Tumor‑infiltrating M2 macrophages driven by specific genomic alterations are associated with prognosis in bladder cancer

Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery

Objective

This study aimed to identify and analyze immunogenic cell death (ICD)-related multi-omics features in bladder cancer (BLCA) using single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. By integrating these datasets, we sought to construct a prognostic signature (ICDRS) and explore its clinical and biological implications, including its association with immune cell infiltration, tumor microenvironment (TME), and drug sensitivity.

Methods

Publicly available datasets from TCGA and GEO, including scRNA-seq (GSE222315, 9 samples) and bulk RNA-seq (TCGA-BLCA, 403 samples; GSE13507, 160 samples), were analyzed. Single-cell data were processed using Seurat, and ICD scores were calculated using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) identified ICD-related modules, and machine learning algorithms (Lasso, Ridge, CoxBoost) were employed to construct the ICDRS. Survival analysis, immune infiltration, pathway enrichment, and drug sensitivity were evaluated to validate the model.

Results

The ICDRS, based on eight key genes (IL32, AHNAK, ANXA5, FN1, GSN, CNN3, FXYD3, CTSS), effectively stratified BLCA patients into high- and low-risk groups with significant differences in overall survival (OS, P < 0.001). High ICDRS scores were associated with immune-suppressive TME, including increased infiltration of T cells CD4 memory resting (P = 0.02) and macrophages M0/M1/M2 (P = 0.01). Pathway enrichment revealed correlations with cholesterol homeostasis, epithelial-mesenchymal transition (EMT), and KRAS signaling. Drug sensitivity analysis showed high-risk groups were resistant to Cisplatin (P = 0.003), Mitomycin C (P = 0.01), and Paclitaxel (P = 0.004), with IC50 values significantly higher than low-risk groups.

Conclusion

The ICDRS serves as a robust prognostic biomarker for BLCA, offering insights into tumor immune evasion mechanisms and potential therapeutic targets. Its integration with clinical features enhances personalized treatment strategies, highlighting the importance of ICD in BLCA immunotherapy and precision medicine. The model's predictive accuracy and biological relevance were validated across multiple datasets, underscoring its potential for clinical application.

Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer

BACKGROUND

Mitotic catastrophe is well-known as a major pathway of endogenous tumor death, but the prognostic significance of its heterogeneity regarding bladder cancer (BLCA) remains unclear.

METHODS

Our study focused on digging deeper into the TCGA and GEO databases. Through differential expression analysis as well as Weighted Gene Co-expression Network Analysis (WGCNA), we identified dysregulated mitotic catastrophe-associated genes, followed by univariate cox regression as well as ten machine learning algorithms to construct robust prognostic models. Based on prognostic stratification, we revealed intergroup differences by enrichment analysis, immune infiltration assessment, and genomic variant analysis. Subsequently by multivariate cox regression as well as survshap(t) model we screened core prognostic gene and identified it by Mendelian randomization. Integration of qRT-PCR, immunohistochemistry, and single-cell analysis explored the core gene expression landscape. In addition, we explored the ceRNA axis containing upstream non-coding RNAs after detailed analysis of pathway activation, immunoregulation, and methylation functions of the core genes. Finally, we performed drug screening and molecular docking experiments based on the core gene in the DSigDB database.

RESULTS

Our efforts culminated in the establishment of an accurate prognostic model containing 16 genes based on Coxboost as well as the Random Survival Forest (RSF) algorithm. Detailed analysis from multiple perspectives revealed a strong link between model scores and many key indicators: pathway activation, immune infiltration landscape, genomic variant landscape, and personalized treatment. Subsequently ANLN was identified as the core of the model, and prognostic analysis revealed that it portends a poor prognosis, further corroborated by Mendelian randomization analysis. Interestingly, ANLN expression was significantly upregulated in cancer cells and specifically clustered in epithelial cells and provided multiple pathways to mediate cell division. In addition, ANLN regulated immune infiltration patterns and was also inseparable from overall methylation levels. Further analysis revealed potential regulation of the MIR4435-2HG, hsa-miR-15a-5p, ANLN axis and highlighted a range of potential therapeutic agents including Phytoestrogens.

CONCLUSION

The model we developed was a powerful predictive tool for BLCA prognosis and revealed the impact of mitotic catastrophe heterogeneity on BLCA in multiple dimensions, which then guided clinical decision-making. Furthermore, we highlighted the potential of ANLN as a BLCA target.

CAR-macrophage therapy for HER2-overexpressing advanced solid tumors: a phase 1 trial

Chimeric antigen receptor (CAR) macrophages (CAR-Ms) mediate antitumor immunity via phagocytosis, cytokine release, activation of the tumor microenvironment and antigen presentation. We report results from a non-prespecified interim analysis of a first-in-human, phase 1 clinical trial of CT-0508, an anti-human epidermal growth factor receptor 2 (HER2) CAR-M in patients with advanced HER2-overexpressing tumors. Fourteen patients were treated across two different regimens. Patients with breast cancer and gastroesophageal cancer were primarily enrolled and had to have demonstrated overexpression of HER2 according to the American Society of Clinical Oncology/College of American Pathologists guidelines (HER2 immunohistochemistry 3+ or immunohistochemistry 2+/in situ hybridization-amplified). No lymphodepletion chemotherapy was used before infusion. The primary endpoints were safety and CAR-M manufacturability. Secondary endpoints included cellular kinetics and efficacy using objective response rate, overall survival, progression-free survival and duration of response. No dose-limiting toxicities, severe cytokine release syndrome (≥grade 3) or immune effector cell-associated neurotoxicity syndrome were observed; 44% (n = 4 of 9, 95% confidence interval = 14-79%) of HER2 3+ tumors achieved stable disease as best overall response 8 weeks after treatment. No meaningful activity was observed in the HER2 2+ population (n = 5). Correlative analyses of serial biopsies confirmed that CT-0508 traffics to and remodels the tumor microenvironment, resulting in expansion of CD8+ T cells. These findings demonstrate the preliminary safety, tolerability and manufacturing feasibility of CT-0508 for HER2+ tumors. ClinicalTrials.gov registration: NCT04660929 .

Integrated radiomics and immune infiltration analysis to decipher immunotherapy efficacy in lung adenocarcinoma

Background

Research in recent years has witnessed unprecedented improvements in immunotherapy, especially immune checkpoint blockade (ICB) for the treatment of lung adenocarcinoma (LUAD) patients. Nevertheless, due to the heterogeneity of immunotherapy response, reliable biomarkers are urgently needed to guide precision cancer therapy. In this study, we aimed to identify immune subtypes in LUAD and develop a radiogenomic model to improve immunotherapy predictive accuracy.

Methods

In this study, clinical data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) databases, and immune subtypes were identified using the ConsensusClusterPlus package in R. Biological, genomic, and epigenomic distinctions were compared. The TCGA cohort and clinical cohort from the Third Xiangya Hospital were utilized to demonstrate no significant differences of survival probability between sexes. Feature extraction and definition were conducted from 103 computed tomography (CT) images from The Cancer Imaging Archive (TCIA) dataset via the "PyRadiomics" embedded in Python. A series of machine learning techniques were applied to build a radiogenomic model.

Results

Two LUAD subtypes with different molecular and immune characteristics were identified. Significant differences in biological, genomic, and epigenomic distinctions among the two subtypes were observed (P<0.05). The immune subtype A participated in pathways related to immune activation and displayed a higher tumor microenvironment (TME) score (P<0.001) with a better prognosis of LUAD [overall survival (OS), P=0.037; disease-specific survival (DSS), P=0.034]. Besides, the model appears to show better fit for females (P=0.015) than for males (P=0.641). Our constructed radiogenomic model incorporating 12 radiomics features displayed satisfactory potential to facilitate the predictive accuracy of immunotherapy in LUAD [test area under the curve (AUC) =0.89; train AUC =0.95].

Conclusions

Our study presented a promising avenue to harness the rich radiomics data to identify the specific immune subtype and integrate it into the existing clinical decision-making system to facilitate the predictive accuracy of immunotherapy in LUAD.

Machine Learning Accurately Predicts Muscle Invasion of Bladder Cancer Based on Three miRNAs

The aim of this study was to validate the diagnostic potential of four previously identified miRNAs in two independent cohorts and to develop accurate classification models to predict invasiveness of bladder cancer. Furthermore, molecular subtypes were investigated. The miRNAs were isolated from pTa low-grade (lg) (n = 113), pT1 high-grade (hg) (n = 133) and muscle-invasive bladder cancer (MIBC) (n = 136) tumour tissue samples (FFPE) after either transurethral resection of a bladder tumour (TURB) or cystectomy (CYS). In both cohorts, the expression of miR-138-5p and miR-200a-3p was significantly lower, and the expression of miR-146b-5p and miR-155-5p was significantly higher in MIBC compared to pTa lg. A k-nearest neighbours (KNN) classifier trained to distinguish pTa lg from MIBC based on three miRNAs achieved an accuracy of 0.94. The accuracy remained at 0.91 when the classifier was applied exclusively to the TURB samples. To guarantee reliable predictions, a conformal prediction approach was applied to the KNN model, which eliminated all misclassifications on the test cohort. pT1 hg samples were classified as MIBC in 32% of cases using the KNN model. miR-146b-5p, miR-155-5p and miR-200a-3p expressions are significantly associated with particular molecular subtypes. In conclusion, we confirmed that the four miRNAs significantly distinguish MIBC from NMIBC. A classification model based on three miRNAs was able to accurately classify the phenotype of invasive tumors. This could potentially support the histopathological diagnosis in bladder cancer and therefore, the clinical decision between performing a radical cystectomy and pursuing bladder-conserving strategies, especially in pT1 hg tumors.

