A robust 11-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts

Overexpression of Basonuclin Zinc Finger Protein 2 in stromal cell is related to mesenchymal phenotype and immunosuppression of mucinous colorectal adenocarcinoma

BACKGROUND

Mucinous carcinoma (MC) is a distinct histologic subtype of colorectal cancer (CRC) that is less studied and associated with poor prognosis. This study aimed to identify MC-specific therapeutic targets and biomarkers to improve the prognosis of this aggressive disease.

METHODS

CRC samples from The Cancer Genome Atlas (TCGA) were categorized into MC and non-MC (NMC) groups based on histologic type. A multi-scale embedded gene co-expression network analysis (MEGENA) was constructed to identify gene modules associated with the MC group. The potential functions of Basonuclin Zinc Finger Protein 2 (BNC2) were further analyzed using the Biomarker Exploration for Solid Tumors (BEST) database. In vivo and in vitro experiments were conducted to validate the predicted results.

RESULTS

We identified the stromal component-related gene, BNC2, in the MC population. This gene is associated with a shorter progression-free interval (PFI) in CRC patients. BNC2 promotes FAP (encoding Fibroblast Activation Protein Alpha) transcription in cancer-associated fibroblasts (CAFs) and is involved in angiogenesis through two pathways. Additionally, BNC2 enhances tumor cell invasiveness in a CAF-dependent manner. Patients with high BNC2 expression benefited less from immunotherapy compared to those with low BNC2 expression.

CONCLUSIONS

Our study highlights the clinical importance of BNC2 in MC, and targeting BNC2 on stromal cells (fibroblasts and endothelial cells) may be an effective strategy for treating MC.

Exploring tumor microenvironment in molecular subtyping and prognostic signatures in ovarian cancer and identification of SH2D1A as a key regulator of ovarian cancer carcinogenesis

Introduction

A deadly gynecological cancer, ovarian cancer (OV), has a poor prognosis because of late-stage diagnosis and few targeted therapies. Addressing the tumor microenvironment (TME) in solid tumors has shown promise since it is crucial in promoting cancer progression.

Methods

We obtained bulk RNA-seq data from TCGA-OV, GSE26712, GSE102073, and ICGC cohorts, as well as scRNA-seq data from EMTAB8107, GSE118828, GSE130000, and GSE154600 cohorts using the TISCH2 database. The ConsensusClusterPlus package was used to cluster the OV tumor tissues hierarchically to determine two molecularly different groups (C1 and C2). A total of ten different types of machine learning techniques with 101 combinations were used for prognostic model construction. Using eight TME algorithms integrated into the IOBR R package, the bulk RNA-seq dataset was analyzed. For in vitro experiments, OVCAR3 and SKOV3, two OV cell lines, were used. The migratory potential of the ovarian cancer cells was assessed using Transwell assay, while proliferation was assessed using CCK8 assay.

Results

Based on TME-related gene set expression, two distinct molecular subgroups (C1 and C2) were identified through consensus clustering, with C1 showing higher TME activity. Further analysis indicated that C1 had increased cancer-associated fibroblasts (CAFs), M1 macrophages, and CD8+ T cells, suggesting a more activated and pro-inflammatory TME. Drug sensitivity analysis revealed that 5-Fluorouracil might be beneficial to C1 patients. Functional differences between C1 and C2 were identified, including cell adhesion, mononuclear cell differentiation, and leukocyte migration. A machine learning model was developed to create a TME-related prognostic signature, demonstrating strong prognostic capabilities across multiple datasets. High-risk patients showed a more immune-suppressive TME and higher tumor stemness. ScRNA-seq disclosed a highly activated TME-related signature in OV. Cancer cell lines had significantly higher SH2D1A mRNA expression than normal ovarian epithelial cells. We observed that SH2D1A knockdown in 2 ovarian cancer cell lines (OVCAR3 and SKOV3) reduced migration and proliferation through a series of in-vitro experiments.

