Identification of Immune-Related Subtypes and Characterization of Tumor Microenvironment Infiltration in Bladder Cancer

The prognostic significance and immune characteristics of bone morphogenetic proteins (BMPs) family: A pan-cancer multi-omics analysis

BACKGROUND

Bone morphogenetic proteins (BMPs) are a group of cancer-related proteins vital for development and progression of certain cancer types. Nevertheless, function of BMP family in pan-cancer was not detailedly researched.

OBJECTIVE

Investigating expression pattern and prognostic value of the BMPs family (BMP1-8A and BMP8B) expression across multiple cancer types.

METHODS

Our research integrated multi-omics data for exploring potential associations between BMPs expression and prognosis, clinicopathological characteristics, copy number or somatic mutations, immune characteristics, tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint genes and drug sensitivity in The Cancer Genome Atlas (TCGA) tumors. Furthermore, association of BMPs expression and immunotherapy effectiveness was investigated in some confirmatory cohorts (GSE111636, GSE78220, GSE67501, GSE176307, IMvigor210 and mRNA sequencing data from currently undergoing TRUCE01 clinical research included), and biological function and potential signaling pathways of BMPs in bladder cancer (BCa) was explored via Gene Set Enrichment Analysis (GSEA). Eventually, immune infiltration analysis was done via BMPs expression, copy number or somatic mutations in BCa, as well as validation of the expression levels by reverse transcription-quantitative PCR and western blot, and in vitro functional experiments of BMP8A.

RESULTS

Discoveries displayed BMPs expression was related to prognosis, clinicopathological characteristics, mutations, TME, TMB, MSI and immune checkpoint genes of TCGA tumors. Anticancer drug sensitivity analysis displayed BMPs were associated with various drug sensitivities. What's more, it was discovered that expression level of certain BMP family members related to objective response to immunotherapy. By GSEA, we discovered multiple immune-associated functions and pathways were enriched. Immune infiltration analysis on BCa also displayed significant associations among BMPs copy number variations, mutation status and infiltration level of diverse immune cells. Furthermore, differential expression validation and in vitro phenotypic experiment indicated that BMP8A significantly promoted BCa cell proliferation, migration and invasion.

CONCLUSIONS

Current results confirmed significance of both BMPs expression and genomic alteration in the prognosis and treatment of diverse cancer types, and suggested that BMPs may be vital for BCa and can possibly be utilized as biomarkers for immunotherapy.

Exploring the mystery of colon cancer from the perspective of molecular subtypes and treatment

The molecular categorization of colon cancer patients remains elusive. Gene set enrichment analysis (GSEA), which investigates the dysregulated genes among tumor and normal samples, has revealed the pivotal role of epithelial-to-mesenchymal transition (EMT) in colon cancer pathogenesis. In this study, we employed multi-clustering method for grouping data, resulting in the identification of two clusters characterized by varying prognostic outcomes. These two subgroups not only displayed disparities in overall survival (OS) but also manifested variations in clinical variables, genetic mutation, and gene expression profiles. Using the nearest template prediction (NTP) method, we were able to replicate the molecular classification effectively within the original dataset and validated it across multiple independent datasets, underscoring its robust repeatability. Furthermore, we constructed two prognostic signatures tailored to each of these subgroups. Our molecular classification, centered on EMT, hold promise in offering fresh insights into the therapy strategies and prognosis assessment for colon cancer.

The prognostic values and immune characteristics of polo-like kinases (PLKs) family: A pan-cancer multi-omics analysis

Background

In the realm of tumor-targeted therapeutics, Polo-like kinases (PLKs) are a significant group of protein kinases that were found recently as being related to tumors. However, the significance of PLKs in pan-cancer remains systematically studied.

Methods and materials

We integrated multi-omics data to comprehensively investigate the expression patterns of the PLK family across various cancer types. Subsequently, study examined the associations between tumor mutation burden (TMB), microsatellite instability (MSI), immune subtype classification, immune infiltration, tumor microenvironment scores, immune checkpoint gene expression, and the PLKs expression profiles within various tumor types. Furthermore, using our mRNA sequencing data (TRUCE01) and four bladder cancer (BLCA) cohorts (GSE111636, GSE176307, and IMvigor210), We examined the correlation between the expression level of PLK and immunotherapy effectiveness. Next, Gene set enrichment analysis (GSEA) was evaluated to find that potentially enriched PLK signaling pathways. Utilizing TIMER 2.0, we conducted an immune infiltration analysis underlying transcriptome expression, copy number variations (CNV), or somatic mutations of PLKs in BLCA. Finally, mRNA expression validation of PLK1/3/4 by real-time PCR within 10 paired BLCA tissues, protein expression verification through the Human Protein Atlas (HPA), and PLK4 in vitro cytological studies have been employed in BLCA.

