Prediction of Prognosis and Recurrence of Bladder Cancer by ECM-Related Genes

Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis

BACKGROUND

Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN.

METHODS

The gene chip data were retrieved from the GEO database using the search term ' diabetic nephropathy '. The ' limma ' software package was used to identify differentially expressed genes (DEGs) between DN and control samples. Gene set enrichment analysis (GSEA) was performed on genes obtained from the molecular characteristic database (MSigDB. The R package 'WGCNA' was used to identify gene modules associated with tubulointerstitial injury in DN, and it was crossed with immune-related DEGs to identify target genes. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on differentially expressed genes using the 'ClusterProfiler' software package in R. Three methods, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest (RF), were used to select immune-related biomarkers for diagnosis. We retrieved the tubulointerstitial dataset from the Nephroseq database to construct an external validation dataset. Unsupervised clustering analysis of the expression levels of immune-related biomarkers was performed using the 'ConsensusClusterPlus 'R software package. The urine of patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from September 2021 to March 2023 was collected, and Elisa was used to detect the mRNA expression level of immune-related biomarkers in urine. Pearson correlation analysis was used to detect the effect of immune-related biomarker expression on renal function in DN patients.

RESULTS

Four microarray datasets from the GEO database are included in the analysis : GSE30122, GSE47185, GSE99340 and GSE104954. These datasets included 63 DN patients and 55 healthy controls. A total of 9415 genes were detected in the data set. We found 153 differentially expressed immune-related genes, of which 112 genes were up-regulated, 41 genes were down-regulated, and 119 overlapping genes were identified. GO analysis showed that they were involved in various biological processes including leukocyte-mediated immunity. KEGG analysis showed that these target genes were mainly involved in the formation of phagosomes in Staphylococcus aureus infection. Among these 119 overlapping genes, machine learning results identified AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1 and FSTL1 as potential tubulointerstitial immune-related biomarkers. External validation suggested that the above markers showed diagnostic efficacy in distinguishing DN patients from healthy controls. Clinical studies have shown that the expression of AGR2, CX3CR1 and FSTL1 in urine samples of DN patients is negatively correlated with GFR, the expression of CX3CR1 and FSTL1 in urine samples of DN is positively correlated with serum creatinine, while the expression of DEFB1 in urine samples of DN is negatively correlated with serum creatinine. In addition, the expression of CX3CR1 in DN urine samples was positively correlated with proteinuria, while the expression of DEFB1 in DN urine samples was negatively correlated with proteinuria. Finally, according to the level of proteinuria, DN patients were divided into nephrotic proteinuria group (n = 24) and subrenal proteinuria group. There were significant differences in urinary AGR2, CCR2 and DEFB1 between the two groups by unpaired t test (P < 0.05).

CONCLUSIONS

Our study provides new insights into the role of immune-related biomarkers in DN tubulointerstitial injury and provides potential targets for early diagnosis and treatment of DN patients. Seven different genes ( AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1, FSTL1 ), as promising sensitive biomarkers, may affect the progression of DN by regulating immune inflammatory response. However, further comprehensive studies are needed to fully understand their exact molecular mechanisms and functional pathways in DN.

CTHRC1 is a prognostic biomarker correlated with immune infiltration in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy worldwide, characterized by high morbidity, high mortality, and poor prognosis. Collagen triple helix repeat containing 1 (CTHRC1) has been shown to be highly expressed in various cancers. However, its biological functions, potential role as a biomarker, and its relationship with immune infiltrates in HNSCC remain unclear. Our principal objective was to analyze CTHRC1 expression, its prognostic implications, biological functions, and its effects on the immune system in HNSCC patients using bioinformatics analysis.

METHODS

The expression matrix was obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). CTHRC1 expression in HNSCC was analyzed between tumor and adjacent normal tissues, different stages were compared, and its impact on clinical prognosis was assessed using Kaplan-Meier analysis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA) were employed for enrichment analysis. The Search Tool for the Retrieval of Interacting Genes database (STRING) was used to analyze protein-protein interactions. Pearson correlation tests were used to investigate the association between CTHRC1 expression and immune checkpoints. The correlation between CTHRC1 and immune infiltration was investigated using CIBERSORT, TIMER, and ESTIMATE.