Conclusion

TME-associated genes were efficient in ovarian cancer molecular subtyping. A TME-based prognosis model was constructed for vigorous prognostic stratification efficacy across multiple datasets. Moreover, we identified a pivotal role of SH2D1A in promoting proliferation and migration in ovarian cancer.

Understanding the heterogeneity in liver hepatocellular carcinoma with a special focus on malignant cell through single-cell analysis

INTRODUCTION

Hepatocellular carcinoma (HCC) is the most common form of liver cancer globally and remains a major cause of cancer-related deaths. HCC exhibits significant intra-tumoral and interpatient heterogeneity, impacting treatment efficacy and patient prognosis.

METHODS

We acquired transcriptome data from the TCGA and ICGC databases, as well as liver cancer chip data from the GEO database, and processed the data for subsequent analysis. We also obtained single cell data from the GEO database and performed data analysis using the Seurat package. To further investigate epithelial cell subgroups and their copy number variations, we used the Seurat workflow for subgroup classification and the InferCNV software for CNV analysis, utilizing endothelial cells as a reference. Pseudo-time analysis and transcription factor analysis of epithelial cells were performed using the monocle2 and SCENIC software, respectively. To assess intercellular communication, we employed the CellChat package to identify potential ligand-receptor interactions. We also analyzed gene expression differences and conducted enrichment analysis using the limma and clusterProfiler packages. Additionally, we established tumor-related risk characteristics using Cox analysis and Lasso regression, and predicted immunotherapy response using various datasets.

RESULTS

The samples were classified into 23 clusters, with malignant epithelial cells being the majority. Trajectory analysis revealed the differentiation states of the malignant epithelial cells, with cluster 1 being in the terminal state. Functional analysis revealed higher aggressiveness and epithelial-mesenchymal transition (EMT) scores in cluster 1, indicating a higher propensity for metastasis. RBP4+ tumor cells were highly enriched with hypoxia process and intensive cell-to-cell communication. A prognostic model was established, and immune infiltration analysis showed increased infiltration in the high-risk group. TP53 demonstrated significant differences in mutation rate between the two risk groups. Validation analysis confirmed the up-regulation of model genes, including AKR1B10, ARL6IP4, ATP6V0B, and BSG in tumor tissues.

CONCLUSION

A prognostic model was established based on HCC malignant cell associated gene signature, displaying decent prognosis guiding effectiveness in the multiple cohorts. The study provided comprehensive insights into the heterogeneity and potential therapeutic targets of LIHC.

Identification of bladder cancer subtypes and predictive signature for prognosis, immune features, and immunotherapy based on immune checkpoint genes

Immunotherapy based on immune checkpoint genes (ICGs) has recently made significant progress in the treatment of bladder cancer patients, but many patients still cannot benefit from it. In the present study, we aimed to perform a comprehensive analysis of ICGs in bladder cancer tissues with the aim of evaluating patient responsiveness to immunotherapy and prognosis. We scored ICGs in each BLCA patient from TCGA and GEO databases by using ssGSEA and selected genes that were significantly associated with ICGs scores by using the WCGNA algorithm. NMF clustering analysis was performed to identify different bladder cancer molecular subtypes based on the expression of ICGs-related genes. Based on the immune related genes differentially expressed among subgroups, we further constructed a novel stratified model containing nine genes by uni-COX regression, LASSO regression, SVM algorithm and multi-COX regression. The model and the nomogram constructed based on the model can accurately predict the prognosis of bladder cancer patients. Besides, the patients classified based on this model have large differences in sensitivity to immunotherapy and chemotherapy, which can provide a reference for individualized treatment of bladder cancer.

Evolutionary screening of precision oncology biomarkers and its applications in prognostic model construction

Biomarker screening is critical for precision oncology. However, one of the main challenges in precision oncology is that the screened biomarkers often fail to achieve the expected clinical effects and are rarely approved by regulatory authorities. Considering the close association between cancer pathogenesis and the evolutionary events of organisms, we first explored the evolutionary feature underlying clinically approved biomarkers, and two evolutionary features of approved biomarkers (Ohnologs and specific evolutionary stages of genes) were identified. Subsequently, we utilized evolutionary features for screening potential prognostic biomarkers in four common cancers: head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Finally, we constructed an evolution-strengthened prognostic model (ESPM) for cancers. These models can predict cancer patients' survival time across different cancer cohorts effectively and perform better than conventional models. In summary, our study highlights the application potentials of evolutionary information in precision oncology biomarker screening.

