The Correlation Between the Immune and Epithelial-Mesenchymal Transition Signatures Suggests Potential Therapeutic Targets and Prognosis Prediction Approaches in Kidney Cancer

Multi-cohort validation: A comprehensive exploration of prognostic marker in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common form of RCC. It is characterized by resistance to traditional radiotherapy and chemotherapy, as well as an unfavorable clinical prognosis. Although TYMP is implicated in the advancement of tumor progression, the role of TYMP in ccRCC is still not understood. Heightened TYMP expression was identified in ccRCC through database mining and confirmed in RCC cell lines. Indeed, TYMP knockdown impacted RCC cell proliferation, migration, and invasion in vitro. TYMP showed a positive correlation with clinicopathological parameters (histological grade, pathological stage). Moreover, patients with high TYMP expression were indicative of poor prognosis in TCGA-ccRCC and external cohorts. The results of single-cell analysis showed that the distribution of TYMP was predominantly observed in monocytes and macrophages. Furthermore, there is a significant association between TYMP and immune status. Methylation analysis further elucidated the relationship between TYMP expression and multiple methylation sites. Drug sensitivity analysis unveiled potential pharmaceutical options. Additionally, mutation analyses identified an association between TYMP and the ccRCC driver genes like BAP1 and ROS1. In summary, TYMP may serve as a reliable prognostic indicator for ccRCC.

Abnormal expression of FOXM1 in carcinogenesis of renal cell carcinoma: From experimental findings to clinical applications

Renal cell carcinoma (RCC) is a prevalent malignancy within the genitourinary system. At present, patients with high-grade or advanced RCC continue to have a bleak prognosis. Mounting research have emphasized the significant involvement of Forkhead box M1 (FOXM1) in RCC development and progression. Therefore, it is imperative to consolidate the existing evidence regarding the contributions of FOXM1 to RCC tumorigenesis through a comprehensive review. This study elucidated the essential functions of FOXM1 in promoting RCC growth, invasion, and metastasis by regulating cell cycle progression, DNA repair, angiogenesis, and epithelial-mesenchymal transition (EMT). Also, FOXM1 might serve as a novel diagnostic and prognostic biomarker as well as a therapeutic target for RCC. Clinical findings demonstrated that the expression of FOXM1 was markedly upregulated in RCC samples, while a high level of FOXM1 was found to be associated with a poor overall survival rate of RCC. Furthermore, it is worth noting that FOXM1 may have a significant impact on the resistance of renal cell carcinoma (RCC) to radiotherapy. This observation suggests that inhibiting FOXM1 could be a promising strategy to impede the progression of RCC and enhance its sensitivity to radiotherapy. The present review highlighted the pivotal role of FOXM1 in RCC development. FOXM1 has the capacity to emerge as not only a valuable diagnostic and prognostic tool but also a viable therapeutic option for unresectable RCC.

A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients

Simple Summary

Clear cell renal cell carcinoma is a type of kidney cancer which comprises the majority of all renal cell carcinomas. Many efforts have been made to identify biomarkers which could help healthcare professionals better treat this kind of cancer. With extensive public data available, we conducted a machine learning study to determine a gene signature that could indicate patient survival with high accuracy. Through the min-Redundancy and Max-Relevance algorithm we generated a signature of 13 genes highly correlated with patient outcomes. These findings reveal potential strategies for personalized medicine in the clinical practice.

Abstract

Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.

Eosinophilic features in clear cell renal cell carcinoma correlate with outcomes of immune checkpoint and angiogenesis blockade

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) displays heterogeneity in appearance-a distinctive pale clear to eosinophilic cytoplasm; however, little is known about the underlying mechanisms and clinical implications. We investigated the role of these eosinophilic features in ccRCC on oncological outcomes and response to tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs).

METHODS

One-hundred and thirty-eight ccRCC cases undergoing radical surgery (cohort 1) and 54 metastatic ccRCC cases receiving either TKIs or ICIs (cohort 2) were included. After histological evaluation, all cases were divided into three phenotypes based on the eosinophilic features at the highest-grade area: clear, mixed, or eosinophilic type. Gene expression and immunohistochemical analyses were performed to explore the potential mechanisms of these phenotypes in cohort 1. Further, the association of the three phenotypes with the best objective response to TKI or ICI, clinical benefit (complete/partial response or stable disease), and overall survival (OS) was assessed in cohort 2.