Development and validation of a prognostic model based on m6A-related lncRNAs to predict prognosis for papillary renal cell cancer patients

To evaluate the predictive utility of N6-methyladenosine (m6A)-associated long non-coding RNAs (lncRNAs) for the prognosis and immunotherapy response in papillary renal cell carcinoma (pRCC). Transcriptomic data of pRCC samples were extracted from the TCGA database. The m6A-related lncRNAs were identified by Pearson correlation analysis. Univariate and LASSO regression analyses were used to develop a risk model. The discrimination and predictive ability were evaluated through survival analysis, ROC analysis and consensus clustering. Tumor mutation burden (TMB) and immune infiltration of the risk groups were compared. A prognostic nomogram was constructed using six m6A-related lncRNAs, and validated through calibration and decision curve analysis (DCA). The lncRNAs HCG25 and NOP14-AS1 were knocked down in a human pRCC cell line using specific siRNA constructs, and the proliferation and migration rates were assessed by the CCK-8 and transwell assays. We identified a total of 153 m6A-related lncRNAs in pRCC datasets, of which six were selected for constructing a m6A-related lncRNA pRCC prognostic model. Mutations in the SETD2 gene correlated with worse prognosis. Significant differences were observed in immune cell infiltration between the two risk groups. A clinical prognostic nomogram for pRCC was further established based on clinical variables. In vitro assays further showed that HCG25 and NOP14-AS1 regulate the proliferation and migration of pRCC cells. The results validated the discrimination ability of both the m6A-related lncRNA pRCC prognostic model and the pRCC clinical prognostic nomogram. We developed a clinical prognostic nomogram for pRCC using pRCC prognostic-associated m6A-related lncRNAs, which can be utilized for predicting the prognosis and immune landscape of pRCC patients.

Discovery of a T cell proliferation-associated regulator signature correlates with prognosis risk and immunotherapy response in bladder cancer

BACKGROUND

The efficacy of immunotherapy is heavily influenced by T cell activity. This study aimed to examine how T cell proliferation regulators can predict the prognosis and response to immunotherapy in patients with bladder cancer (BCa).

METHODS

T cell proliferation-related subtypes were determined by employing the non-negative matrix factorization (NMF) algorithm that analyzed the expression patterns of T cell proliferation regulators. Subtypes were assessed for variations in prognosis, immune infiltration, and functional behaviors. Subsequently, a risk model related to T cell proliferation was created through Cox and Lasso regression analyses in the TCGA cohort and then confirmed in two GEO cohorts and an immunotherapy cohort.

RESULTS

BCa patients were categorized into two subtypes (C1 and C2) according to the expression profiles of 31 T cell proliferation-related genes (TRGs) with distinct prognoses and immune landscapes. The C2 subtype had a shorter overall survival (OS), with higher levels of M2 macrophage infiltration, and the activation of cancer-related pathways than the C1 subtype. Following this, thirteen prognosis-related genes that were involved in T cell proliferation were utilized to create the prognostic signature. The model's predictive accuracy was confirmed by analyzing both internal and external datasets. Individuals in the high-risk category experienced a poorer prognosis, increased immunosuppressive factors in the tumor microenvironment, and diminished responses to immunotherapy. Additionally, the immunotherapeutic prediction efficacy of the model was further confirmed by an immunotherapy cohort (anti-PD-L1 in the IMvigor210 cohort).

CONCLUSIONS

Our study characterized two subtypes linked to T cell proliferation in BCa patients with distinct prognoses and tumor microenvironment (TME) patterns, providing new insights into the heterogeneity of T cell proliferation in BCa and its connection to the immune landscape. The signature has prospective clinical implications for predicting outcomes and may help physicians to select prospective responders who prioritize current immunotherapy.

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.

The potential contribution of aberrant cathepsin K expression to gastric cancer pathogenesis

The role of cathepsin K (CTSK) expression in the pathogenesis and progression of gastric cancer (GC) remains unclear. Hence, the primary objective of this study is to elucidate the precise expression and biological role of CTSK in GC by employing a combination of bioinformatics analysis and in vitro experiments. Our findings indicated a significant upregulation of CTSK in GC. The bioinformatics analysis revealed that GC patients with a high level of CTSK expression exhibited enrichment of hallmark gene sets associated with angiogenesis, epithelial-mesenchymal transition (EMT), inflammatory response, KRAS signaling up, TNFα signaling via KFκB, IL2-STAT5 signaling, and IL6-JAK-STAT3 signaling. Additionally, these patients demonstrated elevated levels of M2-macrophage infiltration, which was also correlated with a poorer prognosis. The results of in vitro experiments provided confirmation that the over-expression of CTSK leads to an increase in the proliferative and invasive abilities of GC cells. However, further evaluation was necessary to determine the impact of CTSK on the migration capability of these cells. Our findings suggested that CTSK has the potential to facilitate the initiation and progression of GC by augmenting the invasive capacity of GC cells, engaging in tumor-associated EMT, and fostering the establishment of an immunosuppressive tumor microenvironment (TME).

Targeting LxCxE cleft pocket of retinoblastoma protein in M2 macrophages inhibits ovarian cancer progression

Ovarian cancer remains a major health threat with limited treatment options available. It is characterized by immunosuppressive tumor microenvironment (TME) maintained by tumor- associated macrophages (TAMs) hindering anti-tumor responses and immunotherapy efficacy. Here we show that targeting retinoblastoma protein (Rb) by disruption of its LxCxE cleft pocket, causes cell death in TAMs by induction of ER stress, p53 and mitochondria-related cell death pathways. A reduction of pro-tumor Rb high M2-type macrophages from TME in vivo enhanced T cell infiltration and inhibited cancer progression. We demonstrate an increased Rb expression in TAMs in women with ovarian cancer is associated with poorer prognosis. Ex vivo, we show analogous cell death induction by therapeutic Rb targeting in TAMs in post-surgery ascites from ovarian cancer patients. Overall, our data elucidates therapeutic targeting of the Rb LxCxE cleft pocket as a novel promising approach for ovarian cancer treatment through depletion of TAMs and re-shaping TME immune landscape.

Statement of significance

Currently, targeting immunosuppressive myeloid cells in ovarian cancer microenvironment is the first priority need to enable successful immunotherapy, but no effective solutions are clinically available. We show that targeting LxCxE cleft pocket of Retinoblastoma protein unexpectedly induces preferential cell death in M2 tumor-associated macrophages. Depletion of immunosuppressive M2 tumor-associated macrophages reshapes tumor microenvironment, enhances anti-tumor T cell responses, and inhibits ovarian cancer. Thus, we identify a novel paradoxical function of Retinoblastoma protein in regulating macrophage viability as well as a promising target to enhance immunotherapy efficacy in ovarian cancer.

m6A methylation modification and immune infiltration analysis in osteonecrosis of the femoral head

Osteonecrosis of the femoral head (ONFH) is a elaborate hip disease characterized by collapse of femoral head and osteoarthritis. RNA N6-methyladenosine (m6A) plays a crucial role in a lot of biological processes within eukaryotic cells. However, the role of m6A in the regulation of ONFH remains unclear. In this study, we identified the m6A regulators in ONFH and performed subtype classification. We identified 7 significantly differentially expressed m6A regulators through the analysis of differences between ONFH and normal samples in the Gene Expression Omnibus (GEO) database. A random forest algorithm was employed to monitor these regulators to assess the risk of developing ONFH. We constructed a nomogram based on these 7 regulators. The decision curve analysis suggested that patients can benefit from the nomogram model. We classified the ONFH samples into two m6A models according to these 7 regulators through consensus clustering algorithm. After that, we evaluated those two m6A patterns using principal component analysis. We assessed the scores of those two m6A patterns and their relationship with immune infiltration. We observed a higher m6A score of type A than that of type B. Finally, we performed a cross-validation of crucial m6A regulatory factors in ONFH using external datasets and femoral head bone samples. In conclusion, we believed that the m6A pattern could provide a novel diagnostic strategy and offer new insights for molecularly targeted therapy of ONFH.

Independent prognostic value of CLDN6 in bladder cancer based on M2 macrophages related signature

M2 macrophages are associated with the prognosis of bladder cancer. CLDN6 has been linked to immune infiltration and is crucial for predicting the prognosis in multi-tumor. The effect of CLDN6 on M2 macrophages in bladder cancer remains elusive. Here, we compared a total of 40 machine learning algorithms, then selected optimal algorithm to develop M2 macrophages-related signature (MMRS) based on the identified M2 macrophages related module. MMRS predicted the prognosis better than other models and associated to immunotherapy response. CLDN6, as an important variable in MMRS, was an independent factor for poor prognosis. We found that CLDN6 was highly expressed and affected immune infiltration, immunotherapy response, and M2 macrophages polarization. Meanwhile, CLDN6 promoted the growth of bladder cancer and enhanced the carcinogenic effect by inducing polarization of M2 macrophages. In total, CLDN6 is an independent risk factor in MMRS to predict the prognosis of bladder cancer.

COL10A1 as a Prognostic Biomarker in Association with Immune Infiltration in Prostate Cancer

BACKGROUND

The collagen type X alpha 1 (COL10A1) has recently been found to play an important role in the development and progression of cancer. However, the link between COL10A1 and the tumor immune microenvironment remains understood scantily.

METHODS

In the current study, the pan-cancer data of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were used to investigate the expression mode, the clinical prognostic and diagnostic value of COL10A1 in different tumors. We used TCGA data to assess the correlations between COL10A1 and clinical symptoms of prostate cancer. The R packages "edgR" and "clusterProfiler" were used for differential expression gene and enrichment analysis of COL10A1. Immunohistochemistry was further employed to corroborate the expression of COL10A1 gene in prostate cancer. After that, we used TIMER to evaluate the pertinence of COL10A1 expression to immune infiltration level in prostate cancer.

