Extracellular Matrix Characterization in Gastric Cancer Helps to Predict Prognosis and Chemotherapy Response

The extracellular matrix protein EMILIN-1 impacts on the microenvironment by hampering gastric cancer development and progression

BACKGROUND

The contribution of the tumor microenvironment and extracellular matrix to the aggressive biology of Gastric Cancer (GC) has been recently characterized; however, the role of EMILIN-1 in this context is unknown. EMILIN-1 is an essential structural element for the maintenance of lymphatic vessel (LV) integrity and displays anti-proliferative properties as demonstrated in skin and colon cancer. Given the key role of LVs in GC progression, the aim of this study was to investigate the role of EMILIN-1 in GC mouse models.

METHODS

We used the syngeneic YTN16 cells which were injected subcutaneously and intraperitoneally in genetically modified EMILIN-1 mice. In alternative, carcinogenesis was induced using N-Methyl-N-nitrosourea (MNU). Mouse-derived samples and human biopsies were analyzed by IHC and IF to the possible correlation between EMILIN-1 expression and LV pattern.

RESULTS

Transgenic mice developed tumors earlier compared to WT animals. 20 days post-injection tumors developed in EMILIN-1 mutant mice were larger and displayed a significant increase of lymphangiogenesis. Treatment of transgenic mice with MNU associated with an increased number of tumors, exacerbated aggressive lesions and higher levels of LV abnormalities. A significant correlation between the levels of EMILIN-1 and podoplanin was detected also in human samples, confirming the results obtained with the pre-clinical models.

CONCLUSIONS

This study demonstrates for the first time that loss of EMILIN-1 in GC leads to lymphatic dysfunction and proliferative advantages that sustain tumorigenesis, and assess the use of our animal model as a valuable tool to verify the fate of GC upon loss of EMILIN-1.

[Screening of differentially expressed genes in gastric cancer based on GEO database and function and pathway enrichment analysis]

OBJECTIVE

To explore the core genes related to the diagnosis and prognosis of gastric cancer (GC) based on Gene Expression Omnibus (GEO) database and screen the molecular targets involved in the occurrence and development of GC.

METHODS

GC microarray data GSE118916, GSE54129 and GSE79973 were downloaded from GEO database, and the differentially expressed genes (DEGs) were screened. Enrichment analysis of the signaling pathways and molecular functions were preformed and protein-protein interaction networks (PPI) were constructed to identify the hub genes, whose expression levels and diagnostic and prognostic values were verifies based on gastric adenocarcinoma data from TCGA. The expression levels of these core genes were also detected in different GC cell lines using qRT- PCR.

RESULTS

Seventy-seven DEGs were identified, which encodes proteins located mainly in the extracellular matrix and basement membrane with activities of oxidoreductase and extracellular matrix receptor and ligand, involving the biological processes of digestion and hormone metabolism and the signaling pathways in retinol metabolism and gastric acid secretion. Nine hub genes were obtained, among which SPARC, TIMP1, THBS2, COL6A3 and THY1 were significantly up- regulated and TFF1, GKN1, TFF2 and PGC were significantly down-regulated in GC. The abnormal expressions of SPARC, TIMP1, THBS2, COL6A3, TFF2 and THY1 were significantly correlated with the survival time of GC patients. ROC curve analysis showed that aberrant expression of TIMP1 SPARC, THY1 and THBS2 had high diagnostic value for GC. High expressions of SPARC, TIMP1, THBS2 and COL6A3 were detected in GC tissues. In the GC cell lines, qRT- PCR revealed different expression patterns of these hub genes, but their expressions were largely consistent with those found in bioinformatics analyses.

CONCLUSION

SPARC, TIMP1, THBS2 and other DEGs are probably involved in GC occurrence and progression and may serve as potential candidate molecular markers for early diagnosis and prognostic evaluation of GC.

Monitoring the Intracellular pH and Metabolic State of Cancer Cells in Response to Chemotherapy Using a Combination of Phosphorescence Lifetime Imaging Microscopy and Fluorescence Lifetime Imaging Microscopy

The extracellular matrix (ECM), in which collagen is the most abundant protein, impacts many aspects of tumor physiology, including cellular metabolism and intracellular pH (pHi), as well as the efficacy of chemotherapy. Meanwhile, the role of collagen in differential cell responses to treatment within heterogeneous tumor environments remains poorly investigated. In the present study, we simultaneously monitored the changes in pHi and metabolism in living colorectal cancer cells in vitro upon treatment with a chemotherapeutic combination, FOLFOX (5-fluorouracil, oxaliplatin and leucovorin). The pHi was followed using the new pH-sensitive probe BC-Ga-Ir, working in the mode of phosphorescence lifetime imaging (PLIM), and metabolism was assessed from the autofluorescence of the metabolic cofactor NAD(P)H using fluorescence lifetime imaging (FLIM) with a two-photon laser scanning microscope. To model the ECM, 3D collagen-based hydrogels were used, and comparisons with conventional monolayer cells were made. It was found that FOLFOX treatment caused an early temporal intracellular acidification (reduction in pHi), followed by a shift to more alkaline values, and changed cellular metabolism to a more oxidative state. The presence of unstructured collagen markedly reduced the cytotoxic effects of FOLFOX, and delayed and diminished the pHi and metabolic responses. These results support the observation that collagen is a factor in the heterogeneous response of cancer cells to chemotherapy and a powerful regulator of their metabolic behavior.

