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Abstract Tumor inflammation is one of the hallmarks of tumors and is closely related to tumor occurrence and development, providing individualized prognostic prediction. However, few studies have evaluated the relationship between inflammation and the prognosis of bladder urothelial carcinoma (BLCA) patients. Therefore, we constructed a novel inflammation-related prognostic model that included six inflammation-related genes (IRGs) that can precisely predict the survival outcomes of BLCA patients. RNA-seq expression and corresponding clinical data from BLCA patients were downloaded from The Cancer Genome Atlas database. Enrichment analysis was subsequently performed to determine the enrichment of GO terms and KEGG pathways. K‒M analysis was used to compare overall survival (OS). Cox regression and LASSO regression were used to identify prognostic factors and construct the model. Finally, this prognostic model was used to evaluate cell infiltration in the BLCA tumor microenvironment and analyze the effect of immunotherapy in high- and low-risk patients. We established an IRG signature-based prognostic model with 6 IRGs (TNFRSF12A, NR1H3, ITIH4, IL1R1, ELN and CYP26B1), among which TNFRSF12A, IL1R1, ELN and CYP26B1 were unfavorable prognostic factors and NR1H3 and ITIH4 were protective indicators. High-risk score patients in the prognostic model had significantly poorer OS. Additionally, high-risk score patients were associated with an inhibitory immune tumor microenvironment and poor immunotherapy response. We also found a correlation between IRS-related genes and bladder cancer chemotherapy drugs in the drug sensitivity data. The IRG signature-based prognostic model we constructed can predict the prognosis of BLCA patients, providing additional information for individualized prognostic judgment and treatment selection.

Abstract Background The effect of SPP1 in squamous cell carcinoma of the penis (PSCC) remained unknown. We attempted to clarify the function of the SPP1 gene in PSCC. Method Eight paired penile cancer specimens (including penile cancer tissue, paracancerous tissue, and positive lymph node tissue) subjected to whole transcriptome sequencing were analysed to identify differentially expressed genes. We used immunohistochemistry to detect the expression of SPP1 protein and immune cell related proteins in penile cancer tissue. Then, we performed weighted gene coexpression network analysis (WGCNA) to identify the genes related to SPP1 in penile cancer tissue and positive lymph node tissue. Based on the GSE57955 dataset, the CIBERSORT and ssGSEA algorithms were carried out to investigate the immune environment of PSCC. GSVA analysis was conducted to identify the signaling pathways related to SPP1 subgroups. Enzyme-linked immunosorbent assay (ELISA) method was adopted to detect SPP1 level in the serum of 60 patients with penile cancer. Results Differential analysis indicated that SPP1 was the most differentially upregulated gene in both penile cancer tissues and positive lymph node tissues. Survival analysis suggested that the prognosis of the low-SPP1 group was significantly poorer than that of the high-SPP1 group. Subsequently, immune-related bioinformatics showed that SPP1 was significantly associated with B cells, CD8 + T cells, CD4 + T cells, macrophages, helper T cells, neutrophils and dendritic cells. The immunohistochemical results showed that the high-SPP1 group was characterized by relatively high expression of CD16 and relatively low expression of CD4. GSVA analysis indicated that high-SPP1 group was significantly associated with immune-related pathways such as PD-L1 expression and the PD-1 checkpoint pathway in cancer and the TNF signaling pathway. ELISA demonstrated that the serum level of SPP1 in patients with positive lymph node metastasis of penile cancer was significantly higher than that in patients with negative lymph node metastasis of penile cancer. Conclusion Our study shows that the SPP1 gene might be an effective biomarker for predicting the prognosis and the efficacy of immunotherapy in PSCC patients.

Abstract The presence and extent of regional lymph node and distant metastasis are the most fatal prognostic factors in penile squamous cell carcinoma (PSCC). However, the available biomarkers and detailed mechanisms underlying the metastasis of PSCC remain elusive. Here, we explored the expression landscape of HOX genes in twelve paired PSCC tissues, including primary tumors, metastatic lymph nodes and corresponding normal tissues, and highlighted that HOXD11 was indispensable in the progression of PSCC. HOXD11 was upregulated in PSCC cell lines and tumors, especially in metastatic lymph nodes. High HOXD11 expression was associated with aggressive features, such as advanced pN stages, extranodal extension, pelvic lymph node and distant metastasis, and predicted poor survival. Furthermore, tumorigenesis assays demonstrated that knockdown of HOXD11 not only inhibited the capability of cell proliferation, invasion and tumor growth but also reduced the burden of metastatic lymph nodes. Further mechanistic studies indicated that miR-138-5p was a tumor suppressor in PSCC by inhibiting the translation of HOXD11 post-transcriptionally through binding to the 3′ untranslated region. Furthermore, HOXD11 activated the transcription of FN1 to decompose the extracellular matrix and to promote epithelial mesenchymal transition-like phenotype metastasis via FN1/MMP2/MMP9 pathways. Our study revealed that HOXD11 is a promising prognostic biomarker and predicts advanced disease with poor outcomes, which could serve as a potential therapeutic target for PSCC.

Background: Immune checkpoint inhibitor therapy has changed the treatment model of metastatic bladder cancer. However, only approximately 20% of patients benefit from this therapy, and robust biomarkers to predict the effect of immunotherapy are still lacking. In this study, we aimed to investigate whether immune-related genes could be indicators for the prognosis of bladder cancer patients and the effect of immunotherapy.Methods: Based on bladder cancer dataset from the Cancer Genome Atlas (TCGA) and GSE48075, 22 immune microenvironment-related cells were identified by CIBERSORT. After performing a series of bioinformatic and machine learning approaches, we identified distinct tumor microenvironment clusters and three bladder cancer specific immune-related genes (EGFR, OAS1 and MST1R). Then, we constructed immune-related gene risk score (IRGRS) by using the Cox regression method and validated it with the IMvigor210 dataset.Results: IRGRS-high patients had a worse overall survival than IRGRS-low patients, which was consistent with the result in the IMvigor210 dataset. Comprehensive analysis shows that patients with high IRGRS scores are mainly enriched in basal/squamous type (Ba/Sq), and tumor metabolism-related pathways are more Active, with higher TP53 and RB1 gene mutation rates, lower CD4+/CD8+ T cell infiltration, higher M0 macrophage infiltration, and lower immunotherapy efficacy. In contrast, Patients with low IRGRS scores are mainly enriched in the luminal papillary type (LumP), which is associated with the activation of IL-17 and TNF signaling pathways, higher mutation rates of FGFR3 and CDKN1A genes, higher CD4+/CD8+ T cell infiltration content, and The level of M0 macrophage infiltration was relatively low, and the immunotherapy was more probably effective.Conclusion: Our study constructed an IRGRS for bladder cancer and clarified the immune and molecular characteristics of IRGRS-defined subgroups of bladder cancer to investigate the association between IRGRS and its potential implications for prognosis and immunotherapy.

ObjectivesTo evaluate the relationship between deep inguinal lymph node metastasis (ILNM) and pelvic lymph node metastasis (PLNM) and explore the prognostic value of deep ILNM in penile squamous cell carcinoma (PSCC).Materials and MethodsThe records of 189 patients with ILNM treated for PSCC were analysed retrospectively. Logistic regression models were used to test for predictors of PLNM. Cox regression was performed in univariable and multivariable analyses of cancer-specific survival (CSS). CSS was compared using Kaplan-Meier analyses and log rank tests.ResultsPLNM were observed in 53 cases (28.0%). According to logistic regression models, only deep ILNM (OR 9.72, p