RESULTS

On the whole, COL10A1 was expressed at significantly higher levels in a variety of tumor tissues than in the corresponding normal tissues. Besides, significant correlations with tumor prognosis and relative exactitude in predicting tumors show that COL10A1 may be a probable prognostic and diagnostic biomarker of prostate cancer. In addition, the evidence indicates a significant correlation between COL10A1 and clinical symptoms of prostate cancer. Furthermore, the main molecular functions of COL10A1 included humoral immune response, complement activation, immunoglobulin, regulation of complement activation, and regulation of humoral immune response. Finally, we found that COL10A1 expression is positively correlated with enhanced macrophage and M2 macrophage infiltration in prostate cancer.

CONCLUSION

The study indicates that COL10A1 might participate in M2 macrophage polarization in prostate cancer. COL10A1 might be an innovative biomarker to evaluate tumor microenvironment immune cell infiltration and prognosis in prostate cancer.

The CAR macrophage cells, a novel generation of chimeric antigen-based approach against solid tumors

Today, adoptive cell therapy has many successes in cancer therapy, and this subject is brilliant in using chimeric antigen receptor T cells. The CAR T cell therapy, with its FDA-approved drugs, could treat several types of hematological malignancies and thus be very attractive for treating solid cancer. Unfortunately, the CAR T cell cannot be very functional in solid cancers due to its unique features. This treatment method has several harmful adverse effects that limit their applications, so novel treatments must use new cells like NK cells, NKT cells, and macrophage cells. Among these cells, the CAR macrophage cells, due to their brilliant innate features, are more attractive for solid tumor therapy and seem to be a better candidate for the prior treatment methods. The CAR macrophage cells have vital roles in the tumor microenvironment and, with their direct effect, can eliminate tumor cells efficiently. In addition, the CAR macrophage cells, due to being a part of the innate immune system, attended the tumor sites. With the high infiltration, their therapy modulations are more effective. This review investigates the last achievements in CAR-macrophage cells and the future of this immunotherapy treatment method.

The G protein-coupled receptor-related gene signatures for predicting prognosis and immunotherapy response in bladder urothelial carcinoma

Bladder urothelial carcinoma (BLCA) is the most common malignant tumor of the urinary tract with a high lethality rate, and its immunotherapy resistance and tumor recurrence have become a major challenge in its clinical treatment. G Protein-Coupled Receptors (GPRs) are the largest family of receptors on the cell membrane surface, involved in multiple signaling pathways, and are excellent targets for oncology drug action. The transcriptome profile, single cell transcriptome profile, and clinical data of BLCA were extracted and integrated from TCGA and GEO databases, respectively. The GPR-related genes were obtained from GSEA-MSigDB database. The GPR-related gene signatures of 15 genes were constructed by using the methods of least absolute shrinkage and selection operator regression, multifactor Cox model. At the same time, tumor microenvironment (TME)-score signatures were constructed based on the immune microenvironment of BLCA, and GPR-TME-score signature was further constructed. The stability of this model was verified by using the external dataset GSE160693. We constructed risk groups by combining BLCA patient prognostic information, and with the help of BLCA scRNA transcriptome profiling, we explored differences in prognosis, immune scores, cell-cell interactions, tumor mutational burden, immune checkpoints, and response to immunotherapy in each risk group. We found that the GPR-TME-score signature was an independent prognostic factor for BLCA patients. the TME-score was a protective factor for the prognosis of BLCA patients. Among BLCA patients, GPR-high + TME-low risk group had the worst prognosis, while GPR-high + TME-high risk group had the best prognosis, and the latter had better immune score and immunotherapy response. The above differences in immune response among the subgroups may be related to the higher immune cell infiltration in the GPR-high + TME-high group. GPR-related gene signatures and TME are closely related to BLCA prognosis and immunotherapy, and GPR-related gene signature can be a useful tool to assess BLCA prognosis and immunotherapy response.

Tumour microenvironment as a predictive factor for immunotherapy in non-muscle-invasive bladder cancer

Bladder cancer (BC) can be divided into two subgroups depending on invasion of the muscular layer: non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). Its aggressiveness is associated, inter alia, with genetic aberrations like losses of 1p, 6q, 9p, 9q and 13q; gain of 5p; or alterations in the p53 and p16 pathways. Moreover, there are reported metabolic disturbances connected with poor diagnosis-for example, enhanced aerobic glycolysis, gluconeogenesis or haem catabolism.Currently, the primary way of treatment method is transurethral resection of the bladder tumour (TURBT) with adjuvant Bacillus Calmette-Guérin (BCG) therapy for NMIBC or radical cystectomy for MIBC combined with chemotherapy or immunotherapy. However, intravesical BCG immunotherapy and immune checkpoint inhibitors are not efficient in every case, so appropriate biomarkers are needed in order to select the proper treatment options. It seems that the success of immunotherapy depends mainly on the tumour microenvironment (TME), which reflects the molecular disturbances in the tumour. TME consists of specific conditions like hypoxia or local acidosis and different populations of immune cells including tumour-infiltrating lymphocytes, natural killer cells, neutrophils and B lymphocytes, which are responsible for shaping the response against tumour neoantigens and crucial pathways like the PD-L1/PD-1 axis.In this review, we summarise holistically the impact of the immune system, genetic alterations and metabolic changes that are key factors in immunotherapy success. These findings should enable better understanding of the TME complexity in case of NMIBC and causes of failures of current therapies.

Construction and analysis of a conjunctive diagnostic model of HNSCC with random forest and artificial neural network

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous tumor that is highly aggressive and ranks fifth among the most common cancers worldwide. Although, the researches that attempted to construct a diagnostic model were deficient in HNSCC. Currently, the gold standard for diagnosing head and neck tumors is pathology, but this requires a traumatic biopsy. There is still a lack of a noninvasive test for such a high-incidence tumor. In order to screen genetic markers and construct diagnostic model, the methods of random forest (RF) and artificial neural network (ANN) were utilized. The data of HNSCC gene expression was accessed from Gene Expression Omnibus (GEO) database; we selected three datasets totally, and we combined 2 datasets (GSE6631 and GSE55547) for screening differentially expressed genes (DEGs) and chose another dataset (GSE13399) for validation. Firstly, the 6 DEGs (CRISP3, SPINK5, KRT4, MMP1, MAL, SPP1) were screened by RF. Subsequently, ANN was applied to calculate the weights of 6 genes. Besides, we created a diagnostic model and nominated it as neuralHNSCC, and the performance of neuralHNSCC by area under curve (AUC) was verified using another dataset. Our model achieved an AUC of 0.998 in the training cohort, and 0.734 in the validation cohort. Furthermore, we used the Cell-type Identification using Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm to investigate the difference in immune cell infiltration between HNSCC and normal tissues initially. The selected 6 DEGs and the constructed novel diagnostic model of HNSCC would make contributions to the diagnosis.

Tumor-associated macrophages and Tregs influence and represent immune cell infiltration of muscle-invasive bladder cancer and predict prognosis

INTRODUCTION AND OBJECTIVE

Muscle-invasive urothelial bladder cancer (MIBC) is associated with limited response rates to systemic therapy, risk of recurrence and death. Tumor infiltrating immune cells have been associated with outcome and response to chemo-and immunotherapy in MIBC. We aimed to profile the immune cells in the tumor microenvironment (TME) to predict prognosis in MIBC and responses to adjuvant chemotherapy.

METHODS

We performed multiplex immunohistochemistry (IHC) profiling and quantification of immune and stromal cells (CD3, CD4, CD8, CD163, FoxP3, PD-1, and CD45, Vimentin, αSMA, PD-L1, Pan-Cytokeratin, Ki67) in 101 patients with MIBC receiving radical cystectomy. We used uni- and multivariate survival analyses to identify cell types predicting prognosis. Samples were subdivided using K-means clustering for Treg and macrophage infiltration resulting in 3 clusters, Cluster 1: Treg high, cluster 2: macrophage high, cluster 3: Treg and macrophage low. Routine CD68 and CD163 IHC were analyzed with QuPath in an extended cohort of 141 MIBC.

RESULTS

High concentrations of macrophages were associated with increased risk of death (HR 10.9, 95% CI 2.8-40.5; p < 0.001) and high concentrations of Tregs were associated with decreased risk of death (HR 0.1, 95% CI 0.01-0.7; p = 0.03) in the multivariate Cox-regression model adjusting for adjuvant chemotherapy, tumor and lymph node stage. Patients in the macrophage rich cluster (2) showed the worst OS with and without adjuvant chemotherapy. The Treg rich cluster (1) showed high levels of effector and proliferating immune cells and had the best survival. Cluster 1 and 2 both were rich in PD-1 and PD-L1 expression on tumor and immune cells.

CONCLUSION

Treg and macrophage concentrations in MIBC are independent predictors of prognosis and are important players in the TME. Standard IHC with CD163 for macrophages is feasible to predict prognosis but validation to use immune-cell infiltration, especially to predict response to systemic therapies, is required.

Syngeneic model of carcinogen-induced tumor mimics basal/squamous, stromal-rich, and neuroendocrine molecular and immunological features of muscle-invasive bladder cancer

Introduction

Bladder cancer is a heterogenous disease and the emerging knowledge on molecular classification of bladder tumors may impact treatment decisions based on molecular subtype. Pre-clinical models representing each subtype are needed to test novel therapies. Carcinogen-induced bladder cancer models represent heterogeneous, immune-competent, pre-clinical testing options with many features found in the human disease.

Methods

Invasive bladder tumors were induced in C57BL/6 mice when continuously exposed to N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN) in the drinking water. Tumors were excised and serially passed by subcutaneous implantation into sex-matched syngeneic C57BL/6 hosts. Eight lines were named BBN-induced Urothelium Roswell Park (BURP) tumor lines. BURP lines were characterized by applying consensus molecular classification to RNA expression, histopathology, and immune profiles by CIBERSORT. Two lines were further characterized for cisplatin response.