Results

The expression of most of the PLK family members exhibits variation between cancerous tissues and adjacent normal tissues across various cancer species. Furthermore, the expression of PLKs demonstrates a significant association with immunotyping, infiltration of immune cell, tumor mutational burden (TMB), microsatellite instability (MSI), immunological checkpoint gene activity and therapeutic effectiveness in pan-tumor tissues. Additional investigation into the correlation between the PLK family and BLCA has revealed that the expression of the PLK genes holds substantial significance in the biological processes of BLCA. Furthermore, a notable association has been observed between the copy number variation, variant status, and the degree of certain immunological cell infiltration. Of note, the expression validation and in vitro phenotypic experiments have demonstrated that PLK4 has a significant function in promoting the BLCA cell proliferation, migration, and invasion.

Conclusion

Collectively, based on various databases, our results highlight the involvement of PLK gene family in the formation of different types of tumors and identify PLK-related genes that may be used for therapy.

JAM3: A prognostic biomarker for bladder cancer via epithelial-mesenchymal transition regulation

Understanding the intricate relationship between prognosis, immune function, and molecular markers in bladder cancer (BC) demands sophisticated analytical methods. To identify novel biomarkers for predicting prognosis and immune function in BC patients, we combined weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis. This was conducted using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Ultimately, we screened the junctional adhesion molecule 3 (JAM3) as an independent risk factor in BC. High levels of JAM3 were linked to adverse clinical parameters, such as higher T and N stages. Additionally, a JAM3-based nomogram model accurately predicted 1-, 3- and 5-year survival rates of BC patients, indicating potential clinical utility. Functional enrichment analysis revealed that high JAM3 expression activated the calcium signaling pathway, the extracellular matrix (ECM)-receptor interaction, and the PI3K-Akt signaling pathway, and was positively correlated with genes associated with epithelial-mesenchymal transition (EMT). Subsequently, we found that overexpression of JAM3 promoted the migration and invasion abilities in BC cells, regulating the expression levels of N-cadherin, matrix metallopeptidase 2 (MMP2), and Claudin-1 thereby promoting EMT levels. Additionally, we showed that JAM3 was negatively correlated with anti-tumor immune cells such as CD8+ T cells, while positively correlated with pro-tumor immune cells such as M2 macrophages, suggesting its involvement in immune cell infiltration. The immune checkpoint CD200 also showed a positive correlation with JAM3. Our findings revealed that elevated JAM3 levels are predictive of poor prognosis and immune cell infiltration in BC patients by regulating the EMT process.

Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for muscle-invasive urothelial cancer

Muscle-invasive urothelial cancer (MUC), characterized by high aggressiveness and significant heterogeneity, is currently lacking highly precise individualized treatment options. We used a computational pipeline to synthesize multiomics data from MUC patients using 10 clustering algorithms, which were then combined with 10 machine learning algorithms to identify molecular subgroups of high resolution and develop a robust consensus machine learning-driven signature (CMLS). Through multiomics clustering, we identified three cancer subtypes (CSs) that are related to prognosis, with CS2 exhibiting the most favorable prognostic outcome. Subsequent screening enabled identification of 12 hub genes that constitute a CMLS with robust predictive power for prognosis. The low-CMLS group exhibited a more favorable prognosis and greater responsiveness to immunotherapy and was more likely to exhibit the "hot tumor" phenotype. The high-CMLS group had a poor prognosis and lower likelihood of benefitting from immunotherapy, but dasatinib and romidepsin may serve as promising treatments for them. Comprehensive analysis of multiomics data can offer important insights and further refine the molecular classification of MUC. Identification of CMLS represents a valuable tool for early prediction of patient prognosis and for screening potential candidates likely to benefit from immunotherapy, with broad implications for clinical practice.

