Basement membrane promotes tumor development by attenuating T cell activation

Prognostic significance of NUAK1 and its association with immune infiltration in stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) represents a significant global health burden, accounting for a considerable proportion of cancer-related mortalities, and NUAK1, a protein kinase, plays a crucial role in cellular metabolism, cell cycle regulation, migration, and tumor progression. However, its relationship with prognosis and immune infiltration in STAD has not been thoroughly investigated.

METHODS

RNA sequencing data from the Cancer Genome Atlas (TCGA) and Genotypic Tissue Expression Project (GTEx) databases were employed to assess disparities in NUAK1 expression between STAD tumour and normal tissues. Additionally, we investigated the correlation between NUAK1 expression and patient prognosis, in addition to the level of immune cell infiltration. The potential functions were elucidated through an examination of the Gene Ontology (GO) Encyclopedia, the Kyoto Encyclopedia of Genes and Genomes (KEGG), and an enrichment analysis (GSEA). The GeneMANIA was used to validate the functions of nuak1-related genes.

RESULTS

Our analysis demonstrated that NUAK1 expression in tumour tissues exhibited a notable disparity from that observed in normal tissues, with elevated levels detected in STAD tissues. We used the GeneMANIA database to identify functionally similar genes with significantly higher expression for some genes in the unpaired group samples. An elevated NUAK1 expression level was found to correlate with a poorer overall survival (OS), disease-specific survival (DSS), and progression-free intervals (PFI). Additionally, immune infiltration analysis indicated a significant positive correlation between NUAK1 expression and various tumor-infiltrating immune cells, while a negative correlation was observed with T helper cell 17(Th17) cells. Furthermore, enrichment analysis was conducted to identify relevant biological features and pathways.

CONCLUSION

The expression levels of NUAK1 are significantly increased in STAD, and this heightened expression correlates with diminished OS, DSS, and PFI among affected patients. These observations indicate that NUAK1 has the potential to function as a prognostic biomarker for STAD and may represent a viable therapeutic target for intervention in its management.

Adaptive immunity of materials: Implications for tissue healing and regeneration

Recent cumulative findings signify the adaptive immunity of materials as a key agenda in tissue healing that can improve regenerative events and outcomes. Modulating immune responses, mainly the recruitment and functions of T and B cells and their further interplay with innate immune cells (e.g., dendritic cells, macrophages) can be orchestrated by materials. For instance, decellularized matrices have been shown to promote muscle healing by inducing T helper 2 (Th2) cell immunity, while synthetic biopolymers exhibit differential effects on B cell responses and fibrosis compared decellularized matrices. We discuss the recent findings on how implantable materials instruct the adaptive immune events and the subsequent tissue healing process. In particular, we dissect the materials' physicochemical properties (shape, size, topology, degradation, rigidity, and matrix dynamic mechanics) to demonstrate the relations of these parameters with the adaptive immune responses in vitro and the underlying biological mechanisms. Furthermore, we present evidence of recent in vivo phenomena, including tissue healing, cancer progression, and fibrosis, wherein biomaterials potentially shape adaptive immune cell functions and in vivo outcomes. Our discussion will help understand the materials-regulated immunology events more deeply, and offer the design rationale of materials with tunable matrix properties for accelerated tissue repair and regeneration.

Identification of basement membrane-related prognostic model associated with the immune microenvironment and synthetic therapy response in pancreatic cancer: integrated bioinformatics analysis and clinical validation