Results

Eight BURP tumor lines were established with 3 male and 3 female BURP tumor lines, having the basal/squamous (BaSq) molecular phenotype and morphology. BURP-16SR was established from a male mouse and has a stromal-rich (SR) molecular phenotype and a sarcomatoid carcinoma morphology. BURP-19NE was established from a male mouse and has a neuroendocrine (NE)-like molecular phenotype and poorly differentiated morphology. The established BURP tumor lines have unique immune profiles with fewer immune infiltrates compared to their originating BBN-induced tumors. The immune profiles of the BURP tumor lines capture some of the features observed in the molecular classifications of human bladder cancer. BURP-16SR growth was inhibited by cisplatin treatment, while BURP-24BaSq did not respond to cisplatin.

Discussion

The BURP lines represent several molecular classifications, including basal/squamous, stroma-rich, and NE-like. The stroma-rich (BURP-16SR) and NE-like (BURP-19NE) represent unique immunocompetent models that can be used to test novel treatments in these less common bladder cancer subtypes. Six basal/squamous tumor lines were established from both male and female mice. Overall, the BURP tumor lines have less heterogeneity than the carcinogen-induced tumors and can be used to evaluate treatment response without the confounding mixed response often observed in heterogeneous tumors. Additionally, basal/squamous tumor lines were established and maintained in both male and female mice, thereby allowing these tumor lines to be used to compare differential treatment responses between sexes.

Alternative splicing event associated with immunological features in bladder cancer

Bladder cancer (BLCA) is the most prevalent urinary tumor with few treatments. Alternative splicing (AS) is closely related to tumor development and tumor immune microenvironment. However, the comprehensive analysis of AS and prognosis and immunological features in BLCA is still lacking. In this study, we downloaded RNA-Seq data and clinical information from The Cancer Genome Atlas (TCGA) database, and AS events were acquired from the TCGA Splice-seq. A total of eight prognostic AS events (C19orf57|47943|ES, ANK3|11845|AP, AK9|77203|AT, GRIK2|77096|AT, DYM|45472|ES, PTGER3|3415|AT, ACTG1|44120|RI, and TRMU|62711|AA) were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression analysis to construct a risk score model. The Kaplan-Meier analysis revealed that the high-risk group had a worse prognosis compared with the low-risk group. The area under the receiver operating characteristic (ROC) curves (AUCs) for this risk score model in 1, 3, and 5 years were 0.698, 0.742, and 0.772, respectively. One of the prognostic AS event-related genes, TRMU, was differentially expressed between tumor and normal tissues in BLCA. The single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithm showed that both the risk score model and TRMU were significantly associated with tumor immune microenvironment and immune status (immune cells, immune-related pathway, and immune checkpoint) in BLCA patients. The TIMER database confirmed the relationship between the expression of TRMU and immune cells and checkpoint genes. Furthermore, Cytoscape software 3.8.0 was used to construct the regulatory network between AS and splicing factors (SFs). Our study demonstrated that AS events were powerful biomarkers to predict the prognosis and immune status in BLCA, which may be potential therapeutic targets in BLCA.

Derivation and external validation of dendritic cell-related gene signatures for predicting prognosis and immunotherapy efficacy in bladder urothelial carcinoma

Background

In the regulation of tumor-related immunity, dendritic cells (DCs) are crucial sentinel cells; they are powerful to present antigens and initiate immune responses. Therefore, we concentrated on investigating the DC-related gene profile, prognosis, and gene mutations in bladder urothelial carcinoma (BLCA) patients to identify sensitivity to immunotherapy of patients.

Methods

According to DC infiltration, BLCA patients were divided into two subgroups, and differentially expressed genes (DEGs) were obtained. Patients were classified by unsupervised clustering into new subgroups. The least absolute shrinkage and selection operator (LASSO) regression analysis and Cox regression were used to develop a DC-related risk model. CIBERSORT, xCell, and GSEA were used to infer immune cells' relative abundance separately and enriched immune pathways.

Results

A total of 29 prognosis-related DEGs were identified from the unsupervised cluster. Among them, 22 genes were selected for constructing the DC-related risk model. The dendritic cell-related risk score (DCRS) can accurately distinguish patients with different sensitive responses to immunotherapy and overall survival outcomes. Furthermore, patients with ryanodine receptor 2 (RYR2) mutation had a better prognosis.

Conclusions

The DCRS played an essential part in immunity pathway and formation of TME diversity. Our study indicated that RYR2 mutation combined with DCRS is useful for predicting the prognosis and discovering appropriate patients for immunotherapy.

Analysis of m6A modulator-mediated methylation modification patterns and the tumor microenvironment in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer. In the development and progression of LUAD, epigenetic aberration plays a crucial role. However, the function of RNA N6-methyladenosine (m⁶A) modifications in the LUAD progression is unknown. The m6A regulator modification patterns in 955 LUAD samples were analyzed comprehensively. Patterns were systematically correlated with the tumor microenvironment (TME) cell-infiltration characteristics. Using principal component analysis algorithms, the m6Ascore was generated to quantify m⁶A modification patterns in individual tumors. Then, their values for predicting prognoses and therapeutic response in LUAD patients were assessed. Three distinct m⁶A modification patterns in LUAD were identified. Among them, the prognosis of m6Acluster C was the best, while the prognosis of m⁶Acluster A was the worst. Interestingly, the characterization of TME cell infiltration and biological behavior differed among the three patterns. To evaluate m⁶A modification patterns within individual tumors, an m⁶Ascore signature was constructed. The results showed that the high m⁶Ascore group was associated with a better prognosis; tumor somatic mutations and tumor microenvironment differed significantly between the high- and low- m⁶Ascore groups. Furthermore, in the cohort with anti-CTLA-4 treatment alone, patients with a high m⁶Ascore had higher ICI scores, which indicated significant therapeutic advantage and clinical benefits.

Construction of the prognostic signature of alternative splicing revealed the prognostic predictor and immune microenvironment in head and neck squamous cell carcinoma

Background: Head and neck squamous cell carcinoma (HNSC) is a prevalent and heterogeneous malignancy with poor prognosis and high mortality rates. There is significant evidence of alternative splicing (AS) contributing to tumor development, suggesting its potential in predicting prognosis and therapeutic efficacy. This study aims to establish an AS-based prognostic signature in HNSC patients.

Methods: The expression profiles and clinical information of 486 HNSC patients were downloaded from the TCGA database, and the AS data were downloaded from the TCGA SpliceSeq database. The survival-associated AS events were identified by conducting a Cox regression analysis and utilized to develop a prognostic signature by fitting into a LASSO-regularized Cox regression model. Survival analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve analysis were performed to evaluate the signature and an independent cohort was used for validation. The immune cell function and infiltration were analyzed by CIBERSORT and the ssGSEA algorithm.

Results: Univariate Cox regression analysis identified 2726 survival-associated AS events from 1714 genes. The correlation network reported DDX39B, PRPF39, and ARGLU1 as key splicing factors (SF) regulating these AS events. Eight survival-associated AS events were selected and validated by LASSO regression to develop a prognostic signature. It was confirmed that this signature could predict HNSC outcomes independent of other variables via multivariate Cox regression analysis. The risk score AUC was more than 0.75 for 3 years, highlighting the signature’s prediction capability. Immune infiltration analysis reported different immune cell distributions between the two risk groups. The immune cell content was higher in the high-risk group than in the low-risk group. The correlation analysis revealed a significant correlation between risk score, immune cell subsets, and immune checkpoint expression.

Conclusion: The prognostic signature developed from survival-associated AS events could predict the prognosis of HNSC patients and their clinical response to immunotherapy. However, this signature requires further research and validation in larger cohort studies.

Comprehensive analysis of m5C-Related lncRNAs in the prognosis and immune landscape of hepatocellular carcinoma

5-Methyladenosine (m5C) is a type of epigenetic modification involved in the progression of various cancers. To investigate the role of m5C-related long non-coding RNAs (lncRNAs) in the prognosis and immune cell infiltration in hepatocellular carcinoma (HCC), we obtained patients' clinical information and transcriptome data of HCC from the Cancer Genome Atlas (TCGA) database. We applied Pearson correlation analysis to construct an m5C-related lncRNA-messenger RNA (mRNA) co-expression network. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analysis were employed to establish an m5C-related lncRNA prognostic risk model. We then verified the model using Kaplan-Meier analysis, principal component analysis, as well as univariate and multivariate Cox analyses. The expression of m5C-related lncRNAs was validated in HCC tissues and different cell lines. Combining the risk score and clinicopathological features, a nomogram was established for predicting the overall survival (OS) of HCC patients. Furthermore, gene set enrichment analysis (GSEA) revealed that some tumor-associated pathways were significantly enriched in the high-risk group. Immune cell infiltration analysis demonstrated that the levels of Treg cells, neutrophils, and M2 macrophages were higher in the high-risk group. In addition, patients with high tumor mutation burden (TMB) had worse OS than those with low TMB. We also assessed the immune checkpoint level and chemotherapeutic agent sensibility. Then in vitro experiments were performed to examine the biological function of MKLN1-AS in HCC cells and found that knockdown of MKLN1-AS suppressed the proliferation, migration, and invasion. In conclusion, m5C-related lncRNAs played a critical role in predicting the prognosis of patients with HCC and may serve as new therapeutic targets for HCC patients.

Significance of tumor-associated macrophages in bladder cancer development

Bladder cancer is the 2nd most common urological oncological disease in the worlds. Tumors can be muscle invasive and non-muscle invasive. Recently, tumor microenvironment (TME) became a focus of investigation in malignant tumors of the bladder. According to the currently available data, TME is a specific environment crating optimal conditions for carcinogenesis in the neoplastic lesion. The main parts of TME are extracellular matrix and stroma including vasculature, stromal, and immune cells. Additionally, TME includes cytokines, chemokines, and other compounds activating signal pathways necessary for tumor cells. Tumor-associated macrophages (TAMs) are being extensively studied as representatives of TME in solid tumors of varying locations. These macrophages can be classified into 2 phenotypes: M1 (pro-inflammatory and antitumor) and M2 (anti-inflammatory and protumor). The phenotypes perform different roles, and M2 macrophages regulate the most important processes of oncogenesis (invasion, proliferation, neoangiogenesis, etc.). In the context of bladder cancer, M2 macrophages are the most significant as they are the most numerous TAMs in TME.

