angiogenesis molecular subtypes of ccRCC. The overall survival (OS) of subtype 1 ccRCC was significantly decreased compared with that of subtype 2 ccRCC (P=0.001). These two molecular subtypes have significantly different tumor microenvironment and immune checkpoint inhibitor sensitivities (P<0.05). Besides, we developed a novel signature based on three ARGs (including MSX1, TIMP1 and JAG2) for subtype 1 ccRCC. The difference in OS between high- and low-risk group was statistically significant in training cohort (P=0.009), test cohort (P=0.024), the whole type 1 cohort (P<0.001), and validation cohort (P=0.041). The AUC for one-year OS prediction was 0.732, 0.710, 0.725, and 0.645 in training cohort, test cohort, the whole type 1 cohort, and validation cohort, respectively. Independent prognostic analysis showed that this signature was an independent predictor for OS of subtype 1 ccRCC (P=0.028914). The power of this prognostic signature was superior to other signatures reported in previous studies.

Conclusion

We developed and successfully validated a novel ARG signature for predicting prognosis of subtype 1 ccRCC, which was superior to several previous signatures.