Pancreatic cancer (PC) is a common and highly malignant tumor. Basement membrane (BM) is formed by the crosslinking of extracellular matrix macromolecules and acts as a barrier against tumor cell metastasis. However, the role of BM in PC prognosis, immune infiltration, and treatment remains unclear. This study collected transcriptome and clinical survival data of PC via TCGA, GEO, and ICGC databases. PC patients (PCs) from the First Affiliated Hospital of Dalian Medical University were obtained as the clinical validation cohort. BM-related genes (BMRGs) were acquired from GeneCards and basement membraneBASE databases. A total of 46 differential-expressed BMRGs were identified. Then the BM-related prognostic model (including DSG3, MET, and PLAU) was built and validated. PCs with a low BM-related score had a better outcome and were more likely to benefit from oxaliplatin, irinotecan, and KRAS(G12C) inhibitor-12, and immunotherapy. Immune analysis revealed that BM-related score was positively correlated with neutrophils, cancer-associated fibroblasts, and macrophages infiltration, but negatively correlated with CD8+ T cells, NK cells, and B cells infiltration. PCs from the clinical cohort further verified that BM-related model could accurately predict PCs' outcomes. DSG3, MET, and PLAU were notably up-regulated within PC tissues and linked to a poor prognosis. In vitro experiments showed that DSG3 knockdown markedly suppressed the proliferation, migration, and invasion of PC cells. Molecular docking indicated that epigallocatechin gallate had a strong binding activity with DSG3, MET, and PLAU and may be used as a potential therapeutic agent for PC. In conclusion, this study developed a BM-related model associated with PC prognosis, immune infiltration, and treatment, which provided new insights into PC stratification and drug intervention.

Prognostic biomarkers associated with immune checkpoint inhibitors in hepatocellular carcinoma

The treatment of hepatocellular carcinoma (HCC), particularly advanced HCC, has been a serious challenge. Immune checkpoint inhibitors (ICIs) are landmark drugs in the field of cancer therapy in recent years, which have changed the landscape of cancer treatment. In the field of HCC treatment, this class of drugs has shown good therapeutic prospects. For example, atezolizumab in combination with bevacizumab has been approved as first-line treatment for advanced HCC due to significant efficacy. However, sensitivity to ICI therapy varies widely among HCC patients. Therefore, there is an urgent need to search for determinants of resistance/sensitivity to ICIs and to screen biomarkers that can predict the efficacy of ICIs. This manuscript reviews the research progress of prognostic biomarkers associated with ICIs in HCC in order to provide a scientific basis for the development of clinically individualised precision medication regimens.

Identification and Validation of Basement Membrane Related LncRNA Signatures as a Novel Prognostic Model for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a significant cancer with limited treatments and a poor prognosis, with the basement membrane (BM) playing a crucial role in its initiation and growth. This study utilized data from The Cancer Genome Atlas and the Gene Expression Omnibus (GEO) databases to identify basement membrane-related genes differentially expressed in HCC. Through gene co-expression analysis, BM-associated long non-coding RNAs (lncRNAs) were discovered. LncRNAs related to HCC survival were selected via univariate analysis, and a prognostic model was constructed using LASSO regression and multivariate analysis. This model effectively classified HCC patients into high and low-risk groups, uncovering significant differences in prognosis, immune response, mutation, and drug sensitivity. Six BM-related lncRNAs (GSEC, MIR4435-2HG, AC092614.1, AC127521.1, LINC02580, and AC008050.1) were validated in normal and HCC cell lines, and the key role of AC092614.1 in regulating proliferation, migration, and invasion of HCC cells in vitro was explored. This research emphasizes the prognostic and therapeutic relevance of BM-related lncRNAs in HCC, highlighting AC092614.1's role in disease progression and as a potential target for targeted therapy.

Oncogene-induced matrix reorganization controls CD8+ T cell function in the soft-tissue sarcoma microenvironment