Aim. To study the role of tumor-associated macrophages in development of bladder tumors, as well as prognostic value of these macrophages.

The tumor microenvironment and prognostic role of autophagy- and immune-related genes in bladder cancer

BACKGROUND: Autophagy-related genes and immune-related genes contribute significantly to the initiation and prognosis of bladder cancer (BLCA). OBJECTIVE: We aimed to explore differentially expressed autophagy-related genes (DEARGs) and immune-related genes (DEIRGs) in BLCA to create a prognostic risk assessment model and gain some insights into BLCA’s molecular underpinnings. METHODS: The prognostic DEARGs and DEIRGs were evaluated for BLCA through The Cancer Genome Atlas (TCGA) database (n= 399) and GSE13507 dataset (n= 165). The BLCA risk model was constructed and verified. The immune score, stromal score, and estimate score in different risk groups were calculated by the ESTIMATE algorithm. Immune infiltration levels were assessed by a single sample gene set enrichment analysis (GSEA) algorithm. RESULTS: In the risk model, AURKA, ACTC1, MYLK, PDGFD, PDGFRA and TNC were significantly associated with the overall survival. The pathways in cancer, T cell receptor signaling pathway and B cell receptor signaling pathway were significantly gathered in the high-risk group. Moreover, the risk score was significantly correlated with infiltrating immune cells, expression of critical immune checkpoints and mismatch repair genes including MSH6, MLH1, and MSH2. CONCLUSIONS: In this study, three DEARGs (AURKA, ACTC1, MYLK) and three DEIRGs (PDGFD, PDGFRA, TNC) were demonstrated to be potential prognostic biomarkers for BLCA patients through bioinformatics methods, which might be novel therapeutic targets and prognostic markers for BLCA, in follow up studies, we will combine experiments to verify this.

Potential biomarkers and immune characteristics of small bowel adenocarcinoma

Small bowel adenocarcinoma (SBA) is a gastrointestinal malignancy with low incidence but poor prognosis, and its pathogenesis is still unclear. This study aimed to explore potential disease-causing biomarkers of SBA. The gene expression datasets of SBA and normal samples were downloaded from the Gene Expression Omnibus database. First, differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed. Common genes (CGs) were obtained by intersection of differentially expressed genes (DEGs) and optimal modal genes of WGCNA. Subsequently, a protein‒protein interaction network was established to screen hub genes, and target genes were obtained by Lasso regression analysis of hub genes. An SBA risk prediction model was established based on target genes. The prediction accuracy of the model was evaluated by the area under the receiver operating characteristic curve (AUC). The levels of immune cell infiltration and activation of immune pathways were compared between SBA and normal samples using the "ggpubr" and "reshape2" packages. A total of 1058 DEGs were identified. WGCNA showed that the signature gene in the brown module was significantly associated with SBA (p = 7E−17), and 469 CGs were obtained. Four target genes (APOA4, APOB, COL1A2, FN1) were identified and showed excellent prediction of SBA risk (AUC = 0.965). In addition, active dendritic cells and macrophages showed higher infiltration levels in SBA. Meanwhile, the APC_co_stimulation pathway and parainflammation pathway were strongly active in SBA. Four target genes (APOA4, APOB, COL1A2, FN1) may be involved in the pathogenesis of small bowel adenocarcinoma.

Identification and analysis of microRNA editing events in recurrent bladder cancer based on RNA sequencing: MicroRNA editing level is a potential novel biomarker

Background: With the continued advancement of RNA-seq (RNA-sequencing), microRNA (miRNA) editing events have been demonstrated to play an important role in different malignancies. However, there is yet no description of the miRNA editing events in recurrent bladder cancer.

Objective: To identify and compare miRNA editing events in primary and recurrent bladder cancer, as well as to investigate the potential molecular mechanism and its impact on patient prognosis.

Methods: We examined the mRNA and miRNA transcriptomes of 12 recurrent bladder cancer cases and 13 primary bladder cancer cases. The differentially expressed mRNA sequences were analyzed. Furthermore, we identified the differentially expressed genes (DEGs) in recurrent bladder cancer. The Gene Ontology (GO) functional enrichment analyses on DEGs and gene set enrichment analysis were performed. The consensus molecular subtype (CMS) classification of bladder cancer was identified using the Consensus MIBC package in R (4.1.0); miRNA sequences were then further subjected to differentially expressed analysis and pathway enrichment analysis. MiRNA editing events were identified using miRge3.0. miRDB and TargetScanHuman were used to predict the downstream targets of specific differentially edited or expressed miRNAs. The expression levels of miR-154-5p and ADAR were validated by RT-qPCR. Finally, survival and co-expression studies were performed on the TCGA-BLCA cohort.

Results: First, the mRNA expression levels in recurrent bladder cancer changed significantly, supporting progression via related molecular signal pathways. Second, significantly altered miRNAs in recurrent bladder cancer were identified, with miR-154-5p showing the highest level of editing in recurrent bladder cancer and may up-regulate the expression levels of downstream targets HS3ST3A1, AQP9, MYLK, and RAB23. The survival analysis results of TCGA data revealed that highly expressed HS3ST3A1 and RAB23 exhibited poor prognosis. In addition, miR-154 editing events were found to be significant to CMS classification.

Conclusion: MiRNA editing in recurrent bladder cancer was detected and linked with poor patient prognosis, providing a reference for further uncovering the intricate molecular mechanism in recurrent bladder cancer. Therefore, inhibiting A-to-I editing of miRNA may be a viable target for bladder cancer treatment, allowing current treatment choices to be expanded and individualized.

The Urinary Microbiome: Role in Bladder Cancer and Treatment

Commensal microbes have increasingly been found to be involved in the development and progression of cancer. The recent discovery of the urinary microbiome bolstered the notion that microbes might play a role in bladder cancer. Although microbial involvement in bladder neoplastic transformation and metastatic progression, except schisto somiasis, has not been established, accumulating research suggests that dysbiosis of the urinary microbiome can produce a chronically inflammatory urothelial microenvironment and lead to bladder cancer. In this review, we describe how the urinary microbiome might facilitate the development of bladder cancer by altering the host immune system and the kind of cytokines that are directly involved in these responses. We investigated the therapeutic possibilities of modulating the urinary microbiome, including immune checkpoint therapy. The responsiveness of patients to intravesical Bacillus Calmette-Guerin therapy was evaluated with respect to microbiome composition. We conclude by noting that the application of microbes to orchestrate the inflammatory response in the bladder may facilitate the development of treatments for bladder cancer.

An Eleven-microRNA Signature Related to Tumor-Associated Macrophages Predicts Prognosis of Breast Cancer

The dysregulation of microRNAs (miRNAs) has been known to play important roles in tumor development and progression. However, the understanding of the involvement of miRNAs in regulating tumor-associated macrophages (TAMs) and how these TAM-related miRNAs (TRMs) modulate cancer progression is still in its infancy. This study aims to explore the prognostic value of TRMs in breast cancer via the construction of a novel TRM signature. Potential TRMs were identified from the literature, and their prognostic value was evaluated using 1063 cases in The Cancer Genome Atlas Breast Cancer database. The TRM signature was further validated in the external Gene Expression Omnibus GSE22220 dataset. Gene sets enrichment analyses were performed to gain insight into the biological functions of this TRM signature. An eleven-TRM signature consisting of mir-21, mir-24-2, mir-125a, mir-221, mir-22, mir-501, mir-365b, mir-660, mir-146a, let-7b and mir-31 was constructed. This signature significantly differentiated the high-risk group from the low-risk in terms of overall survival (OS)/ distant-relapse free survival (DRFS) (p value < 0.001). The prognostic value of the signature was further enhanced by incorporating other independent prognostic factors in a nomogram-based prediction model, yielding the highest AUC of 0.79 (95% CI: 0.72−0.86) at 5-year OS. Enrichment analyses confirmed that the differentially expressed genes were mainly involved in immune-related pathways such as adaptive immune response, humoral immune response and Th1 and Th2 cell differentiation. This eleven-TRM signature has great potential as a prognostic factor for breast cancer patients besides unravelling the dysregulated immune pathways in high-risk breast cancer.

Comprehensive analysis of the PD-L1 and immune infiltrates of N6-methyladenosine related long non-coding RNAs in bladder cancer

Bladder cancer (BLCA) is one of the most frequent genitourinary cancers, with a high rate of morbidity and mortality. The connection of m6A-related lncRNAs with PD-L1 and tumor immune microenvironment (TIME) in BLCA prognosis was extensively investigated in this study, which could suggest novel therapeutic targets for further investigation. 30 m6A-associated lncRNAs with predictive values from the TCGA data set were identified with co-expression analysis. Cluster2 was correlated with a poor prognosis, upregulated PD-L1 expression, and higher immune ratings. Cluster2 had larger amounts of resting CD4 memory-activated T cells, M2 macrophages, neutrophils, and NK cells infiltration. "CHEMOKINE SIGNALING PATHWAY" was the most significantly enriched signaling pathway according to GSEA, which may play an important role in the different immune cell infiltrates between cluster1/2. The risk model for m6A-related lncRNAs could be employed in a prognostic model to predict BLCA prognosis, regardless of other clinical features. Collectively, m6A-related lncRNAs were linked to PD-L1 and TIME, which would dynamically affect the number of tumor-infiltrating immune cells. m6A-related lncRNAs may be key mediators of PD-L1 expression and immune cells infiltration and may strongly affect the TIME of BLCA.