A retrospective analysis of the clinicopathological features and prognostic value of MAPK12 protein expression in diffuse large B-cell lymphoma

PURPOSE

Mitogen-activated protein kinase 12 (MAPK12), also known as p38γ, is a member of the p38 MAPK family and plays a crucial role in tumor occurrence and invasion. However, there is still uncertainty regarding MAPK12 involvement in diffuse large B-cell lymphoma (DLBCL).

METHODS

Our study investigated the expression of MAPK12 mRNA in various types of cancer using bioinformatic analysis. Furthermore, we performed immunohistochemistry (IHC) to detect the expression of MAPK12 in patients with DLBCL and compared clinical indicators and survival rates.

RESULTS

We found that the high expression rate of MAPK12 was 43.1% in DLBCL patients. Several clinical indicators, including IPI scores, Hans classifications, LDH levels, and Ki-67 expression were closely associated with MAPK12 expression. Survival analysis revealed that higher expression of MAPK12 was significantly correlated with shorter progression-free survival (PFS) and overall survival (OS) in DLBCL patients. In addition, both univariate and multivariate analyses revealed IPI score, MAPK12 expression, and rituximab use as the independent OS risk factors (P < 0.05). To explore the functional role of MAPK12 in DLBCL, weighted gene co-expression network analysis (WGCNA) and gene ontology (GO) were used to confirm the involvement of MAPK12 in the regulation of type II interferon production, positive regulation of lymphocyte proliferation, and other related biological processes.

CONCLUSION

DLBCL patients have poor prognoses when MAPK12 levels are high, which is expected to be a therapeutic target and prognostic factor.

Identification and Validation of a Cancer-Testis Antigen-Related Signature to Predict the Prognosis in Stomach Adenocarcinoma

Background: Stomach adenocarcinoma (STAD) is the fifth most common cancer and the third leading cause of cancer-related deaths worldwide. Cancer-testis antigens (CTAs) participate in the pathogenesis and development of multiple cancers and are aberrantly overexpressed in various types of cancer. This study aimed to develop a CTA-related gene signature (CTARSig) to predict prognosis in STAD patients and explore its underlying mechanisms. Methods: We performed differential and prognostic analyses of CTA-related genes and constructed a CTA-related signature (CTARSig) along with a novel nomogram to predict the prognosis of patients with STAD based on the Cox and The Least Absolute Shrinkage and Selection Operator. CTARSig was further validated in an external cohort (GSE84437). Additionally, univariate and multivariate Cox regression, as well as receiver operating characteristic (ROC) analyses, were performed to assess the CTARSig systematically. Single-sample gene set enrichment analysis and ESTIMATE were used to characterise the Tumor Immune Microenvironment (TIME) in patients with STAD. Furthermore, Gene Set Variation Analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology analyses revealed the biological functions and signalling pathways associated with CTARSig. Finally, the human gastric cancer cell lines, HCG-27 and AGS, were used for in vitro and in vivo experiments, respectively, to further validate the role of ELOVL4. Results: Eleven CTA-related genes were identified to construct the CTARSig. Kaplan-Meier curves, independent prognostic analysis, and ROC curves revealed that CTARSig could better predict survival in patients with STAD. Moreover, in our study, we demonstrated that ELOVL4 is upregulated in gastric cancer tissues and that its high expression is associated with poor survival. Additionally, in vitro and in vivo experiments demonstrated that ELOVL4 promotes the metastatic and invasive potential of STAD cells, suggesting it may be a potential therapeutic target for STAD. Conclusion: In this study, a novel signature associated with CTAs was constructed for STAD, which may be a good predictor of patient prognosis. Thus, ELOVL4 may be a potential therapeutic target for gastric cancer. This study provides new insights into the potential roles of CTAs in gastric cancer.