RESULTS

The clear type was significantly associated with increased hypoxia as well as angiogenesis gene signatures compared with the eosinophilic type. Gene signatures and protein expression related to effector T cell and immune checkpoint molecules were elevated to a greater extent in the eosinophilic type, followed by the mixed and clear types. The mixed and eosinophilic types exhibited greater PBRM1-negativity and increased prevalence of the epithelial-mesenchymal transition gene signature than the clear type. In the mixed/eosinophilic types of cohort 2, significant clinical benefit was observed in the ICI therapy group versus the TKI therapy group (p=0.035), and TKI therapy vs ICI therapy was an independent factor for worse prognosis of OS (HR 3.236; p=0.012).

CONCLUSION

The histological phenotype based on the eosinophilic features, which are linked to major immunological mechanisms of ccRCC, was significantly correlated with therapeutic efficacy.

Heparan Sulfate Facilitates Spike Protein-Mediated SARS-CoV-2 Host Cell Invasion and Contributes to Increased Infection of SARS-CoV-2 G614 Mutant and in Lung Cancer

The severe acute respiratory syndrome (SARS)-like coronavirus disease (COVID-19) is caused by SARS-CoV-2 and has been a serious threat to global public health with limited treatment. Cellular heparan sulfate (HS) has been found to bind SARS-CoV-2 spike protein (SV2-S) and co-operate with cell surface receptor angiotensin-converting enzyme 2 (ACE2) to mediate SARS-CoV-2 infection of host cells. In this study, we determined that host cell surface SV2-S binding depends on and correlates with host cell surface HS expression. This binding is required for SARS-Cov-2 virus to infect host cells and can be blocked by heparin lyase, HS antagonist surfen, heparin, and heparin derivatives. The binding of heparin/HS to SV2-S is mainly determined by its overall sulfation with potential, minor contribution of specific SV2-S binding motifs. The higher binding affinity of SV2-S G614 mutant to heparin and upregulated HS expression may be one of the mechanisms underlying the higher infectivity of the SARS-CoV-2 G614 variant and the high vulnerability of lung cancer patients to SARS-CoV-2 infection, respectively. The higher host cell infection by SARS-CoV-2 G614 variant pseudovirus and the increased infection caused by upregulated HS expression both can be effectively blocked by heparin lyase and heparin, and possibly surfen and heparin derivatives too. Our findings support blocking HS-SV2-S interaction may provide one addition to achieve effective prevention and/treatment of COVID-19.

Development and Clinical Validation of a Seven-Gene Prognostic Signature Based on Multiple Machine Learning Algorithms in Kidney Cancer

About a third of patients with kidney cancer experience recurrence or cancer-related progression. Clinically, kidney cancer prognoses may be quite different, even in patients with kidney cancer at the same clinical stage. Therefore, there is an urgent need to screen for kidney cancer prognosis biomarkers. Differentially expressed genes (DEGs) were identified using kidney cancer RNA sequencing data from the Gene Expression Omnibus (GEO) database. Biomarkers were screened using random forest (RF) and support vector machine (SVM) models, and a multigene signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. Univariate and multivariate Cox regression analyses were performed to explore the relationships between clinical features and prognosis. Finally, the reliability and clinical applicability of the model were validated, and relationships with biological pathways were identified. Western blots were also performed to evaluate gene expression. A total of 50 DEGs were obtained by intersecting the RF and SVM models. A seven-gene signature (RNASET2, EZH2, FXYD5, KIF18A, NAT8, CDCA7, and WNT7B) was constructed by LASSO regression. Univariate and multivariate Cox regression analyses showed that the seven-gene signature was an independent prognostic factor for kidney cancer. Finally, a predictive nomogram was established in The Cancer Genome Atlas (TCGA) cohort and validated internally. In tumor tissue, RNASET2 and FXYD5 were highly expressed and NAT8 was lowly expressed at the protein and transcription levels. This model could complement the clinicopathological characteristics of kidney cancer and promote the personalized management of patients with kidney cancer.

A novel single-cell based method for breast cancer prognosis

Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.