Normal Uterine Fibroblast Are Reprogramed into Ovarian Cancer-Associated Fibroblasts by Ovarian Tumor-derived Conditioned Media

Cancer-associated fibroblasts (CAFs) are key contributors to ovarian cancer (OC) progression and therapeutic resistance through dysregulation of the extracellular matrix (ECM). CAFs are a heterogenous population derived from different cell types through activation and reprogramming. Current studies rely on uncharacterized heterogenous primary CAFs or normal fibroblasts that fail to recapitulate CAF-like tumor behavior. Here, we present a translatable-based approach for the reprogramming of normal uterine fibroblasts into ovarian CAFs using ovarian tumor-derived conditioned media to establish two well-characterized ovarian conditioned CAF lines. Phenotypic and functional characterization demonstrated that the conditioned CAFs expressed a CAF-like phenotype, strengthened proliferation, secretory, contractility, and ECM remodeling properties when compared to resting normal fibroblasts, consistent with an activated fibroblast status. Moreover, conditioned CAFs significantly enhanced drug resistance and tumor progression and resembled a CAF-like subtype associated with worse prognosis. The present study provides a reproducible, cost-effective, and clinically relevant protocol to reprogram normal fibroblasts into CAFs using tumor-derived conditioned media. Using these resources, further development of therapeutics that possess potentiality and specificity towards CAF-mediated chemoresistance in OC are further warranted.

A prognostic matrix code defines functional glioblastoma phenotypes and niches

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better patient survival, respectively. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles provide potential biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification which could be applied to optimize treatment responses.

Transcriptome profiling of patient-derived tumor xenografts suggests novel extracellular matrix-related signatures for gastric cancer prognosis prediction

BACKGROUND

A major obstacle to the development of personalized therapies for gastric cancer (GC) is the prevalent heterogeneity at the intra-tumor, intra-patient, and inter-patient levels. Although the pathological stage and histological subtype diagnosis can approximately predict prognosis, GC heterogeneity is rarely considered. The extracellular matrix (ECM), a major component of the tumor microenvironment (TME), extensively interacts with tumor and immune cells, providing a possible proxy to investigate GC heterogeneity. However, ECM consists of numerous protein components, and there are no suitable models to screen ECM-related genes contributing to tumor growth and prognosis. We constructed patient-derived tumor xenograft (PDTX) models to obtain robust ECM-related transcriptomic signatures to improve GC prognosis prediction and therapy design.

METHODS

One hundred twenty two primary GC tumor tissues were collected to construct PDTX models. The tumorigenesis rate and its relationship with GC prognosis were investigated. Transcriptome profiling was performed for PDTX-originating tumors, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to extract prognostic ECM signatures and establish PDTX tumorigenicity-related gene (PTG) scores. The predictive ability of the PTG score was validated using two independent cohorts. Finally, we combined PTG score, age, and pathological stage information to establish a robust nomogram for GC prognosis prediction.

RESULTS

We found that PDTX tumorigenicity indicated a poor prognosis in patients with GC, even at the same pathological stage. Transcriptome profiling of PDTX-originating GC tissues and corresponding normal controls identified 383 differentially expressed genes, with enrichment of ECM-related genes. A robust prognosis prediction model using the PTG score showed robust performance in two validation cohorts. A high PTG score was associated with elevated M2 polarized macrophage and cancer-associated fibroblast infiltration. Finally, combining the PTG score with age and TNM stage resulted in a more effective prognostic model than age or TNM stage alone.

CONCLUSIONS

We found that ECM-related signatures may contribute to PDTX tumorigenesis and indicate a poor prognosis in GC. A feasible survival prediction model was built based on the PTG score, which was associated with immune cell infiltration. Together with patient ages and pathological TNM stages, PTG score could be a new approach for GC prognosis prediction.

The prognostic importance of red blood cell distribution width for gastric cancer: a systematic review and meta-analysis

Background

For cancer patients, red blood cell distribution width (RDW) is a readily accessible and cost-effective preoperative prognostic predictor. This study aimed to determine whether RDW is a predictive factor for individuals undergoing radical surgery for gastric cancer (GC).

Methods

A literature search was performed to select relevant studies for inclusion in the subsequent meta-analysis. Relevant data were pooled to assess the association between RDW and GC results, including overall survival (OS), disease-free survival (DFS), and cancer-specific survival (CSS), as well as clinicopathological features.

