The tumor microenvironment (TME) is critical for cancer initiation, growth, metastasis, and therapeutic resistance. The extracellular matrix (ECM) is a significant tumor component that serves various functions, including mechanical support, TME regulation, and signal molecule generation. The quantity and cross‐linking status of ECM components are crucial factors in tumor development, as they determine tissue stiffness and the interaction between stiff TME and cancer cells, resulting in aberrant mechanotransduction, proliferation, migration, invasion, angiogenesis, immune evasion, and treatment resistance. Therefore, broad knowledge of ECM dysregulation in the TME might aid in developing innovative cancer therapies. This review summarized the available information on major ECM components, their functions, factors that increase and decrease matrix stiffness, and related signaling pathways that interplay between cancer cells and the ECM in TME. Moreover, mechanotransduction alters during tumorogenesis, and current drug therapy based on ECM as targets, as well as future efforts in ECM and cancer, are also discussed.

Bladder cancer (BC) ranks as the sixth cancer in males and the ninth most common cancer worldwide. Conventional treatment modalities, including surgery, radiation, chemotherapy, and immunotherapy, have limited efficacy in certain advanced instances. The involvement of GALNT6-mediated aberrant O-glycosylation modification in several malignancies and immune evasion is a subject of speculation. However, its significance in BC has not been investigated. Through the integration of bioinformatics analysis and laboratory experimentation, we have successfully clarified the role of GALNT6 in BC. Our investigation revealed that GALNT6 has significant expression in BC, and its high expression level correlates with advanced stage and high grade, leading to poor overall survival. Moreover, both in vitro and in vivo experiments demonstrate a strong correlation between elevated levels of GALNT6 and tumor growth, migration, and invasion. Furthermore, there is a negative correlation between elevated GALNT6 levels, the extent of CD8+ T cell infiltration in the tumor microenvironment, and the prognosis of patients. Functional experiments have shown that the increased expression of GALNT6 could enhance the malignant characteristics of cancer cells by activating the epithelial-mesenchymal transition (EMT) pathway. In brief, this study examined the impact of GALNT6-mediated abnormal O-glycosylation on the occurrence and progression of bladder cancer and its influence on immune evasion. It also explored the possible molecular mechanism underlying the interaction between tumor cells and immune cells, as well as the bidirectional signaling involved. These findings offer a novel theoretical foundation rooted in glycobiology for the clinical application of immunotherapy in BC.

Background: It is anticipated that there will be a large rise in the number of tumor diagnoses and mortality in those aged 65 and older over the course of upcoming decades. Immune checkpoint inhibitors, often known as ICIs, boost immune system activity by selectively targeting ICI genes. On the other hand, old age may be connected with unfavorable results. Methods: The Cancer Genome Atlas (TCGA) provided gene expression data from ccRCC tissue and key clinical variables. ICI gene databases were applied and verified using the GEO database. Results: We identified 14 ICI genes as risk gene signatures among 528 ccRCC patients using univariate and multivariable cox hazard models, and the elderly group was linked with poor survival. Then, by utilizing a new nomogram method, the TNFSF15 gene and age predicting values were estimated at one, three, and five years (85%, 81%, and 81%), respectively, and our age-related risk score was significant even after multivariable analysis (HR = 1.518, p = 0.009, CI = 1.1102.076). TNFSF15 gene expression was lower in elderly ccRCC patients (p = 0.0001). A negative connection between age and the TNFSF15 gene expression was discovered by correlation analysis (p = 0.0001). The verification of the gene by utilizing GEO (GSE167093) with 604 patients was obtained as external validation that showed significant differences in the TNFSF15 gene between young and elderly patients (p = 0.007). Additionally, the protein–protein interactions of the TNFSF15 gene with other ICI genes and aging-related genes was determined. In addition, the TNFSF15 expression was significantly correlated with pathological stages (p = 0.018). Furthermore, it was discovered that the biological processes of senescence, cellular senescence, the immune system, and many immune cell infiltration and immune function types are all closely tied. Conclusions: Along with the risk score evaluation, the ICI gene TNFSF15 was identified as a tumor suppressor gene related to inequalities in age survival and is associated with pathological stages and different immunity statuses. The aging responses of ccRCC patients and related gene expression need further investigation in order to identify potential therapeutic targets.