Identification of potential key genes and key pathways related to clear cell renal cell carcinoma through bioinformatics analysis

Clear cell renal cell carcinoma (ccRCC) is a common malignancy of the genitourinary system and is associated with high mortality rates. However, the molecular mechanism of ccRCC pathogenesis is still unclear, which translates to few effective diagnostic and prognostic biomarkers. In this study, we conducted a bioinformatics analysis on three Gene Expression Omnibus datasets and identified 437 differentially expressed genes (DEGs) related to ccRCC development and prognosis, of which 311 and 126 genes are respectively down-regulated and up-regulated. The protein-protein interaction network of these DEGs consists of 395 nodes and 1872 interactions and 2 prominent modules. The Staphylococcus aureus infection and complement and coagulation cascades are significantly enriched in module 1 and are likely involved in ccRCC progression. Forty-two hub genes were screened, of which von Willebrand factor, TIMP metallopeptidase inhibitor 1, plasminogen, formimidoyltransferase cyclodeaminase, solute carrier family 34 member 1, hydroxyacid oxidase 2, alanine-glyoxylate aminotransferase 2, phosphoenolpyruvate carboxykinase 1, and 3-hydroxy-3-methylglutaryl-CoA synthase 2 are possibly related to the prognosis of ccRCC. The differential expression of all nine genes was confirmed by quantitative real-time polymerase chain reaction analysis of the ccRCC and normal renal tissues. These key genes are potential biomarkers for the diagnosis and prognosis of ccRCC and warrant further investigation.

New CXCR4 Antagonist Peptide R (Pep R) Improves Standard Therapy in Colorectal Cancer

The chemokine receptor CXCR4 is overexpressed and functional in colorectal cancer. To investigate the role of CXCR4 antagonism in potentiating colon cancer standard therapy, the new peptide CXCR4 antagonist Peptide R (Pep R) was employed. Human colon cancer HCT116 xenograft-bearing mice were treated with chemotherapeutic agents (CT) 5-Fluorouracil (5FU) and oxaliplatin (OX) or 5FU and radio chemotherapy (RT-CT) in the presence of Pep R. After two weeks, CT plus Pep R reduced by 4-fold the relative tumor volume (RTV) as compared to 2- and 1.6-fold reductions induced, respectively, by CT and Pep R. In vitro Pep R addition to CT/RT-CT impaired HCT116 cell growth and further reduced HCT116 and HT29 clonal capability. Thus, the hypothesis that Pep R could target the epithelial mesenchyme transition (EMT) process was evaluated. While CT decreased ECAD and increased ZEB-1 and CD90 expression, the addition of Pep R restored the pretreatment expression. In HCT116 and HT29 cells, CT/RT-CT induced a population of CD133+CXCR4+ cells, supposedly a stem-resistant cancer cell population, while Pep R reduced it. Taken together, the results showed that targeting CXCR4 ameliorates the effect of treatment in colon cancer through inhibition of cell growth and reversal of EMT treatment-induced markers, supporting further clinical studies.

Comprehensive expression analysis of mRNA and microRNA for the investigation of compensatory mechanisms in the rat kidney after unilateral nephrectomy

Compensation is a physiological response that occurs during chemical exposure to maintain homeostasis. Because compensatory responses are not usually considered adverse effects, it is important to understand compensatory mechanisms for chemical risk assessment. Although the kidney is a major target organ for toxicity, there is controversy over whether hyperplasia or hypertrophy contributes to the compensatory mechanism, and there is limited information to apply for chemical risk assessment. In the present study, compensatory mechanisms of the kidney were investigated in a unilateral nephrectomy (UNx) model using adult male and female F344 rats. In residual kidneys of male and female rats after UNx, 5-bromo-2'-deoxyuridine-labeling indices and mRNA expression of cell cycle-related genes were increased, although there were no fluctuations in mRNA expression of transforming growth factor-β1, which contributes to hypertrophy in renal tubules. Pathway analysis using mRNA expression data from a complementary DNA (cDNA) microarray revealed that canonical pathways related to cell proliferation were mainly activated and that forkhead box M1 (FOXM1) was an upstream regulator of compensatory cell proliferation in residual kidneys of male and female rats. cDNA microarray for microRNAs (miRNAs) demonstrated that nine miRNAs were downregulated in residual kidneys, and mRNA/miRNA integrated analysis indicated that miRNAs were associated with the expression of factors downstream of FOXM1. Overall, these results suggested that FOXM1-mediated hyperplasia rather than hypertrophy contributed to compensatory mechanisms in the kidney and that miRNAs regulated downstream FOXM1 signaling. These results will be beneficial for evaluating nephrotoxicity in chemical risk assessment and for developing new biomarkers to predict nephrotoxicity.