A paclitaxel-hyaluronan conjugate (ONCOFID-P-B™) in patients with BCG-unresponsive carcinoma in situ of the bladder: a dynamic assessment of the tumor microenvironment

BACKGROUND

The intravesical instillation of the paclitaxel-hyaluronan conjugate ONCOFID-P-B™ in patients with bacillus Calmette-Guérin (BCG)-unresponsive bladder carcinoma in situ (CIS; NCT04798703 phase I study), induced 75 and 40% of complete response (CR) after 12 weeks of intensive phase and 12 months of maintenance phase, respectively. The aim of this study was to provide a detailed description of the tumor microenvironment (TME) of ONCOFID-P-B™-treated BCG-unresponsive bladder CIS patients enrolled in the NCT04798703 phase I study, in order to identify predictive biomarkers of response.

METHODS

The composition and spatial interactions of tumor-infiltrating immune cells and the expression of the most relevant hyaluronic acid (HA) receptors on cancer cells, were analyzed in biopsies from the 20 patients enrolled in the NCT04798703 phase I study collected before starting ONCOFID-P-B™ therapy (baseline), and after the intensive and the maintenance phases. Clinical data were correlated with cell densities, cell distribution and cell interactions. Associations between immune populations or HA receptors expression and outcome were analyzed using univariate Cox regression and log-rank analysis.

RESULTS

In baseline biopsies, patients achieving CR after the intensive phase had a lower density of intra-tumoral CD8+ cytotoxic T lymphocytes (CTL), but also fewer interactions between CTL and macrophages or T-regulatory cells, as compared to non-responders (NR). NR expressed higher levels of the HA receptors CD44v6, ICAM-1 and RHAMM. The intra-tumoral macrophage density was positively correlated with the expression of the pro-metastatic and aggressive variant CD44v6, and the combined score of intra-tumoral macrophage density and CD44v6 expression had an AUC of 0.85 (95% CI 0.68-1.00) for patient response prediction.

CONCLUSIONS

The clinical response to ONCOFID-P-B™ in bladder CIS likely relies on several components of the TME, and the combined evaluation of intra-tumoral macrophages density and CD44v6 expression is a potentially new predictive biomarker for patient response. Overall, our data allow to advance a potential rationale for combinatorial treatments targeting the immune infiltrate such as immune checkpoint inhibitors, to make bladder CIS more responsive to ONCOFID-P-B™ treatment.

A prognostic signature of fatty acid metabolism-related genes for predicting survival of gastric cancer patients

To analyze the expression profile of fatty acid metabolism (FAM)-related genes, identify a prognostic signature, and evaluate its clinical value for gastric cancer (GC) patients. The mRNA expression profiles of 493 FAM-related genes were obtained from TCGA database. Differentially expressed genes (DEGs) between cancer and non-cancer samples were identified, and their relationships with overall survival (OS) of GC patients were evaluated. A prognostic signature of FAM-related genes was identified by the LASSO regression model, and its predictive performance was tested by an independent external cohort. Ninety-three DEGs were identified, of which 44 were downregulated and 49 were upregulated. After optimizing risk characteristics, a prognostic signature of four FAM-related genes (ACBD5, AVPR1A, ELOVL4, and FAAH) were developed. All patients were divided into high-risk (>1.020) and low-risk groups (≤1.020) on the basis of the median risk score. Survival analysis indicated that high-risk patients had a shorter OS than low-risk patients (5-year OS rate, 26.3% vs. 45.0%, p < 0.001). The AUC values for the prediction of 3-year and 5-year OS were 0.664 and 0.624, respectively. In the GSE62254 data set, the 5-year OS rate of high-risk and low-risk patients were 44.7% versus 61.5%, respectively (p = 0.003). The AUC values were 0.632 and 0.627 at 3-year and 5-year prediction. The prognostic signature of FAM-related genes was an independent predictor of OS (hanzard ratio [HR] for TCGA cohort: 1.851, 95% confidence interval [CI]: 1.394-2.458, p < 0.001; HR for GSE62254: 1.549, 95% CI: 1.098-2.185, p = 0.013). The risk signature of four FAM-related genes was a valuable prognostic tool, and it might be helpful for clinical management and therapeutic decision of gastric cancer patients.