CD8+ T cell dysfunction impedes antitumor immunity in solid cancers, but the underlying mechanisms are diverse and poorly understood. Extracellular matrix (ECM) composition has been linked to impaired T cell migration and enhanced tumor progression; however, impacts of individual ECM molecules on T cell function in the tumor microenvironment (TME) are only beginning to be elucidated. Upstream regulators of aberrant ECM deposition and organization in solid tumors are equally ill-defined. Therefore, we investigated how ECM composition modulates CD8+ T cell function in undifferentiated pleomorphic sarcoma (UPS), an immunologically active desmoplastic tumor. Using an autochthonous murine model of UPS and data from multiple human patient cohorts, we discovered a multifaceted mechanism wherein the transcriptional coactivator YAP1 promotes collagen VI (COLVI) deposition in the UPS TME. In turn, COLVI induces CD8+ T cell dysfunction and immune evasion by remodeling fibrillar collagen and inhibiting T cell autophagic flux. Unexpectedly, collagen I (COLI) opposed COLVI in this setting, promoting CD8+ T cell function and acting as a tumor suppressor. Thus, CD8+ T cell responses in sarcoma depend on oncogene-mediated ECM composition and remodeling.

Identification of a basement membrane gene signature for predicting prognosis and estimating the tumor immune microenvironment in prostate cancer

Basement membrane plays an important role in tumor invasion and metastasis, which is closely related to prognosis. However, the prognostic value and biology of basement membrane genes (BMGs) in prostate cancer (PCa) remain unknown. In the TCGA training set, we used differentially expressed gene analysis, protein-protein interaction networks, univariate and multivariate Cox regression, and least absolute shrinkage and selection operator regression to construct a basement membrane-related risk model (BMRM) and validated its effectiveness in the MSKCC validation set. Furthermore, the accurate nomogram was constructed to improve clinical applicability. Patients with PCa were divided into high-risk and low-risk groups according to the optimal cut-off value of the basement membrane-related risk score (BMRS). It was found that BMRS was significantly associated with RFS, T-stage, Gleason score, and tumor microenvironmental characteristics in PCa patients. Further analysis showed that the model grouping was closely related to tumor immune microenvironment characteristics, immune checkpoint inhibitors, and chemotherapeutic drug sensitivity. In this study, we developed a new BMGs-based prognostic model to determine the prognostic value of BMGs in PCa. Finally, we confirmed that THBS2, a key gene in BMRM, may be an important link in the occurrence and progression of PCa. This study provides a novel perspective to assess the prognosis of PCa patients and provides clues for the selection of future personalized treatment regimens.

Pan-cancer spatially resolved single-cell analysis reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment

Cancer-associated fibroblasts (CAFs) are a heterogeneous cell population that plays a crucial role in remodeling the tumor microenvironment (TME). Here, through the integrated analysis of spatial and single-cell transcriptomics data across six common cancer types, we identified four distinct functional subgroups of CAFs and described their spatial distribution characteristics. Additionally, the analysis of single-cell RNA sequencing (scRNA-seq) data from three additional common cancer types and two newly generated scRNA-seq datasets of rare cancer types, namely epithelial-myoepithelial carcinoma (EMC) and mucoepidermoid carcinoma (MEC), expanded our understanding of CAF heterogeneity. Cell-cell interaction analysis conducted within the spatial context highlighted the pivotal roles of matrix CAFs (mCAFs) in tumor angiogenesis and inflammatory CAFs (iCAFs) in shaping the immunosuppressive microenvironment. In patients with breast cancer (BRCA) undergoing anti-PD-1 immunotherapy, iCAFs demonstrated heightened capacity in facilitating cancer cell proliferation, promoting epithelial-mesenchymal transition (EMT), and contributing to the establishment of an immunosuppressive microenvironment. Furthermore, a scoring system based on iCAFs showed a significant correlation with immune therapy response in melanoma patients. Lastly, we provided a web interface ( https://chenxisd.shinyapps.io/pancaf/ ) for the research community to investigate CAFs in the context of pan-cancer.

Comprehensive analysis of basement membrane and immune checkpoint related lncRNA and its prognostic value in hepatocellular carcinoma via machine learning

Background

Hepatocellular carcinoma (HCC), which is characterized by its high malignancy, generally exhibits poor response to immunotherapy. As part of the tumor microenvironment, basement membranes (BMs) are involved in tumor development and immune activities. Presently, there is no integrated analysis linking the basement membrane with immune checkpoints, especially from the perspective of lncRNA.