The global research of bladder cancer immunotherapy from 2012 to 2021: A bibliometric analysis

Intravesical instillation of Bacillus Calmette-Guérin has been used as an immunotherapy to treat superficial bladder cancer for almost half a century. In recent years, the approval of several monoclonal antibody treatments has transformed the treatment landscape for patients with muscle-invasive or metastatic uroepithelial carcinoma. The purpose of this study was to conduct a thorough review of immunotherapy in bladder cancer through a bibliometric approach. Publications related to bladder cancer immunotherapy were obtained from the Web of Science Core Collection on July 1st, 2022. We conducted a bibliometric analysis of literature information using CiteSpace IV, VOSviewer, and Scimago Graphica, including co-authorship or co-citation of authors, countries/regions, journals, references, and keyword co-occurrence. There was a total of 2,352 papers included, with the most contributions coming from the United States, China, and Italy. The United States had the highest H-index value and was the leading country in this field. Meanwhile, the number of publications in China was steadily growing. The top three productive researchers were Kamat AM, Necchi A, and Shariat SF, with Powles T as the top co-cited author. Most papers were published by the University of Texas System. The majority of papers in this field were published in Urologic Oncology Seminars and Original Investigations and European Urology was the most influential journal with the highest H-index. The tumor microenvironment and complete molecular characterization may still be the frontier in this research area, allowing us to obtain a better understanding of the pathogenesis and clinical prognosis of bladder cancer. More research are conducted to identify clinically meaningful biomarkers that may provide opportunities for the personalization of bladder cancer therapy. This study provides clinicians and researchers with an overview and helpful guidance on how to choose the research direction and management of bladder cancer immunotherapy.

Immune landscape of distinct subtypes in urothelial carcinoma based on immune gene profile

Immune checkpoint blockade (ICB) has become a promising therapy for multiple cancers. However, only a small proportion of patients display a limited antitumor response. The present study aimed to classify distinct immune subtypes and investigate the tumor microenvironment (TME) of urothelial carcinoma, which may help to understand treatment failure and improve the immunotherapy response. RNA-seq data and clinical parameters were obtained from TCGA-BLCA, E-MTAB-4321, and IMVigor210 datasets. A consensus cluster method was used to distinguish different immune subtypes of patients. Infiltrating immune cells, TME signatures, immune checkpoints, and immunogenic cell death modulators were evaluated in distinct immune subtypes. Dimension reduction analysis was performed to visualize the immune status of urothelial carcinoma based on graph learning. Weighted gene co-expression network analysis (WGCNA) was performed to obtain hub genes to predict responses after immunotherapy. Patients with urothelial carcinoma were classified into four distinct immune subtypes (C1, C2, C3 and C4) with various types of molecular expression, immune cell infiltration, and clinical characteristics. Patients with the C3 immune subtype displayed abundant immune cell infiltrations in the tumor microenvironment and were typically identified as “hot” tumor phenotypes, whereas those with the C4 immune subtype with few immune cell infiltrations were identified as “cold” tumor phenotypes. The immune-related and metastasis-related signaling pathways were enriched in the C3 subtype compared to the C4 subtype. In addition, tumor mutation burden, inhibitory immune checkpoints, and immunogenic cell death modulators were highly expressed in the C3 subtype. Furthermore, patients with the C4 subtype had a better probability of overall survival than patients with the C3 subtype in TCGA-BLCA and E-MTAB-4321 cohorts. Patients with the C1 subtype had the best prognosis when undergoing anti-PD-L1 antibody treatment. Finally, the immune landscape of urothelial carcinoma showed the immune status in each patient, and TGFB3 was identified as a potential biomarker for the prediction of immunotherapy resistance after anti-PD-L1 monoclonal antibody treatment. The present study provided a bioinformatics basis for understanding the immune landscape of the tumor microenvironment of urothelial carcinoma.

Identification of Immune-Related Subtypes and Characterization of Tumor Microenvironment Infiltration in Kidney Renal Clear Cell Carcinoma

Background: Tumor microenvironment (TME) plays indisputable role in the progression of cancers. Immune cell infiltration (ICI) in TME was related to the prognosis of tumor patients. In this paper, we identified the pattern of immune-related ICI subtypes based on the TME immune infiltration pattern.

Methods: The data from kidney renal clear cell carcinoma data (KIRC) was downloaded from the TCGA database. The distinct ICI subtypes were identified using CIBERSORT and ESTIMATE algorithms. The gene subgroups were identified based on DEGs in ICI subtypes. The single sample gene set enrichment analysis (ssGSEA) was used to ascertain the ICI score. Kaplan-Meier curve with log-rank test was conducted to analyze the survival probability of patients with KIRC in different subtypes.

Results: The patients with high ICI scores exhibited a longer survival time and lower expression of checkpoint-related and immune activity-related genes. The high ICI score clusters were positively related to TMB. The patients in the low TMB subgroups have a favorable prognosis. The prediction ICI score did not affect the TMB status, and the low TMB subgroups + low/high ICI score subgroups exhibited better survival.

Conclusion: In all, the tumor immune microenvironment, ICI score, and TMB were important determinants of KIRC patients’ survival outcomes. The TMB + ICI score may be a potential biomarker for predicting the prognosis of patients and for targeted immunotherapies to treating KIRC.