Clinicopathological and Prognostic Characteristics of CD276 (B7-H3) Expression in Adrenocortical Carcinoma

Background

Adrenocortical carcinoma (ACC) is a rare malignant endocrine tumor with a high tumor recurrence rate and poor postoperative survival. Recent studies suggest that CD276- (B7-H3) targeted therapy represents a promising therapeutic option for solid tumors. However, little is known about the expression status of CD276 or its association with progression and prognosis of ACC.

Methods

Clinical data were retrospectively analyzed from patients who underwent resection of ACC at our institution (n = 48). Archived, formalin-fixed, and paraffin-embedded samples were collected for immunohistochemical analysis, and the correlation between CD276 expression and clinicopathological parameters was evaluated. Kaplan-Meier and univariate/multivariate Cox regression methods were implemented to identify any prognostic effects. Data from The Cancer Genome Atlas (TCGA) ACC cohort (n = 48). Archived, formalin-fixed, and paraffin-embedded samples were collected for immunohistochemical analysis, and the correlation between CD276 expression and clinicopathological parameters was evaluated. Kaplan-Meier and univariate/multivariate Cox regression methods were implemented to identify any prognostic effects. Data from The Cancer Genome Atlas (TCGA) ACC cohort (.

Results

Positive expression of CD276 was detected on the cell membrane and in the cytoplasm of cancer cells or tumor-associated vascular cells in 91.67% (44/48) of ACCs. Vascular expression of CD276 was associated with local aggression (higher T stage, P = 0.029) and advanced ENSAT stage (P = 0.029) and advanced ENSAT stage (P = 0.029) and advanced ENSAT stage (P = 0.029) and advanced ENSAT stage (P = 0.029) and advanced ENSAT stage (P = 0.029) and advanced ENSAT stage (.

Conclusion

These findings highlight the immune checkpoint factor CD276 as an independent prognostic factor and a potential therapeutic target in ACC.

Single nucleotide polymorphism rs110861313 in the intergenic region of chromosome 23 is associated with the development of leukosis in the Russian Black Pied cattle

In recent years, using a genome-wide association study (GWAS), a number of single nucleotide polymorphisms (SNPs) have been suggested to be associated with susceptibility to leukemia in cattle. However, all studies have been done with purebred Holstein cows and their hybrids. In this regard, it is important to confirm the functional role of polymorphisms previously identified in a GWAS study in Russian cattle breeds. The aim of this study was to verify the association between rs110861313 in the intergenic region of bovine chromosome 23 and leukemia in the Russian Black Pied cattle. Based on the levels of bovine leukemia virus (BLV)-specific antibodies detected in serum using serodiagnostic techniques, animals were divided into three groups: healthy animals (n = 115), asymptomatic virus carriers (n = 145) and animals with leukemia (n = 107). Genotyping of rs110861313 was carried out using polymerase chain reaction followed by analysis of restriction fragment length polymorphisms. A significant decrease in the frequency of the A/A genotype (11.2 %) was revealed in animals with persistent lymphocytosis compared to virus carriers (27.6 %) (p < 0.002). At the same time, the frequency of animals with the C/C genotype in animals with persistent lymphocytosis (41.1 %) was significantly higher than that of virus carriers (21.4 %) (p < 0.001). In this case, asymptomatic virus carriers can be considered a more suitable control than healthy animals that have not been in contact with the virus. According to bioinformatics analysis, resistance to BLV can be due to the presence of the transcription factor FOXM1 binding site in the region of rs110861313. FOXM1 is expressed in immune cells and can potentially affect the expression of the neighboring genes (LY6G5B, GPANK1, ABHD16A, LY6G6F, LY6G6E, CSNK2B, ApoM). Thus, we found that SNP rs110861313 in the intergenic region of bovine chromosome 23 is associated with the development of leukemia following BLV infection in the Russian Black Pied cattle.

Expression of FSCN1 and FOXM1 are associated with poor prognosis of adrenocortical carcinoma patients

Background

Adrenocortical carcinoma (ACC) is a rare malignant endocrine tumour. Due to a high tumour recurrence rate, the post-operative overall survival (OS) and disease-free survival (DFS) of ACCs is limited. Our research aims to identify the role of the epithelial-mesenchymal transition (EMT) related genes FSCN1 and FOXM1 in the tumour microenvironment and assess their prognostic value in ACCs.