Background. To construct and validate a deep learning cluster from whole slide images (WSI) for depicting the immunophenotypes and functional heterogeneity of the tumor microenvironment (TME) in patients with bladder cancer (BLCA) and to explore an artificial intelligence (AI) score to explore the underlying biological pathways in the developed WSI cluster. Methods. In this study, the WSI cluster was constructed based on a deep learning procedure. Further rerecognition of TME features in pathological images was applied based on a neural network. Then, we integrated the TCGA cohort and several external testing cohorts to explore and validate this novel WSI cluster and a corresponding quantitative indicator, the AI score. Finally, correlations between the AI cluster (AI score) and classical BLCA molecular subtypes, immunophenotypes, functional heterogeneity, and potential therapeutic method in BLCA were assessed. Results. The WSI cluster was identified associated with clinical survival ( $P<0.001$ ) and was proved as an independent predictor ( $P=0.031$ ), which could also predict the immunology and the clinical significance of BLCA. Rerecognition of pathological images established a robust 3-year survival prediction model (with an average classification accuracy of 86%, AUC of 0.95) for BLCA patients combining TME features and clinical features. In addition, an AI score was constructed to quantify the underlying logic of the WSI cluster ( $\text{AUC}=0.838$ ). Finally, we hypothesized that high AI score shapes an immune-hot TME in BLCA. Thus, treatment options including immune checkpoint blockade (ICB), chemotherapy, and ERBB therapy can be used for the treatment of BLCA patients in WSI cluster1 (high AI score subtype). Conclusions. In general, we showed that deep learning can predict prognosis and may aid in the precision medicine for BLCA directly from H&E histology, which is more economical and efficient.

Background. To assess the clinical characteristics, radiological predictors, and pathological features of perinephric fat adhesion degree (PFAD) graded based on fixed criteria and to determine the impact of adherent perinephric fat (APF) on retroperitoneal laparoscopic partial nephrectomy (RLPN) outcomes. Methods. 84 patients undergoing RLPN were included and graded into 4 groups based on PFAD. Univariate and multivariate analyses were performed for clinical characteristics and radiological predictors of PFAD. Perioperative data were compared between APF groups and non-APF groups. Masson staining determined collagen fibers. Immunohistochemistry detected CD45 immune cells and CD34 vessels. Results. 20, 28, 18, and 18 patients were graded as normal perinephric fat (NPF), mild adherent perinephric fat (MiPF), moderate adherent perinephric fat (MoPF), and severe adherent perinephric fat (SPF), respectively. Multivariate analysis revealed that gender ( $p$ < 0.001), age ( $p$ = 0.003), and hypertension ( $p$ = 0.006) were significant clinical risk factors of PFAD, while radiological predictors included perinephric stranding ( $p$ = 0.001), posterior perinephric fat thickness ( $p$ = 0.009), and perinephric fat density ( $p$ = 0.02). APF was associated with drain output ( $p$ = 0.012) and accompanied by immune cells gathering in renal cortex near thickened renal capsule with many vessels. Conclusions. Clinical characteristics and radiological predictors can evaluate PFAD and may assist to guide preoperative surgical option. Pathological features of APF reflect decapsulation and bleeding during kidney mobilization at RLPN.

Bladder cancer (BCa) is a heterogeneous malignancy characterized by distinct immune subtypes, primarily due to differences in tumor-infiltrating immune cells and their functional characteristics. Therefore, understanding the tumor immune microenvironment (TIME) landscape in BCa is crucial for prognostic prediction and guiding precision therapy. In this study, we integrated 10 machine learning algorithms to develop an immune-related machine learning signature (IRMLS) and subsequently created a deep learning model to detect the IRMLS subtype based on pathological images. The IRMLS proved to be an independent prognostic factor for overall survival (OS) and demonstrated robust and stable performance (p < 0.01). The high-risk group exhibited an immune-inflamed phenotype, associated with poorer prognosis and higher levels of immune cell infiltration. We further investigated the cancer immune cycle and mutation landscape within the IRMLS model, confirming that the high-risk group is more sensitive to immune checkpoint immunotherapy (ICI) and adjuvant chemotherapy with cisplatin (p = 2.8e-10), docetaxel (p = 8.8e-13), etoposide (p = 1.8e-07), and paclitaxel (p = 6.2e-13). In conclusion, we identified and validated a machine learning-based molecular characteristic, IRMLS, which reflects various aspects of the BCa biological process and offers new insights into personalized precision therapy for BCa patients.