Sexual dimorphism in bladder cancer: a review of etiology, biology, diagnosis, and outcomes

Bladder carcinoma represents a prevalent malignancy, wherein the influence of sex extends across its incidence, biological attributes, and clinical outcomes. This scholarly exposition meticulously examines pertinent investigations, elucidating the nuanced impact of sex on bladder cancer, and posits cogent avenues for future research and intervention modalities. In the initial discourse, an exhaustive scrutiny is undertaken of the etiological underpinnings of bladder cancer, encompassing variables such as tobacco consumption, occupational exposures, and genetic aberrations. Subsequently, a comprehensive dissection unfolds, delving into the intricate biological disparities inherent in sex vis-à-vis the initiation and progression of bladder cancer. This analytical framework embraces multifaceted considerations, spanning sex hormones, sex chromosomal dynamics, metabolic enzymatic cascades, and the intricate interplay with the microbiome. Lastly, a synthesized exposition encapsulates the ramifications of gender differentials on the diagnostic and prognostic landscapes of bladder cancer, underscoring the imperative for intensified investigative endeavors directed towards elucidating gender-specific variances and the formulation of tailored therapeutic strategies.

PRR11 is a prognostic biomarker and correlates with immune infiltrates in bladder urothelial carcinoma

Abnormal proline-rich protein 11 (PRR11) expression is associated with various tumors. However, there are few reports concerning PRR11 with prognostic risk, immune infiltration, or immunotherapy of bladder urothelial carcinoma (BLCA). This study is based on online databases, such as Oncomine, GEPIA, HPA, LinkedOmics, TIMER, ESTIMATE and TISIDB, and BLCA data downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus, we employed an array of bioinformatics methods to explore the potential oncogenic roles of PRR11, including analyzing the relationship between PRR11 and prognosis, tumor mutational burden (TMB), microsatellite instability, and immune cell infiltration in BLCA. The results depict that PRR11 is highly expressed in BLCA, and BLCA patients with higher PRR11 expression have worse outcomes. In addition, there was a significant correlation between PRR11 expression and TMB and tumor immune infiltration. These findings suggest that PRR11 can be used as a potential marker for BLCA patient assessment and risk stratification to improve clinical prognosis, and its potential regulatory mechanism in the BLCA tumor microenvironment and targeted therapy is worthy of further investigation.

PRR11 in Malignancies: Biological Activities and Targeted Therapies

Proline rich 11 (PRR11), initially renowned for its relevance with cell-cycle progression, is a proline-rich protein coding gene in chromosome 17q22-23. Currently, accumulating studies have demonstrated that PRR11 plays a critical role in cellular proliferation, colony formation, migration, invasion, cell-cycle progression, apoptosis, autophagy and chemotherapy resistance via multiple signaling pathways and biological molecules in several solid tumors. In particular, PRR11 also serves as a promising prognostic indicator in a limited number of human cancers, gradually manifesting its potential application for targeted therapies. In this review, we summarize functional activities, related signaling pathways and biological molecules of PRR11 in various malignancies and generalize potential application of PRR11 for targeted therapies, thereby contributing to further exploration of PRR11 in cancer treatment.

A novel focal adhesion-related risk model predicts prognosis of bladder cancer —— a bioinformatic study based on TCGA and GEO database

Background

Bladder cancer (BLCA) is the ninth most common cancer globally, as well as the fourth most common cancer in men, with an incidence of 7%. However, few effective prognostic biomarkers or models of BLCA are available at present.