Methods

Based on transcriptome data from The Cancer Genome Atlas, BMs-related and immune checkpoint-related lncRNAs were identified. By applying univariable Cox regression and Machine learning (LASSO and SVM-RFE algorithm), a 10-lncRNA prognosis signature was constructed. The prognostic significance of this signature was assessed by survival analysis. GSEA, ssGSEA, and drug sensitivity analysis were conducted to investigate potential functional pathways, immune status, and clinical implications of guiding individual treatments in HCC. Finally, the promoting migration effect of LINC01224 was validated via in vitro experiments.

Results

The multiple Cox regression, receiver operating characteristic curves, and stratified survival analysis of clinical subgroups exhibited the robust prognostic ability of the lncRNA signature. Results of the GSEA and drug sensitivity analysis revealed significant differences in potential functional pathways and response to drugs between the two risk groups. In addition, the risk level of HCC patients was distinctly correlated with immune cell infiltration status. More importantly, LINC01224 was independently associated with the OS of HCC patients (P < 0.05), suppressing the expression of LINC01224 inhibited the migration of HCC cells.

Conclusion

This study developed a reliable signature for the prognosis of HCC based on BM and immune checkpoint related lncRNA, revealing that LINC01224 might be a prognostic biomarker for HCC associated with the progression of HCC.

Identification of a basement membrane-related genes signature to predict prognosis, immune landscape and guide therapy in gastric cancer

The basement membrane is an essential defense against cancer progression and is intimately linked to the tumor immune microenvironment. However, there is limited research comprehensively discussing the potential application of basement membrane-related genes (BMRGs) in the prognosis evaluation and immunotherapy of gastric cancer (GC). The RNA-seq data and clinical information of GC patients were collected from the TCGA and GEO database. Prognosis-associated BMRGs were filtered via univariate Cox regression analysis. The 4-BMRGs signatures were constructed by lasso regression. Prognostic predictive accuracy of the 4-BMRGs signature was appraised with survival analysis, receiver operating characteristic curves, and nomogram. Gene set enrichment analysis (GSEA), gene ontology, and gene set variation analysis were performed to dig out potential mechanisms and functions. The Estimate algorithm and ssGSEA were used for assessing the tumor microenvironment and immunological characteristics. Identification of molecular subtypes by consensus clustering. Drug sensitivity analysis using the "pRRophetic" R package. Immunotherapy validation with immunotherapy cohort. A 4-BMRGs signature was constructed, which could excellently predict the GC patient prognosis (5-year AUC value of 0.873). Kaplan-Meier and Cox regression analyses showed that the 4-BMRGs signature was an OS-independent prognostic factor, and that higher risk scores were associated with shorter OS. The high-risk subgroup exhibits a higher abundance of immune cell infiltration, such as macrophages. Additionally, we observed a strong correlation between 2 BMRGs (LUM, SPARC) and immune cells such as CD8 + T cells and macrophages. The high-risk subgroup appears to be more sensitive to Axitinib, DMOG, Gemcitabine and Docetaxel by pRRophetic analysis. Furthermore, the validation of the cohort that received immune therapy revealed that patients in the high-risk group who underwent immune checkpoint inhibitor treatment exhibited better response rates. Pan-cancer analysis also shows that risk scores are strongly associated with immune and carcinogenic pathways. The 4-BMRGs signature has demonstrated accuracy and reliability in predicting the GC patient's prognosis and could assist in the formulation of clinical strategies.

A novel basement membrane-related gene signature for prognosis of lung adenocarcinomas

BACKGROUND

Lung adenocarcinoma (LUAD) remains a global health concern with its poor prognosis and high mortality. Whether tumor cells invade through the basement membrane (BM) is the key factor to determine the prognosis of LUAD. This study aimed to identify the BM-related gene signatures to improve the overall prognosis of LUAD.