Classification of Muscle Invasive Bladder Cancer to Predict Prognosis of Patients Treated with Immunotherapy

Background

Recently, immunotherapies have been approved for advanced muscle invasive bladder cancer (MIBC) treatment, but only a small fraction of MIBC patients could achieve a durable drug response. Our study is aimed at identifying tumor microenvironment (TME) subtypes that have different immunotherapy response rates.

Methods

The mRNA expression profiles of MIBC samples from seven discovery datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, GSE48276, and GSE69795) were analyzed to identify TME subtypes. The identified TME subtypes were then validated by an independent dataset (TCGA-MIBC). The subtype-related biomarkers were discovered using computational analyses and then utilized to establish a random forest predictive model. The associations of TME subtypes with immunotherapy therapeutic responses were investigated in a group of patients who had been treated with immunotherapy. A prognostic index model was constructed using the subtype-related biomarkers. Two nomograms were built by the subtype-related biomarkers or the clinical parameters.

Results

Two TME subtypes, including ECM-enriched class (EC) and immune-enriched class (IC), were found. EC was associated with greater extracellular matrix (ECM) pathways, and IC was correlated with immune pathways, respectively. Overall survival was significantly greater for tumors classified as IC, whereas the EC subtype had a worse prognosis. A total of nine genes (AKAP12, APOL3, CXCL13, CXCL9, GBP4, LRIG1, PEG3, PODN, and PTPRD) were selected by computational analyses to construct the random forest model. The area under the curve (AUC) values for this model were 0.827 and 0.767 in the testing and external validation datasets, respectively. Therapeutic response rates were greater in IC patients than in EC patients (28 percent vs. 18 percent). Patients with a high prognostic index had a poorer prognosis than those with a low prognostic index. The nomogram constructed from nine genes and stage achieved a C-index of 0.71.

Conclusion

The present investigation defined two distinct TME subtypes and developed models to assess immunotherapeutic treatment outcomes.

Characterization of the Different Subtypes of Immune Cell Infiltration to Aid Immunotherapy

Background?PD-1 ablation or PD-L1 specific monoclonal antibody against PD-1 can recruit the accumulation of functional T cells, leading to tumor rejection in the microenvironment and significantly improving the prognosis of various cancers. Despite these unprecedented clinical successes, intervention remission rates remain low after treatment, rarely exceeding 40%. The observation of PD-1/L1 blocking in patients is undoubtedly multifactorial, but the infiltrating degree of CD8+T cell may be an important factor for immunotherapeutic resistance.

Methods:We proposed two computational algorithms to reveal the immune cell infiltration (ICI) landscape of 1646 lung adenocarcinoma patients. Three immune cell infiltration patterns were defined and the relative ICI scoring depended on principal-component analysis.

Results:A high ICI score was associated with the increased tumor mutation burden and cell proliferation-related signaling pathways. Different cellular signaling pathways were observed in low ICI score subtypes, indicating active cell proliferation, and may be associated with poor prognosis.

Conclusion:Our research identified that the ICI scores worked as an effective immunotherapy index, which may provide promising therapeutic strategies on immune therapeutics for lung adenocarcinoma.

Construction of an Immune Cell Infiltration Score to Evaluate the Prognosis and Therapeutic Efficacy of Ovarian Cancer Patients

Background

Ovarian cancer (OC) is an immunogenetic disease that contains tumor-infiltrating lymphocytes (TILs), and immunotherapy has become a novel treatment for OC. With the development of next-generation sequencing (NGS), profiles of gene expression and comprehensive landscape of immune cells can be applied to predict clinical outcome and response to immunotherapy.

Methods

We obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and applied two computational algorithms (CIBERSORT and ESTIMATE) for consensus clustering of immune cells. Patients were divided into two subtypes using immune cell infiltration (ICI) levels. Then, differentially expressed genes (DEGs) associated with immune cell infiltration (ICI) level were identified. We also constructed ICI score after principle-component analysis (PCA) for dimension reduction.

Results

Patients in ICI cluster B had better survival than those in ICI cluster A. After construction of ICI score, we found that high ICI score had better clinical OS and significantly higher tumor mutation burden (TMB). According to the expression of immune checkpoints, the results showed that patients in high ICI group showed high expression of CTLA4, PD1, PD-L1, and PD-L2, which implies that they might benefit from immunotherapy. Besides, patients in high ICI group showed higher sensitivity to two first-line chemotherapy drugs (Paclitaxel and Cisplatin).

Conclusion

ICI score is an effective prognosis-related biomarker for OC and can provide valuable information on the potential response to immunotherapy.