RESULTS

Compared to adjacent normal tissues, CTHRC1 was found to be highly overexpressed in tumors. Increased expression of CTHRC1 was more evident in the advanced stage of HNSCC and predicted a poor prognosis. Most genes related to CTHRC1 in HNSCC were enriched in physiological functions of Extracellular matrix(ECM) and tumor. Furthermore, several immune checkpoints, such as TNFSF4 and CD276 have been shown to be associated with CTHRC1 expression. Notably, the level of CTHRC1 expression correlated significantly with immune infiltration levels, particularly activated macrophages in HNSCC.

CONCLUSIONS

High expression of CTHRC1 predicts poor prognosis and is associated with immune infiltration in HNSCC, confirming its utility as a tumor marker for HNSCC.

TRIAL REGISTRATION

Not applicable. All data are from public databases and do not contain any clinical trials.

FSTL1: A double-edged sword in cancer development

Flolistatin-related protein 1 (FSTL1), a secreted glycoprotein that is involved in many physiological functions, has attracted much interest and has been implicated in a wide range of diseases, including heart diseases and inflammatory diseases. In recent years, the involvement of FSTL1 in cancer progression has been implicated and researched. FSTL1 plays a contradictory role in cancer, depending on the cancer type as well as the contents of the tumor microenvironment. As reviewed here, the structure and distribution of FSTL1 are first introduced. Subsequently, the expression and clinical significance of FSTL1 in various types of cancer as a tumor enhancer or inhibitor are addressed. Furthermore, we discuss the functional role of FSTL1 in various processes that involve tumor cell proliferation, metastasis, immune responses, stemness, cell apoptosis, and resistance to chemotherapy. FSTL1 expression is tightly controlled in cancer, and a multitude of cancer-related signaling cascades like TGF-β/BMP/Smad signaling, AKT, NF-κB, and Wnt-β-catenin signaling pathways are modulated by FSTL1. Finally, FSTL1 as a therapeutic target using monoclonal antibodies is stated. Herein, we review recent findings showing the double-edged characteristics and mechanisms of FSTL1 in cancer and elaborate on the current understanding of therapeutic approaches targeting FSTL1.

The role of collagen triple helix repeat containing 1 (CTHRC1) in cancer development and progression

INTRODUCTION

Collagen triple helix repeat containing 1 (CTHRC1) is a protein that has been implicated in pro-migratory pathways, arterial tissue-repair processes, and inhibition of collagen deposition via the regulation of multiple signaling cascades. Studies have also demonstrated an upregulation of CTHRC1 in multiple cancers where it has been linked to enhanced proliferation, invasion, and metastasis. However, the understanding of the exact role and mechanisms of CTHRC1 in cancer is far from complete.

AREAS COVERED

This review focuses on analyzing the role of CTHRC1 in cancer as well as its associations with clinicopathologies and cancer-related processes and signaling. We have also summarized the available literature information regarding the role of CTHRC1 in tumor microenvironment and immune signaling. Finally, we have discussed the mechanisms associated with CTHRC1 regulations, and opportunities and challenges regarding the development of CTHRC1 as a potential target for cancer management.

EXPERT OPINION

CTHRC1 is a multifaceted protein with critical roles in cancer progression and other pathological conditions. Its association with lower overall survival in various cancers, and impact on the tumor immune microenvironment make it an intriguing target for further research and potential therapeutic interventions in cancer.

6-O-endosulfatases in tumor metastasis: heparan sulfate proteoglycans modification and potential therapeutic targets

Metastasis is the leading cause of cancer-associated mortality. Although advances in the targeted treatment and immunotherapy have improved the management of some cancers, the prognosis of metastatic cancers remains unsatisfied. Therefore, the specific mechanisms in tumor metastasis need further investigation. 6-O-endosulfatases (SULFs), comprising sulfatase1 (SULF1) and sulfatase 2 (SULF2), play pivotal roles in the post-synthetic modifications of heparan sulfate proteoglycans (HSPGs). Consequently, these extracellular enzymes can regulate a variety of downstream pathways by modulating HSPGs function. During the past decades, researchers have detected the expression of SULF1 and SULF2 in most cancers and revealed their roles in tumor progression and metastasis. Herein we reviewed the metastasis steps which SULFs participated in, elucidated the specific roles and mechanisms of SULFs in metastasis process, and discussed the effects of SULFs in different types of cancers. Moreover, we summarized the role of targeting SULFs in combination therapy to treat metastatic cancers, which provided some novel strategies for cancer therapy.