Methods

The prognostic genes of BLCA were screened from one cohort of The Cancer Genome Atlas (TCGA) database through univariate Cox regression analysis and functionally annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The intersecting genes of the BLCA gene set and focal adhesion-related gene were obtained and subjected to the least absolute shrinkage and selection operator regression (LASSO) to construct a prognostic model. Gene set enrichment analysis (GSEA) of high- and low-risk patients was performed to explore further the biological process related to focal adhesion genes. Univariate and multivariate Cox analysis, receiver operating characteristic (ROC) curve analysis, and Kaplan–Meier survival analysis (KM) were used to evaluate the prognostic model. DNA methylation analysis was presented to explore the relationship between prognosis and gene methylation. Furthermore, immune cell infiltration was assessed by CIBERSORT, ESTIMATE, and TIMER. The model was verified in an external GSE32894 cohort of the Gene Expression Omnibus (GEO) database, and the Prognoscan database presented further validation of genes. The HPA database validated the related protein level, and functional experiments verified significant risk factors in the model.

Results

VCL, COL6A1, RAC3, PDGFD, JUN, LAMA2, and ITGB6 were used to construct a prognostic model in the TCGA-BLCA cohort and validated in the GSE32894 cohort. The 7-gene model successfully stratified the patients into both cohorts’ high- and low-risk groups. The higher risk score was associated with a worse prognosis.

Conclusions

The 7-gene prognostic model can classify BLCA patients into high- and low-risk groups based on the risk score and predict the overall survival, which may aid clinical decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10264-5.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

Identification of a metabolism-related gene signature predicting overall survival for bladder cancer

Reprogramming of metabolism is becoming a novel hallmark of cancer. This study aims to perform bioinformatics analysis of metabolism-related genes in bladder cancer, and to construct a signature of metabolism-related genes for predicting the prognosis. A total of 373 differentially expressed metabolism-related genes were identified from TCGA database. Taking survival time and clinical information into consideration, we constructed a risk score to predict clinical prognosis. Low-risk patients had a better prognosis than high-risk patients. Multivariate analysis showed that risk score was an independent prognostic indicator in bladder cancer. ROC curve also proved that risk score had better ability to predict prognosis than other individual indicators. Nomogram also showed a clinical net benefit to evaluate the prognosis of bladder cancer patients. GSEA revealed several metabolism-related pathways that were differentially enriched in the high-risk and low-risk groups, which might help to explain the underlying mechanisms. This signature was confirmed to be an effective prognostic biomarker in bladder cancer.

Chitosan oligosaccharides packaged into rat adipose mesenchymal stem cells-derived extracellular vesicles facilitating cartilage injury repair and alleviating osteoarthritis

Objectives

This study aimed to investigate the roles of adipose mesenchymal stem cell (AMSC)-derived extracellular vesicles (EVs) binding with chitosan oligosaccharides (COS) in cartilage injury, as well as the related mechanisms.

Results

IL-1β treatment significantly inhibited the viability and migration of chondrocytes and enhanced cell apoptosis (P < 0.05), while chitosan oligosaccharides and extracellular vesicles-chitosan oligosaccharide conjugates (EVs-COS/EVs-COS conjugates) reversed the changes induced by IL-1β (P < 0.05), and the effects of extracellular vesicles-chitosan oligosaccharide conjugates were better than those of chitosan oligosaccharides (P < 0.05). After cartilage damage, IL-1β, OPN, and p53 were significantly upregulated, COL1A1, COL2A1, OCN, RUNX2, p-Akt/Akt, PI3K, c-Myc, and Bcl2 were markedly downregulated, and extracellular vesicles-chitosan oligosaccharide conjugates reversed the expression induced by cartilage injury. Through sequencing, 760 differentially expressed genes (DEGs) clustered into four expression patterns were associated with negative regulation of the canonical Wnt, PI3K-Akt, AMPK, and MAPK signaling pathways.

Conclusion

Extracellular vesicles-chitosan oligosaccharide conjugates may serve as a new cell-free biomaterial to facilitate cartilage injury repair and improve osteoarthritis.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12951-021-01086-x.