MATERIALS & METHODS

A series of bioinformatics analyses were conducted based on TCGA and GEO datasets. Unsupervised consistent cluster analysis was performed, and 500 LUAD patients were assigned to two different groups according to expressions of 222 BM-related genes. The differentially expressed genes (DEGs) between the two clusters were identified, and Lasso regression, ROC curve, univariate and multivariate Cox regression analyses and enrichment analysis were conducted. Besides, ssGSEA, CIBERSORT and ESTIMATE algorithmwere were employed to understand the relationship between the tumor microenvironment (TME) and risk scores. Moreover, single cell clustering and trajectory analyses were performed to further understand the significance of BM-related genes. Finally, qRT-PCR was used to verify the prognosis model.

RESULTS

A total of 31 prognostic BM-related genes were determined for LUAD, and a novel 17-mRNA prognostic model named BMsocre was successfully established to predict the overall survival of LUAD patients. The high BMscore group indicated worse prognosis. Seventeen DEGs were enriched mainly in metabolism, ECM-receptor interaction and immune response. In addition, the high-risk group showed higher TMB and lower immune score. The low-risk group had a better immunotherapeutic response where immune escape was less likely. The BMscore model was verified in our patient cohort. Furthermore, NELL2 was mainly expressed in clusters of T cells, and was identified to play a critical role in T-cell differentiation.

CONCLUSIONS

A novel BMscore model was successfully established and might be effective for providing guidance to LUAD therapy.

A Model of Basement Membrane-Associated Gene Signature Predicts Liver Hepatocellular Carcinoma Response to Immune Checkpoint Inhibitors

Liver hepatocellular carcinoma (LIHC) is a highly lethal malignant tumor originating from the digestive system, which is a serious threat to human health. In recent years, immunotherapy has shown significant therapeutic effects in the treatment of LIHC, but only for a minority of patients. The basement membrane (BM) plays an important role in the occurrence and development of tumors, including LIHC. Therefore, this study is aimed at establishing a risk score model based on basement membrane-related genes (BMRGs) to predict patient prognosis and response to immunotherapy. First, we defined three patterns of BMRG modification (C1, C2, and C3) by consensus clustering of BMRG sets and LIHC transcriptome data obtained from public databases. Survival analysis showed that patients in the C2 group had a better prognosis, and Gene Set Variation Analysis (GSVA) revealed that the statistically significant pathways were mainly enriched in the C2 group. Moreover, we performed Weighted Correlation Network Analysis (WGCNA) on the above three subgroups and obtained 179 intersecting genes. We further applied function enrichment analyses, and the results demonstrated that they were mainly enriched in metabolism-related pathways. Furthermore, we conducted the LASSO regression analysis and obtained 4 BMRGs (MPV17, GNB1, DHX34, and MAFG) that were significantly related to the prognosis of LIHC patients. We further constructed a prognostic risk score model based on the above genes, which was verified to have good predictive performance for LIHC prognosis. In addition, we analyzed the correlation between the risk score and the tumor immune microenvironment (TIM), and the results showed that the high-risk scoring group tended to be in an immunosuppressed status. Finally, we investigated the relationship between the risk score and LIHC immune function. The results demonstrated that the risk score was closely related to the expression levels of multiple immune checkpoints. Patients in the low-risk group had significantly higher IPS scores, and patients in the high-risk group had lower immune escape and TIDE score. In conclusion, we established a novel risk model based on BMRGs, which may serve as a biomarker for prognosis and immunotherapy in LIHC.