A novel anoikis-related gene signature identifies LYPD1 as a novel therapy target for bladder cancer

Bladder cancer (BLCA) is a malignant tumor associated with unfavorable outcomes. Studies suggest that anoikis plays a crucial role in tumor progression and cancer cell metastasis. However, its specific role in bladder cancer remains poorly understood. Our objective was to identify anoikis-related genes (ARGs) and subsequently construct a risk model to assess their potential for predicting the prognosis of bladder cancer.The transcriptome data and clinical data of BLCA patients were sourced from The Cancer Genome Atlas and GEO database. We then performed the differential expression analysis to screen differentially expressed ARGs. Subsequently, we conducted non-negative matrix factorization (NMF) clustering analysis to establish molecular subtypes based on the differentially expressed ARGs. The CIBERSORT algorithm was used to estimate the quantification of different cell infiltration in BLCA tumor microenviroment. A prognostic risk model containing 7 ARGs was established using Lasso-Cox regression analysis. The nomogram was built for predicting the survival probability of BLCA patients. To determine the drug sensitivity of each sample from the high- and low-risk groups, the R package "pRRophetic" was performed. Finally, the role of LYPD1 was explored in BLCA cell lines.We identified 90 differential expression ARGs and NMF clustering categorizated the BLCA patientss into two distinct groups (cluster A and B). Patients in cluster A had a better prognosis than those in cluster B. Then, we established a ARGs risk model including CALR, FASN, FOSL1, JUN, LYPD1, MST1R, and SATB1, which was validated in the train and test set. The results suggested overall survival rate was much higher in low risk group than high risk group. The cox regression analysis, ROC curve analysis, and nomogram collectively demonstrated that the risk model served as an independent prognostic factor. The high risk group had a higher level TME scores compared to the low risk group. Furthermore, LYPD1 was low expression in BLCA cells and overexpression of LYPD1 inhibits the prolifearation, migration and invasion.In the current study, we have identified differential expression ARGs and constructed a risk model with the promise for guiding prognostic predictions and provided a therapeutic target for patients with BLCA.

A risk model based on lncRNA-miRNA-mRNA gene signature for predicting prognosis of patients with bladder cancer

OBJECTIVES

We aimed to analyze lncRNAs, miRNAs, and mRNA expression profiles of bladder cancer (BC) patients, thereby establishing a gene signature-based risk model for predicting prognosis of patients with BC.

METHODS

We downloaded the expression data of lncRNAs, miRNAs and mRNA from The Cancer Genome Atlas (TCGA) as training cohort including 19 healthy control samples and 401 BC samples. The differentially expressed RNAs (DERs) were screened using limma package, and the competing endogenous RNAs (ceRNA) regulatory network was constructed and visualized by the cytoscape. Candidate DERs were screened to construct the risk score model and nomogram for predicting the overall survival (OS) time and prognosis of BC patients. The prognostic value was verified using a validation cohort in GSE13507.

RESULTS

Based on 13 selected. lncRNAs, miRNAs and mRNA screened using L1-penalized algorithm, BC patients were classified into two groups: high-risk group (including 201 patients ) and low risk group (including 200 patients). The high-risk group's OS time ( hazard ratio [HR], 2.160; 95% CI, 1.586 to 2.942; P= 5.678e-07) was poorer than that of low-risk groups' (HR, 1.675; 95% CI, 1.037 to 2.713; P= 3.393 e-02) in the training cohort. The area under curve (AUC) for training and validation datasets were 0.852. Younger patients (age ⩽ 60 years) had an improved OS than the patients with advanced age (age > 60 years) (HR 1.033, 95% CI 1.017 to 1.049; p= 2.544E-05). We built a predictive model based on the TCGA cohort by using nomograms, including clinicopathological factors such as age, recurrence rate, and prognostic score.

CONCLUSIONS

The risk model based on 13 DERs patterns could well predict the prognosis for patients with BC.