The Role of p38γ in Cancer: From review to outlook

p38γ is a member of the p38 Mitogen Activated Protein Kinases (p38 MAPKs). It contains four subtypes in mammalian cells encoded by different genes including p38α (MAPK14), p38β (MAPK11), p38γ (MAPK12), and p38δ (MAPK13). Recent studies revealed that p38γ may exhibit a crucial role in tumorigenesis and cancer aggressiveness. Despite the large number of published literatures, further researches are demanded to clarify its role in cancer development, the tissue-specific function and associated novel treatment strategies. In this article, we provide the latest view on the connection between p38γ and malignant tumors, highlighting the function of p38γ. The clinical value of p38γ is also discussed, helping the translation into the remarkable therapeutic strategy in tumor diseases.

SERPINE2 Overexpression Is Associated with Poor Prognosis of Urothelial Carcinoma

Recent studies have reported that SERPINE2 contributes to the development of various cancers. However, its association with urothelial carcinoma (UC) remains unclear. In this study, data on urinary bladder UC (UBUC) cases from The Cancer Genome Atlas (TCGA) database were used to investigate the prognostic value of SERPINE2 mRNA expression. Then, SERPINE2 expression was analyzed with tissue microarrays constructed from 117 upper tract UC (UTUC) and 84 UBUC tissue specimens using immunohistochemical staining. Results were compared to clinicopathologic data by multivariate analysis. In the TCGA database, high SERPINE2 mRNA expression indicated a poor prognosis in patients with UBUC. Furthermore, Mann-Whitney U test showed that high SERPINE2 immunoexpression was significantly associated with adverse pathologic parameters including invasion, high grade, coexistence of UC in situ, and advanced pT stage (all p < 0.05, except for a marginal association with high-grade UBUC, p = 0.066). Kaplan-Meier analysis revealed that high SERPINE2 expression was associated with worse overall survival (OS; UTUC, p = 0.003; UBUC, p = 0.014) and disease-free survival (UTUC, p = 0.031; UBUC, p = 0.033). Moreover, multivariate analysis identified high SERPINE2 expression as an independent prognostic factor for OS (UTUC, p = 0.002; UBUC, p = 0.024). Taken together, our findings demonstrated that increased SERPINE2 expression is associated with adverse pathologic features and may serve as a prognostic biomarker for UC.

Identification of an Immune-Related Risk Signature Correlates With Immunophenotype and Predicts Anti-PD-L1 Efficacy of Urothelial Cancer

Immune checkpoint inhibitor (ICI) treatment has been used to treat advanced urothelial cancer. Molecular markers might improve risk stratification and prediction of ICI benefit for urothelial cancer patients. We analyzed 406 cases of bladder urothelial cancer from The Cancer Genome Atlas (TCGA) data set and identified 161 messenger RNAs (mRNAs) as differentially expressed immunity genes (DEIGs). Using the LASSO Cox regression model, an eight-mRNA-based risk signature was built. We validated the prognostic and predictive accuracy of this immune-related risk signature in 348 metastatic urothelial cancer (mUC) samples treated with anti-PD-L1 (atezolizumab) from IMvigor210. We built an immune-related risk signature based on the eight mRNAs: ANXA1, IL22, IL9R, KLRK1, LRP1, NRG3, SEMA6D, and STAP2. The eight-mRNA-based risk signature successfully categorizes patients into high-risk and low-risk groups. Overall survival was significantly different between these groups, regardless if the initial TCGA training set, the internal TCGA testing set, all TCGA set, or the ICI treatment set. The hazard ratio (HR) of the high-risk group to the low-risk group was 3.65 (p < 0.0001), 2.56 (p < 0.0001), 3.36 (p < 0.0001), and 2.42 (p = 0.0009). The risk signature was an independent prognostic factor for prediction survival. Moreover, the risk signature was related to immunity characteristics. In different tumor mutational burden (TMB) subgroups, it successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome. Our eight-mRNA-based risk signature is a stable biomarker for urothelial cancer and might be able to predict which patients benefit from ICI treatment. It might play a role in precision individualized immunotherapy.