Expression of ferroptosis-related gene correlates with immune microenvironment and predicts prognosis in gastric cancer

The study is to explore the role of ferroptosis-related genes (FRGs) in the occurrence and development of gastric cancer (GC), and to construct a new prognosis signature to predict the prognosis in GC. Clinical information and corresponding RNA data of GC patients were downloaded from TCGA and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, CIBERSORT, McpCounter and TIMER algorithm were used to analyze the infiltration of immune cells in two molecular subgroups. LASSO algorithm and multivariate Cox analysis were used to construct a prognostic risk signature. Functional analysis was conducted to elucidate the underlying mechanisms. Finally, the FRPGs were verified by Quantitative Real-Time PCR. We obtained 16 FRGs and divided GC patients into two subgroups by consistent clustering. Cluster C1 with a higher abundance of immune cell infiltration but lower probability in response to immunotherapy, it was reasonable to speculate that Cluster C1 was in accordance with the immune rejection type. Functional analysis showed that the biological process of DEGs in training cohort mainly included immune globulin, and human immune response mediated by circulating immune globulin. GSEA analysis showed that compared with Cluster C2, Cluster C1 showed lower expression in lipid metabolism. The nomogram combined with risk signature and clinical features can accurately predict the prognosis of GC patients. We identified two molecular subtypes, Clusters C1 and C2. In Cluster C1, patients with poor prognosis present with a hyperimmune status and low lipid metabolism, and we speculate that Cluster C1 was in accordance with the immune rejection type. The risk model based on FRPGs can accurately predict the prognosis of GC. These results indicated that ferroptosis is associated with TIME, and deserved considerable attention in determining immunotherapy treatment strategy for GC patients.

M2 Tumor Associate Macrophage- (TAM-) Derived lncRNA HISLA Promotes EMT Potential in Bladder Cancer

Background

Tumor-associated macrophages (TAMs) are M2-like phenotype macrophages which contribute to the tumor progression in tumor microenvironment. The precise mechanisms of TAMs were intricated, and recently, it has been illustrated that TAM-derived exosomal lncRNAs played pivotal roles in the tumor development. In the present study, we investigated the role of TAM-derived exosomal lncRNA HISLA in bladder cancer.

Materials and Methods

Effects of TAM exosomes and exosomal lncRNA HISLA on migration and invasion in bladder cells were detected by wound healing assay, transwell assay, and western blot assay. Differential expression of lncRNA HISLA in exosomes derived from M0 or TAMs was examined by qRT-PCR. Western blot assay was used to classify the precise molecular mechanisms.

Results

We found that TAM-derived exosomes significantly promote the migration and invasion abilities of bladder cells. Expression of epithelial-mesenchymal transition (EMT) markers was obviously affected by TAM exosome administration. Furthermore, we found that the expression of lncRNA HISLA was specifically elevated in TAM exosomes and TAM exosome-treated bladder cells. Silencing of lncRNA HISLA was found to suppress the processes of migration, invasion, and EMT in bladder cells. In addition, we found that β-catenin levels were downregulated, and Ser33 phosphorylated β-catenin levels were increased by HISLA siRNA treatment. At last, we found that HISLA stabilized β-catenin expression through preventing interaction between GSK3β and β-catenin.

Conclusion

In conclusion, our results investigated the prometastatic role of exosomal lncRNA HISLA derived from TAMs in bladder cancer and suggested TAM-derived HISLA as a promising therapeutic target of bladder cancer.

Oxidative Stress and Inflammation in Cardiovascular Diseases and Cancer: Role of Non-coding RNAs

High oxidative stress, Th1/Th17 immune response, M1 macrophage inflammation, and cell death are associated with cardiovascular diseases. Controlled oxidative stress, Th2/Treg anti-tumor immune response, M2 macrophage inflammation, and survival are associated with cancer. MiR-21 protects against cardiovascular diseases but may induce tumor growth by retaining the anti-inflammatory M2 macrophage and Treg phenotypes and inhibiting apoptosis. Down-regulation of let-7, miR-1, miR-9, miR-16, miR-20a, miR-22a, miR-23a, miR-24a, miR-26a, miR-29, miR-30a, miR-34a, miR-124, miR-128, miR-130a, miR-133, miR-140, miR-143-145, miR-150, miR-153, miR-181a, miR-378, and miR-383 may aid cancer cells to escape from stresses. Upregulation of miR-146 and miR-223 may reduce anti-tumor immune response together with miR-21 that also protects against apoptosis. MiR-155 and silencing of let-7e, miR-125, and miR-126 increase anti-tumor immune response. MiR expression depends on oxidative stress, cytokines, MYC, and TGF-β, and expression of silencing lncRNAs and circ-RNAs. However, one lncRNA or circ-RNA may have opposite effects by targeting several miRs. For example, PVT1 induces apoptosis by targeting miR-16a and miR-30a but inhibits apoptosis by silencing miR-17. In addition, levels of a non-coding RNA in a cell type depend not only on expression in that cell type but also on an exchange of microvesicles between cell types and tumors. Although we got more insight into the function of a growing number of individual non-coding RNAs, overall, we do not know enough how several of them interact in functional networks and how their expression changes at different stages of disease progression.

Association of Elevated Expression Levels of COL4A1 in Stromal Cells with an Immunosuppressive Tumor Microenvironment in Low-Grade Glioma, Pancreatic Adenocarcinoma, Skin Cutaneous Melanoma, and Stomach Adenocarcinoma

Aberrant expression of collagen type IV alpha chain 1 (COL4A1) can influence tumor cell behavior. To examine the association of COL4A1 expression in the tumor microenvironment (TME) with tumor progression, we performed bioinformatics analyses of The Cancer Genome Atlas RNA sequencing and RNA microarray datasets available in public databases and identified upregulated COL4A1 expression in most examined tumor types compared to their normal counterparts. The elevated expression of COL4A1 was correlated with low survival rates of patients with low-grade glioma, pancreatic adenocarcinoma, skin cutaneous melanoma, and stomach adenocarcinoma, thus suggesting its potential use as a biomarker for the poor prognosis of these tumors. However, COL4A1 was mostly expressed in adjacent stromal cells, such as cancer-associated fibroblasts (CAFs) and endothelial cells. Additionally, COL4A1 expression was highly correlated with the signatures of CAFs and endothelial cells in all four tumor types. The expression of marker genes for the infiltration of pro-tumoral immune cells, such as Treg, M2, and TAM, and those of immunosuppressive cytokines exhibited very strong positive correlations with COL4A1 expression. Collectively, our data suggest that COL4A1 overexpression in stromal cells may be a potential regulator of tumor-supporting TME composition associated with poor prognosis.

An immune-related lncRNA model for predicting prognosis, immune landscape and chemotherapeutic response in bladder cancer

Long noncoding RNAs (lncRNAs) participate in cancer immunity. We characterized the clinical significance of an immune-related lncRNA model and evaluated its association with immune infiltrations and chemosensitivity in bladder cancer. Transcriptome data of bladder cancer specimens were employed from The Cancer Genome Atlas. Dysregulated immune-related lncRNAs were screened via Pearson correlation and differential expression analyses, followed by recognition of lncRNA pairs. Then, a LASSO regression model was constructed, and receiver operator characteristic curves of one-, three- and five-year survival were established. Akaike information criterion (AIC) value of one-year survival was determined as the cutoff of high- and low-risk subgroups. The differences in survival, clinical features, immune cell infiltrations and chemosensitivity were compared between subgroups. Totally, 90 immune-related lncRNA pairs were identified, 15 of which were screened for constructing the prognostic model. The area under the curves of one-, three- and five-year survival were 0.806, 0.825 and 0.828, confirming the favorable predictive performance of this model. According to the AIC value, we clustered patients into high- and low-risk subgroups. High-risk score indicated unfavorable outcomes. The risk model was related to survival status, age, stage and TNM. Compared with conventional clinicopathological characteristics, the risk model displayed higher predictive efficacy and served as an independent predictor. Also, it could well characterize immune cell infiltration landscape and predict immune checkpoint expression and sensitivity to cisplatin and methotrexate. Collectively, the model conducted by paring immune-related lncRNAs regardless of expressions exhibits a favorable efficacy in predicting prognosis, immune landscape and chemotherapeutic response in bladder cancer.

Construction of an Immune-Autophagy Prognostic Model Based on ssGSEA Immune Scoring Algorithm Analysis and Prognostic Value Exploration of the Immune-Autophagy Gene in Endometrial Carcinoma (EC) Based on Bioinformatics

BACKGROUND

Endometrial carcinoma (EC) is a malignant cancer spreading worldwide and in the fourth position among all other types of cancer in women. The purpose of this paper is to explore the prognostic value of the immune-autophagy gene in endometrial carcinoma (EC) based on bioinformatics, construct an immune-autophagy prognostic model of endometrial carcinoma, search for independent prognostic markers, and finally predict the potential therapeutic drugs of TCGA subgroup.

METHODS

The Cancer Genome Atlas (TCGA) database was used to extract transcriptome sequencing data of patients suffering from EC; 28 kinds of immune cells were scored by ssGSEA, and the immune subtypes were grouped by consistency cluster analysis. The accuracy and effectiveness of the grouping were verified by the analysis of differential gene expression and survival rate of immune checkpoints in the two groups to provide the premise and basis for the establishment of independent prognostic factors. The expression of different genes in high and low immune groups was analyzed. The analysis of various genes' expression in immune groups (high and low) has been performed. Go function annotation and KEGG pathway enrichment analysis were used to evaluate the difference of immune infiltration between high and low immune groups. The immune and autophagy genes were crossed, the key (hub) genes were selected, the risk was scored, the prognosis model was constructed, and the independent prognostic markers were established. CAMP and CTRP 2.0 were used to test the drug sensitivity.