The collagen landscape in cancer: profiling collagens in tumors and in circulation reveals novel markers of cancer-associated fibroblast subtypes

Cancer-associated fibroblasts (CAFs) deposit and remodel collagens in the tumor stroma, impacting cancer progression and efficacy of interventions. CAFs are the focus of new therapeutics with the aim of normalizing the tumor microenvironment. To do this, a better understanding of CAF heterogeneity and collagen composition in cancer is needed. In this study, we sought to profile the expression of collagens at multiple levels with the goal of identifying cancer biomarkers. We investigated the collagen expression pattern in various cell types and CAF subtypes in a publicly available single-cell RNA sequencing (RNA-seq) dataset of pancreatic ductal adenocarcinoma. Next, we investigated the collagen expression profile in tumor samples across cancer types from The Cancer Genome Atlas (TCGA) database and evaluated if specific patterns of collagen expression were associated with prognosis. Finally, we profiled circulating collagen peptides using a panel of immunoassays to measure collagen fragments in the serum of cancer patients. We found that pancreatic stellate cells and fibroblasts were the primary producers of collagens in the pancreas. COL1A1, COL3A1, COL5A1, COL6A1 were expressed in all CAF subtypes, whereas COL8A1, COL10A1, COL11A1, COL12A1 were specific to myofibroblast CAFs (myCAF) and COL14A1 specific to inflammatory CAFs (iCAF). In TCGA database, myCAF collagens COL10A1 and COL11A1 were elevated across solid tumor types, and multiple associations between high expression and worse survival were found. Finally, circulating collagen biomarkers were elevated in the serum of patients with cancer relative to healthy controls with COL11A1 (myCAF) having the best diagnostic accuracy of the markers measured. In conclusion, CAFs express a noncanonical collagen profile with specific collagen subtypes associated with iCAFs and myCAFs in PDAC. These collagens are deregulated at the cellular, tumor, and systemic levels across different solid tumors and associate with survival. These findings could lead to new discoveries such as novel biomarkers and therapeutic targets. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Development of a butyrate metabolism-related gene-based molecular subtypes and scoring system for predicting prognosis and immunotherapy response in bladder cancer

PURPOSE

In recent times, multiple molecular subtypes with varying prognoses have been identified in bladder cancer (BLCA). However, the attributes of butyrate metabolism-related (BMR) molecular subtypes and their correlation with immunotherapy response remain inadequately explored in BLCA.

METHODS

We utilized 594 samples of BLCA to investigate the molecular subtypes mediated by BMR genes and their correlation with the immunotherapy response. To quantify the BMR features of individual tumors, we developed a BMR score through the COX and LASSO regression methods. Clinical-related, tumor microenvironment, drug-sensitive and immunotherapy analyses were used to comprehensively analyze BMR scores.

RESULTS

Two distinct molecular subtypes related to butyrate metabolism were identified in BLCA, each with unique prognostic implications and immune microenvironments. BMR score was constructed based on 7 BMR genes and was used to classify the patients into two score groups. Clinical analysis revealed that the BMR score was an independent prognostic factor. The higher the score, the worse the prognosis. The BMR score can also predict tumor immunity. The results demonstrated that a low BMR score was associated with higher efficacy of immunotherapy, which was also validated by an external dataset.

CONCLUSION

Our study proposes both molecular subtypes and a BMR-based score as promising prognostic classifications in BLCA. These findings may offer new insights for the development of precise targeted cancer therapies.

The construction and validation of ECM-related prognosis model in laryngeal squamous cell carcinoma

Background

Laryngeal squamous cell carcinoma (LSCC) is a kind of common and aggressive tumor with high mortality. The application of molecular biomarkers is useful for the early diagnosis and treatment of LSCC.

Methods

The expression and clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Principal components analysis (PCA) was used to discriminate between LSCC and normal samples. The hub genes were screened out through univariate and multivariate cox analyses. The Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve was used to validate the predictive performance. The single sample gene set enrichment analysis (ssGSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to determine the enrichment function. Protein-Protein Interaction (PPI) network was constructed in STRING. The immune analysis was performed by ESTIMATE, IPS and xCELL. The drug sensitivity was identified with GSCA database.

Results

We identified that 47 extracellular matrix (ECM) genes were differentially expressed in LSCC compared with normal group. Univariate and multivariate cox analysis determined that leucine-rich glioma-inactivated 4 (LGI4), matrilin 4 (MATN4), microfibrillar-associated protein 2 (MFAP2) and fibrinogen like 2 (FGL2) were closely related to the disease free survival (DSS) of LSCC. ROC curve determined that the risk model has a good predictive performance. PPI network showed the top 100 genes with high correlation of hub genes. The ssGSEA, GO and KEGG enrichment analyses determined that immune response was significantly involved in the development of LSCC. Immune infiltration analysis showed that most immune cells and immune checkpoints were inhibited in high risk score group. Drug sensitivity analysis showed that MATN4, FGL2 and LGI4 were negatively related to various drugs, while MFAP2 was positively related to many drugs.