Identification of a basement membrane-based risk scoring system for prognosis prediction and individualized therapy in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) belongs to one of the 10 most frequently diagnosed cancers worldwide and has a poor prognosis at the advanced stage. Although multiple therapeutic agents have been proven to be curative in ccRCC, their clinical application was limited due to the lack of reliable biomarkers. Considering the important role of basement membrane (BM) in tumor metastasis and TME regulation, we investigated the expression of BM-related genes in ccRCC and identified prognostic BM genes through differentially expression analysis and univariate cox regression analysis. Then, BM-related ccRCC subtypes were recognized through consensus non-negative matrix factorization based on the prognostic BM genes and evaluated with regard to clinical and TME features. Next, utilizing the differentially expressed genes between the BM-related subtypes, a risk scoring system BMRS was established after serial analysis of univariate cox regression analysis, lasso regression analysis, and multivariate cox regression analysis. Time-dependent ROC curve revealed the satisfactory prognosis predictive capacity of BMRS with internal, and external validation. Multivariate analysis proved the independent predictive ability of BMRS and a BMRS-based nomogram was constructed for clinical application. Some featured mutants were discovered through genomic analysis of the BMRS risk groups. Meanwhile, the BMRS groups were found to have distinct immune scores, immune cell infiltration levels, and immune-related functions. Moreover, with the help of data from The Cancer Immunome Atlas (TCIA) and Genomics of Drug Sensitivity in Cancer (GDSC), the potential of BMRS in predicting therapeutic response was evaluated and some possible therapeutic compounds were proposed through ConnectivityMap (CMap). For the practicability of BMRS, we validated the expression of BMRS-related genes in clinical samples. After all, we identified BM-related ccRCC subtypes with distinct clinical and TME features and constructed a risk scoring system for the prediction of prognosis, therapeutic responses, and potential therapeutic agents of ccRCC. As ccRCC systemic therapy continues to evolve, the risk scoring system BMRS we reported may assist in individualized medication administration.

The basement membrane-related gene signature is associated with immunity and predicts survival accurately in hepatocellular carcinoma

AIMS

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Expression defects and turnover of basement membrane (BM) proteins are key pathogenic factors in cancer. It is still uncertain how the expression of BM-related genes (BMGs) in HCC relates to prognosis.

METHODS

All of the HCC cohort's RNA-seq and clinical information came from TCGA datasets. The least absolute shrinkage and selection operator (LASSO) regression algorithm was utilized to filter down the candidate genes and construct the prognostic model. Univariate and multivariate Cox analyses were run to examine if the risk score may serve as a standalone prognostic indicator. The single-sample gene set enrichment analysis (ssGSEA) was utilized to analyze examine immune cell infiltration and pathway activity.

RESULTS

Five genes and their risk coefficients were eventually identified and patients with HCC were classified as either high or low risk based on the median of risk scores. Multivariate Cox regression analysis found a significant correlation between risk score and OS (p < 0.001). Subgroup analysis showed that BMGs signature had good prediction ability for HCC patients in age, gender, T stage, and AJCC stage (all p < 0.05). According to the ssGSEA, the high-risk subgroup showed higher levels of immune cell infiltration and immune-related pathways were more engaged in the high-risk group.

CONCLUSIONS

Our research systematically built a prognostic model using risk score based on BMGs signature in HCC patients. The immune feature analysis of the BMGs signature indicated a potential regulation between tumor immunity and BM in HCC.

A prognostic and therapeutic hallmark developed by the integrated profile of basement membrane and immune infiltrative landscape in lung adenocarcinoma

Basement membranes (BMs) are specialised extracellular matrices that maintain cellular integrity and resist the breaching of carcinoma cells for metastases while regulating tumour immunity. The tumour immune microenvironment (TME) is essential for tumour growth and the response to and benefits from immunotherapy. In this study, the BM score and TME score were constructed based on the expression signatures of BM-related genes and the presence of immune cells in lung adenocarcinoma (LUAD), respectively. Subsequently, the BM-TME classifier was developed with the combination of BM score and TME score for accurate prognostic prediction. Further, Kaplan-Meier survival estimation, univariate Cox regression analysis and receiver operating characteristic curves were used to cross-validate and elucidate the prognostic prediction value of the BM-TME classifier in several cohorts. Findings from functional annotation analysis suggested that the potential molecular regulatory mechanisms of the BM-TME classifier were closely related to the cell cycle, mitosis and DNA replication pathways. Additionally, the guiding value of the treatment strategy of the BM-TME classifier for LUAD was determined. Future clinical disease management may benefit from the findings of our research.