Methods

Clinical and specimen data from 130 adrenocortical carcinoma (ACC) patients was acquired from the Cancer Genome Atlas (TCGA) database (n = 79) and a West China Hospital (WCH) cohort (n = 51). In the WCH cohort, archived formalin-fixed paraffin embedded (FFPE) samples were collected for immunohistochemical analysis. The correlation between the EMT genes and the tumour microenvironment status was estimated based on the Tumour Immune Estimation Resource (TIMER) algorithm. Kaplan-Meier analysis, followed by univariate and multivariate regression analyses, were performed to identify the prognostic association of FSCN1 and FOXM1.

Results

FSCN1 and FOXM1 were over-expressed in ACC tissue when compared with adrenocortical adenoma and normal adrenal tissue. Over-expression of FSCN1 or FOXM1 was associated with the tumour microenvironment and immune signatures in ACCs. Patients with higher expression of FSCN1 or FOXM1 were more likely to have worse prognoses. The prognostic effects were further verified in both early (stage I/II) and advanced (stage III/IV) ACCs. Furthermore, FSCN1 and FOXM1 appeared as independent prognostic factors in ACC.

Conclusions

These results show that FSCN1 and FOXM1 are independent prognostic factors in ACCs and over-expression of FSCN1 or FOXM1 indicates a worse prognosis.

GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis

Introduced in 2017, the GEPIA (Gene Expression Profiling Interactive Analysis) web server has been a valuable and highly cited resource for gene expression analysis based on tumor and normal samples from the TCGA and the GTEx databases. Here, we present GEPIA2, an updated and enhanced version to provide insights with higher resolution and more functionalities. Featuring 198 619 isoforms and 84 cancer subtypes, GEPIA2 has extended gene expression quantification from the gene level to the transcript level, and supports analysis of a specific cancer subtype, and comparison between subtypes. In addition, GEPIA2 has adopted new analysis techniques of gene signature quantification inspired by single-cell sequencing studies, and provides customized analysis where users can upload their own RNA-seq data and compare them with TCGA and GTEx samples. We also offer an API for batch process and easy retrieval of the analysis results. The updated web server is publicly accessible at http://gepia2.cancer-pku.cn/.

Impact of hypoxic tumor microenvironment and tumor cell plasticity on the expression of immune checkpoints

Compared to traditional therapies, such as surgery, radio-chemotherapy, or targeted approaches, immunotherapies based on immune checkpoint blockers (ICBs) have revolutionized the treatment of cancer. Although ICBs have yielded long-lasting results and have improved patient survival, this success has been seriously challenged by clinical observations showing that only a small fraction of patients benefit from this revolutionary therapy and no benefit has been found in patients with highly aggressive tumors. Efforts are currently ongoing to identify factors that predict the response to ICB. Among the different predictive markers established so far, the expression levels of immune checkpoint genes have proven to be important biomarkers for informing treatment choices. Therefore, understanding the mechanisms involved in the regulation of immune checkpoints is a key element that will facilitate novel combination approaches and optimize patient outcome. In this review, we discuss the impact of hypoxia and tumor cell plasticity on immune checkpoint gene expression and provide insight into the therapeutic value of the EMT signature and the rationale for novel combination approaches to improve ICB therapy and maximize the benefits for patients with cancer.

Therapeutically exploiting STAT3 activity in cancer - using tissue repair as a road map

The tightly orchestrated temporal and spatial control of signal transducer and activator of transcription 3 (STAT3) activity in epithelial, immune and stromal cells is critical for wound healing and tissue repair. Excessive STAT3 activation within cancer cells and cells of the tumour microenvironment can be viewed as a neoplastic mimic of an inflammation-driven repair response that collectively promotes tumour progression. In addition to the canonical transcriptional pathways by which STAT3 promotes stem cell-like characteristics, survival, proliferation, metastatic potential and immune evasion, cytoplasmic STAT3 activity fuels tumour growth by metabolic and other non-transcriptional mechanisms. Here, we review the tumour-modulating activities of STAT3 in light of its role as a signalling node integrating inflammatory responses during wound healing. Accordingly, many of the cytokines that contribute to the para-inflammatory state of most solid malignancies converge on and underpin dysregulated STAT3 activity. Targeting of these cytokines, their cognate receptors and associated signalling cascades in clinical trials is beginning to demonstrate therapeutic efficacy, given that interference with STAT3 activity is likely to simultaneously curb the growth of cancer cells and augment antitumour immunity.