Conclusion

We established a risk model constructed with four ECM-related genes, which could effectively predict the prognosis of LSCC.

A potential biological signature of 7-methylguanosine-related lncRNA to predict the immunotherapy effects in bladder cancer

Background

Bladder urothelial carcinoma (BLCA) is the second prevalent genitourinary carcinoma globally. N7-methylguanosine (m7G) is important for tumorigenesis and progression. This study aimed to build a predictive model for m7G-related long non-coding RNAs (lncRNAs), elucidate their role in the tumor immune microenvironment (TIME), and predict immunotherapy response in BLCA.

Methods

We first used univariate Cox regression and coexpression analyses to identify m7G-related lncRNAs. Next, the prognostic model was built by utilizing LASSO regression analysis. Then, the prognostic significance of the model was examined utilizing Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, nomogram, and univariate, multivariate Cox regression. We also analyzed Gene set enrichment analyses (GSEA), immune analysis and principal component analysis (PCA) in risk groups. To further predict immunotherapy effectiveness, we evaluated the predictive ability for immunotherapy in 2 risk groups and clusters using tumor immune dysfunction and exclusion (TIDE) score and Immunophenoscore (IPS).

Results

Seven lncRNAs related to m7G were used to create a model. The calibration plots for the model suggested a strong fit with the prediction of overall survival (OS). The area under the curve (AUC) for first, second, and third years was respectively, 0.722, 0.711, and 0.686. In addition, the risk score had strong correlation with TIME features and genes linked to immune checkpoint blockade (ICB). TIDE scores were dramatically different between two risk groups (p < 0.05), and IPS scores were markedly different between two clusters (p < 0.05).

Conclusion

Our research constructed a novel m7G-related lncRNAs that could be used to predict patient outcomes and the effectiveness of immunotherapy in BLCA. Immunotherapy may be more effective for the low-risk group and cluster 2.

Prognostic value of COL10A1 and its correlation with tumor-infiltrating immune cells in urothelial bladder cancer: A comprehensive study based on bioinformatics and clinical analysis validation

Introduction

Bladder cancer (BLCA) is one of the most lethal diseases. COL10A1 is secreted small-chain collagen in the extracellular matrix associated with various tumors, including gastric, colon, breast, and lung cancer. However, the role of COL10A1 in BLCA remains unclear. This is the first research focusing on the prognostic value of COL10A1 in BLCA. In this research, we aimed to uncover the association between COL10A1 and the prognosis, as well as other clinicopathological parameters in BLCA.

Methods

We obtained gene expression profiles of BLCA and normal tissues from the TCGA, GEO, and ArrayExpress databases. Immunohistochemistry staining was performed to investigate the protein expression and prognostic value of COL10A1 in BLCA patients. GO and KEGG enrichment along with GSEA analyses were performed to reveal the biological functions and potential regulatory mechanisms of COL10A1 based on the gene co-expression network. We used the “maftools” R package to display the mutation profiles between the high and low COL10A1 groups. GIPIA2, TIMER, and CIBERSORT algorithms were utilized to explore the effect of COL10A1 on the tumor immune microenvironment.

Results

We found that COL10A1 was upregulated in the BLCA samples, and increased COL10A1 expression was related to poor overall survival. Functional annotation of 200 co-expressed genes positively correlated with COL10A1 expression, including GO, KEGG, and GSEA enrichment analyses, indicated that COL10A1 was basically involved in the extracellular matrix, protein modification, molecular binding, ECM-receptor interaction, protein digestion and absorption, focal adhesion, and PI3K-Akt signaling pathway. The most commonly mutated genes of BLCA were different between high and low COL10A1 groups. Tumor immune infiltrating analyses showed that COL10A1 might have an essential role in recruiting infiltrating immune cells and regulating immunity in BLCA, thus affecting prognosis. Finally, external datasets and biospecimens were used, and the results further validated the aberrant expression of COL10A1 in BLCA samples.

Conclusions

In conclusion, our study demonstrates that COL10A1 is an underlying prognostic and predictive biomarker in BLCA.