RESULTS

According to the level of immune cell enrichment, the results have been subcategorized into two immune subtypes: high immunity group_ H and low immunity group_ L. Two immune subtypes, CD274, PDCD1, and CTLA4, were detected in the immune system_ H and immunity_L. A significant difference was detected between these two groups in the expression and survival rate. Few more differences were also detected between the two groups through the evaluation of immune infiltration, which proved the grouping's accuracy and effectiveness. Differential gene expression analysis showed that there were 721 DEGs and 3 hub genes. DEGs are mainly involved in lymphocyte activation, proliferation, differentiation, leukocyte proliferation, and other biological processes, mediate chemokines' activities, chemokine receptor binding, and other molecular functions, and are enriched in the outer plasma membrane, endoplasmic reticulum, and T cell receptor complex. The enriched pathways are allograft, complex, inflammatory, interferon-alpha, interferon-gamma, E2F, G2M, mitotic, etc.

CONCLUSION

Through bioinformatics analysis, we successfully constructed the immuno-autophagy prognosis model of endometrial cancer and identified three high-risk immunoautophagy genes, including VEGFA, CCL2, and Ifng. Four potential therapeutic drugs were predicted as sildenafil, sunitinib, TPCA-1, and etoposide.

Identification and validation of a twelve immune infiltration-related lncRNA prognostic signature for bladder cancer

The prognosis of bladder cancer patients is strongly related to both the immune-infiltrating cells and the expression of lncRNAs. In this study, we analyzed the infiltration of immune cells in 403 bladder cancer samples obtained from TCGA by applying the ssGSEA to these samples, then dividing them into high/low immune cell infiltration groups. Based on these groupings, we found 404 differentially expressed immune infiltration-related lncRNAs, which were successively analyzed by univariate Cox regression, then Least Absolute Shrinkage and Selection Operator (LASSO), and finally stepwise multiple Cox regression. Then 12 differentially expressed immune infiltration-related lncRNAs were identified and used to construct a prognostic signature for bladder cancer. Subsequently, Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, and multivariate time-dependent ROC analyses (for 1, 3, 5 years) all revealed that this signature performed well in predicting overall survival and served as an independent prognostic factor for patients with bladder cancer. Finally, both TIMER and CIBESORT showed that this 12-lncRNA prognostic signature for bladder cancer was associated with the infiltration of immune cell subtypes. Besides, nomogram considered risk score and clinical characteristics was assembled and showed great performance. More importantly, we found our signature could well distinguish the drug response of patients with bladder cancer. High risk patients showed a better response to cisplatin, doxorubicin, and anti- CTLA4 immunotherapy, low risk patients showed a better response to methotrexate and anti-PD1 immunotherapy compared with each other.

Establishment and Analysis of an Individualized Immune-Related Gene Signature for the Prognosis of Gastric Cancer

A growing number of studies have shown that immunity plays an important clinical role in the process of gastric cancer (GC). The purpose of this study was to explore the function of differentially expressed immune-related genes (DEIRGs) of GC, and construct a gene signature to predict the overall survival (OS) of patients. Gene expression profiles and clinical data of GC patients were downloaded from TCGA and GEO databases. Combined with immune-related genes (IRGs) downloaded from the ImmPort database, 357 DEIRGs in GC tissues and adjacent tissues were identified. Based on the analysis of Lasso and Cox in the training set, a prognostic risk scoring model consisting of 9 (RBP7, DES, CCR1, PNOC, SPP1, VIP, TNFRSF12A, TUBB3, PRKCG) DEIRGs was obtained. Functional analysis revealed that model genes may participate in the formation and development of tumor cells by affecting the function of cell gap junction intercellular communication (GJJC). According to the model score, the samples were divided into high-risk and low-risk groups. In multivariate Cox regression analysis, the risk score was an independent prognostic factor (HR = 1.674, 95% CI = 1.470–1.907, P &lt; 0.001). Survival analysis showed that the OS of high-risk GC patients was significantly lower than that of low-risk GC patients (P &lt; 0.001). The area under the receiver operating characteristic curve (ROC) of the model was greater than other clinical indicators when verified in various data sets, confirming that the prediction model has a reliable accuracy. In conclusion, this study has explored the biological functions of DEIRGs in GC and discovered novel gene targets for the treatment of GC. The constructed prognostic gene signature is helpful for clinicians to determine the prognosis of GC patients and formulate personalized treatment plans.

Identification of a novel signature based on unfolded protein response-related gene for predicting prognosis in bladder cancer

BACKGROUND

The unfolded protein response (UPR) served as a vital role in the progression of tumors, but the molecule mechanisms of UPR in bladder cancer (BLCA) have been not fully investigated.

METHODS

We identified differentially expressed unfolded protein response-related genes (UPRRGs) between BLCA samples and normal bladder samples in the Cancer Genome Atlas (TCGA) database. Univariate Cox analysis and the least absolute shrinkage and selection operator penalized Cox regression analysis were used to construct a prognostic signature in the TCGA set. We implemented the validation of the prognostic signature in GSE13507 from the Gene Expression Omnibus database. The ESTIMATE, CIBERSORT, and ssGSEA algorithms were used to explore the correlation between the prognostic signature and immune cells infiltration as well as key immune checkpoints (PD-1, PD-L1, CTLA-4, and HAVCR2). GDSC database analyses were conducted to investigate the chemotherapy sensitivity among different groups. GSEA analysis was used to explore the potential mechanisms of UPR-based signature.

RESULTS

A prognostic signature comprising of seven genes (CALR, CRYAB, DNAJB4, KDELR3, CREB3L3, HSPB6, and FBXO6) was constructed to predict the outcome of BLCA. Based on the UPRRGs signature, the patients with BLCA could be classified into low-risk groups and high-risk groups. Patients with BLCA in the low-risk groups showed the more favorable outcomes than those in the high-risk groups, which was verified in GSE13507 set. This signature could serve as an autocephalous prognostic factor in BLCA. A nomogram based on risk score and clinical characteristics was established to predict the over survival of BLCA patients. Furthermore, the signature was closely related to immune checkpoints (PD-L1, CTLA-4, and HAVCR2) and immune cells infiltration including CD8⁺ T cells, follicular helper T cells, activated dendritic cells, and M2 macrophages. GSEA analysis indicated that immune and carcinogenic pathways were enriched in high-risk group.

CONCLUSIONS

We identified a novel unfolded protein response-related gene signature which could predict the over survival, immune microenvironment, and chemotherapy response of patients with bladder cancer.

Checkpoint Inhibition in Bladder Cancer: Clinical Expectations, Current Evidence, and Proposal of Future Strategies Based on a Tumor-Specific Immunobiological Approach

Simple Summary

In contrast with other strategies, immunotherapy is a treatment aimed at empowering the patient’s immune system in order to increase immunity and the response against cancer. Recently, a new class of drugs, immune checkpoint inhibitors, has shown potential in increasing treatment chances for patients with bladder cancers, improving their survival. However, predicting the response to immune checkpoint inhibition is important, since only a group of patients develop a good response. Biomarkers to predict the response to checkpoint inhibition must identify tumors’ and patients’ specific profiles. This study reviews the current knowledge on this most relevant clinical topic, focusing on bladder cancer, going from basic science to ongoing clinical trials and available clinical evidence. Finally, a critical analysis of published data is provided, and an original panel of biomarkers, able to select the right patients for treatments, based on patient-specific immune profiling, is proposed.

Abstract

In contrast with other strategies, immunotherapy is the only treatment aimed at empowering the immune system to increase the response against tumor growth. Immunotherapy has a role in the treatment of bladder cancer (BC) due to these tumors’ high tumor mutational burden (TMB) and mostly prominent immune infiltrate. The therapy or combination has to be adjusted to the tumor’s immunobiology. Recently, a new class of immunotherapeutic agents, immune checkpoint inhibitors (ICI), has shown potential in increasing treatment chances for patients with genitourinary cancers, improving their oncological outcomes. The clinical efficacy of ICI has been shown in both the first-line treatment of cisplatin-ineligible patients, with programmed death ligand 1 (PD-L1)-positive tumors (atezolizumab, pembrolizumab), and in second-line settings, for progression after platinum-based chemotherapy (atezolizumab, pembrolizumab, and nivolumab for FDA and EMA; durvalumab and avelumab for FDA alone). Predicting the response to ICI is important since only a subset of patients undergoing ICI therapy develop a concrete and lasting response. Most of the patients require a different therapy or therapy combination to achieve tumor control. The cancer immunity cycle provides a conceptual framework to assist therapy selection. Biomarkers to predict response to ICI must identify where the cancer immunity cycle is disrupted. We reviewed the current knowledge on ICI treatment in BC, going from basic science to current data and available clinical evidence. Secondly, a critical analysis of published data is provided, and an original panel of biomarkers able to predict response to ICI treatment, based on tumor-specific immune profiling, is proposed.

Immune-Related Genes to Construct a Novel Prognostic Model of Breast Cancer: A Chemosensitivity-Based Study

Chemotherapy combined with surgery is effective for patients with breast cancer (BC). However, chemoresistance restricts the effectiveness of BC treatment. Immune microenvironmental changes are of pivotal importance for chemotherapy responses. Thus, we sought to construct and validate an immune prognostic model based on chemosensitivity status in BC. Here, immune-related and chemosensitivity-related genes were obtained from GSE25055. Then, univariate analysis was employed to identify prognostic-related gene pairs from the intersection of the two parts of the genes, and modified least absolute shrinkage and selection operator (LASSO) analysis was performed to build a prognostic model. Furthermore, we investigated the efficiency of this model from various perspectives, and further validation was performed using the Cancer Genome Atlas (TCGA) cohorts. We identified seven immune and chemosensitivity-related gene pairs and incorporated them into the Cox regression model. After multilevel validation, the risk model was found to be closely related to the survival rate, various clinical characteristics, tumor mutation burden (TMB) score, immune checkpoints, and response to chemotherapeutic drugs. In addition, the model was verified to exhibit predictive capacity as an independent factor over other candidate clinical features. Notably, the constructed nomogram was more accurate than any single factor. Altogether, the risk score model and the nomogram have potential predictive value and may have important practical implications.

Sinomenine Inhibits the Progression of Bladder Cancer Cells by Downregulating LncRNA-HEIH Expression

Background

Sinomenine has been reported to effectively repress the progression of lung cancer and breast cancer. However, the effects of sinomenine in bladder cancer are not well understood. The purpose of this study was to evaluate the effects of sinomenine in bladder cancer.

Methods

The mRNA expression of HEIH in bladder cancer cells was measured by RT-qPCR. T24 and SW780 cells were treated with sinomenine for 24 hours. Cell viability was detected by the MTT assay. Cell migration and invasion were detected by the transwell assay. Western blotting assay was performed to assess the protein expression of Bcl-2, Bax, and caspase-3.

Results

Sinomenine significantly suppressed cell viability in T24 and SW780 cells. Moreover, cell migration and invasion were significantly inhibited by sinomenine. Sinomenine accelerated the expression of Bax and caspase-3 but decreased the expression of Bcl-2. HEIH was upregulated in bladder cancer cells compared with normal bladder epithelial cells. Besides this, we noticed that HEIH knockdown blocked cell proliferation, migration, and invasion but facilitated cell apoptosis in bladder cancer cells. Additionally, HEIH reversed the suppression of the progression induced by sinomenine.

Conclusion

Sinomenine was observed to suppress cell progression of bladder cancer cells by inhibiting HEIH expression. Our findings suggested that the use of sinomenine might be an effective treatment for bladder cancer.

Identification of Prognostic Model Based on Immune-Related LncRNAs in Stage I-III Non-Small Cell Lung Cancer

Background

Long non-coding RNAs (lncRNAs) participate in the regulation of immune response and carcinogenesis, shaping tumor immune microenvironment, which could be utilized in the construction of prognostic signatures for non-small cell lung cancer (NSCLC) as supplements.

Methods

Data of patients with stage I-III NSCLC was downloaded from online databases. The least absolute shrinkage and selection operator was used to construct a lncRNA-based prognostic model. Differences in tumor immune microenvironments and pathways were explored for high-risk and low-risk groups, stratified by the model. We explored the potential association between the model and immunotherapy by the tumor immune dysfunction and exclusion algorithm.

Results

Our study extracted 15 immune-related lncRNAs to construct a prognostic model. Survival analysis suggested better survival probability in low-risk group in training and validation cohorts. The combination of tumor, node, and metastasis staging systems with immune-related lncRNA signatures presented higher prognostic efficacy than tumor, node, and metastasis staging systems. Single sample gene set enrichment analysis showed higher infiltration abundance in the low-risk group, including B cells (p<0.001), activated CD8+ T cells (p<0.01), CD4+ T cells (p<0.001), activated dendritic cells (p<0.01), and CD56+ Natural killer cells (p<0.01). Low-risk patients had significantly higher immune scores and estimated scores from the ESTIMATE algorithm. The predicted proportion of responders to immunotherapy was higher in the low-risk group. Critical pathways in the model were enriched in immune response and cytoskeleton.

Conclusions

Our immune-related lncRNA model could describe the immune contexture of tumor microenvironments and facilitate clinical therapeutic strategies by improving the prognostic efficacy of traditional tumor staging systems.

Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer

We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored.

Dysregulation of tumor microenvironment promotes malignant progression and predicts risk of metastasis in bladder cancer

Background

The tumor microenvironment (TME) is not only a key factor in the malignant progression of cancer but also plays an indispensable role in tumor immunotherapy. As an important regulatory factor in the TME, long non-coding RNAs (incRNA) are important for the development of bladder cancer. The purpose of this study was to explore the molecular mechanism of malignant progression of bladder cancer (BCa) from the perspective of immunology, establish a reliable signature, and evaluate its effect on prognosis, metastasis, and the effectiveness of immunotherapy.

Methods

The TME was assessed by single-sample gene set enrichment analysis (ssGSEA) in 373 patients with muscle invasive bladder cancer (MIBC) in The Cancer Genome Atlas (TCGA). Combining RNA sequence data from 49 BCa patients in our center, we established TME-related prognostic signatures (TMERPS) based on TME-related immune prognosis genes using weighted gene correlation network analysis, selection operator Cox analysis, minimum absolute shrinkage, and survival analysis. Real-Time Quantitative PCR was used for expression level analysis of related genes. Functional enrichment analysis and nomograms were used to explore the potential impact of TMERPS on the immune system, prognosis, and metastasis.

Results

The ssGSEA proved to be an accurate assessment of immune levels in BCa samples. TMERPS was established based on six TME-associated prognostic lncRNAs and was shown to be closely associated with prognosis, metastasis, and immune levels, and to have a significant stratifying effect on the therapeutic efficacy of immune checkpoint inhibitors. Finally, three TMERPS-based nomograms were shown to be effective in predicting prognosis, lymph node metastasis, and distant metastasis in BCa patients.

Conclusions

TMERPS can stratify BCa patients into different risk groups with different prognoses, immunotherapy sensitivity, and risk of metastasis. TMERPS-based nomograms can effectively predict prognosis and metastasis in BCa patients.

Hypoxia-related prognostic model in bladder urothelial reflects immune cell infiltration

Hypoxia is a common feature of tumor microenvironment (TME). This study aims to establish the genetic features related to hypoxia in Bladder urothelial carcinoma (BLCA) and investigate the potential correlation with hypoxia in the TME and immune cells. We established a BLCA outcome model using the hypoxia-related genes from The Cancer Genome Atlas using regression analysis and verified the model using the Gene Expression Omnibus GSE32894 cohort. We measured the effect of each gene in the hypoxia-related risk model using the Human Protein Atlas website. The predictive abilities were compared using the area under the receiver operating characteristic curves. Gene Set Enrichment Analysis was utilized for indicating enrichment pathways. We analyzed immune cell infiltration between risk groups using the CIBERSORT method. The indicators related to immune status between the two groups were also analyzed. The findings indicated that the high-risk group had better outcomes than the low-risk group in the training and validation sets. Each gene in the model affected the survival of BLCA patients. Our hypoxia-related risk model had better performance compared to other hypoxia-related markers (HIF-1α and GLUT-1). The high-risk group was enriched in immune-related pathways. The expression of chemokines and immune cell markers differed significantly between risk groups. Immune checkpoints were more highly expressed in the high-risk group. These findings suggest that the hypoxia-related risk model predicts patients' outcomes and immune status in BLCA risk groups. Our findings may contribute to the treatment of BLCA.

Tumor-Associated Macrophages in Bladder Cancer: Biological Role, Impact on Therapeutic Response and Perspectives for Immunotherapy

Tumor-associated macrophages (TAMs) are one of the most abundant infiltrating immune cells of solid tumors. Despite their possible dual role, i.e., pro- or anti-tumoral, there is considerable evidence showing that the accumulation of TAMs promotes tumor progression rather than slowing it. Several strategies are being developed and clinically tested to target these cells. Bladder cancer (BCa) is one of the most common cancers, and despite heavy treatments, including immune checkpoint inhibitors (ICIs), the overall patient survival for advanced BCa is still poor. TAMs are present in bladder tumors and play a significant role in BCa development. However, few investigations have analyzed the effect of targeting TAMs in BCa. In this review, we focus on the importance of TAMs in a cancerous bladder, their association with patient outcome and treatment efficiency as well as on how current BCa treatments impact these cells. We also report different strategies used in other cancer types to develop new immunotherapeutic strategies with the aim of improving BCa management through TAMs targeting.

TACR2 is associated with the immune microenvironment and inhibits migration and proliferation via the Wnt/β-catenin signaling pathway in prostate cancer

Background

The tachykinin receptor 2 (TACR2) is encoded by the tachykinin receptor correlation gene. Recent microarray analysis for prostate cancer suggests that TACR2 expression is associated with clinical phenotype and disease-free survival among patients with prostate cancer.

Results

TACR2 protein levels were lower in prostate cancer tissues than in adjacent normal prostate tissue. TACR2 expression significantly correlated with clinical stage, Gleason scores, and survival outcomes. TACR2 expression positively correlated with mast cells and negatively correlated with M2 macrophages. Overexpression of TACR2 promoted the migration and proliferation of prostate cancer cells by regulating the Wnt signaling pathway.

Conclusions

The TACR2-Wnt/β-catenin signaling pathway is critical in prostate cancer. TACR2 may affect tumor cells’ occurrence and development by changing the content of immune cells in the tumor microenvironment. These findings suggest that TACR2 may be a candidate molecular biomarker for prostate cancer therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02126-0.

Alternatively spliced ANLN isoforms synergistically contribute to the progression of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a common cancer with high mortality. Anilin actin-binding protein (ANLN) has been reported to be associated with carcinogenesis in multiple tumors. However, the expression pattern and functional effects of ANLN in HNSCC remain to be unclear. Clinical data and online databases were used to analyze the expression of ANLN and its relationship with HNSCC patient survival. Expression of two major splice variants of ANLN was assessed in HNSCC tissues and cell lines. The functional effects and related mechanisms of ANLN isoforms were investigated in HNSCC in vitro and in vivo. Our study showed that patients with high expression of ANLN had a poor prognosis. The two primary isoforms of ANLN transcripts ANLN-201 and ANLN-210 were highly expressed in HNSCC tissues and cell lines. Knockout of ANLN restrained cell proliferation, migration, and invasion of SCC-9 cells. Mechanically, ANLN-201 could interact with c-Myc to keep its protein stability, thereby playing a oncogenic role in HNSCC. ANLN-210 could be transferred to macrophages via exosomes by binding to RNA-binding protein hnRNPC. Exosomal ANLN-210 promoted macrophage polarization via PTEN/PI3K/Akt signaling pathway, thus stimulating tumor growth of HNSCC. ANLN was an independent prognostic factor in patients with HNSCC. Alternatively spliced ANLN isoforms collaboratively promote HNSCC tumorigenesis in vitro and in vivo, which might provide the in-depth role and mechanism of ANLN in HNSCC development.