Results

The meta-analysis and systemic review included data from 8 studies comprising 1,587 individuals diagnosed with GC. In this context, RDW refers to the coefficient of variation of RDW (RDW-CV). A high level of RDW-CV was significantly associated with older age [odds ratio (OR) =2.25; 95% confidence interval (CI): 1.72-2.94; P<0.00001], larger tumor diameter (OR =1.90; 95% CI: 1.42-2.56; P<0.0001), and vascular invasion (OR =2.22; 95% CI: 1.10-4.49; P=0.03). After hazard ratios (HRs) and 95% CIs were pooled, RDW-CV was found to be an independent prognostic factor of OS (HR =1.79; 95% CI: 1.21-2.66; I2=85%; P=0.004), DFS (HR =1.81; 95% CI: 1.37-2.39; I2=0%; P<0.0001), and CSS (HR =2.73; 95% CI: 1.36-5.49; I2=0%; P=0.005) in patients with GC.

Conclusions

The association between high levels of RDW-CV and poor survival in GC suggests that RDW-CV may be a viable prognostic indicator for patients with GC.

Extracellular Matrix- and Integrin Adhesion Complexes-Related Genes in the Prognosis of Prostate Cancer Patients' Progression-Free Survival

Prostate cancer is a heterogeneous disease, and one of the main obstacles in its management is the inability to foresee its course. Therefore, novel biomarkers are needed that will guide the treatment options. The extracellular matrix (ECM) is an important part of the tumor microenvironment that largely influences cell behavior. ECM components are ligands for integrin receptors which are involved in every step of tumor progression. An underlying characteristic of integrin activation and ligation is the formation of integrin adhesion complexes (IACs), intracellular structures that carry information conveyed by integrins. By using The Cancer Genome Atlas data, we show that the expression of ECM- and IACs-related genes is changed in prostate cancer. Moreover, machine learning methods revealed that they are a source of biomarkers for progression-free survival of patients that are stratified according to the Gleason score. Namely, low expression of FMOD and high expression of PTPN2 genes are associated with worse survival of patients with a Gleason score lower than 9. The FMOD gene encodes protein that may play a role in the assembly of the ECM and the PTPN2 gene product is a protein tyrosine phosphatase activated by integrins. Our results suggest potential biomarkers of prostate cancer progression.

A prognostic matrix code defines functional glioblastoma phenotypes and niches

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better survival, respectively, of patients. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles may provide biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification to optimize treatment responses.

Leveraging various extracellular matrix levels to assess prognosis and sensitivity to immunotherapy in patients with ovarian cancer

Background

Ovarian cancer (OC) is the fifth leading cause of cancer-related deaths among women. Late diagnosis and heterogeneous treatment result in a poor prognosis for patients with OC. Therefore, we aimed to develop new biomarkers to predict accurate prognoses and provide references for individualized treatment strategies.

Methods

We constructed a co-expression network applying the "WGCNA" package and identified the extracellular matrix-associated gene modules. We figured out the best model and generated the extracellular matrix score (ECMS). The ECMS' ability to predict accurate OC patients' prognoses and responses to immunotherapy was evaluated.

Results

The ECMS was an independent prognostic factor in the training [hazard ratio (HR) = 3.132 (2.068-4.744), p< 0.001] and testing sets [HR = 5.514 (2.084-14.586), p< 0.001]. The receiver operating characteristic curve (ROC) analysis showed that the AUC values for 1, 3, and 5 years were 0.528, 0.594, and 0.67 for the training set, respectively, and 0.571, 0.635, and 0.684 for the testing set, respectively. It was found that the high ECMS group had shorter overall survival than the low ECMS group [HR = 2 (1.53-2.61), p< 0.001 in the training set; HR = 1.62 (1.06-2.47), p = 0.021 in the testing set; HR = 1.39 (1.05-1.86), p = 0.022 in the training set]. The ROC values of the ECMS model for predicting immune response were 0.566 (training set) and 0.572 (testing set). The response rate to immunotherapy was higher in patients with low ECMS.

Conclusion

We created an ECMS model to predict the prognosis and immunotherapeutic benefits in OC patients and provided references for individualized treatment of OC patients.

Comprehensive Profiling of Early Neoplastic Gastric Microenvironment Modifications and Biodynamics in Impaired BMP-Signaling FoxL1+-Telocytes

FoxL1⁺telocytes (TC^FoxL1+) are novel gastrointestinal subepithelial cells that form a communication axis between the mesenchyme and epithelium. TC^FoxL1+ are strategically positioned to be key contributors to the microenvironment through production and secretion of growth factors and extracellular matrix (ECM) proteins. In recent years, the alteration of the bone morphogenetic protein (BMP) signaling in TC^FoxL1+ was demonstrated to trigger a toxic microenvironment with ECM remodeling that leads to the development of pre-neoplastic gastric lesions. However, a comprehensive analysis of variations in the ECM composition and its associated proteins in gastric neoplasia linked to TC^FoxL1+ dysregulation has never been performed. This study provides a better understanding of how TC^FoxL1+ defective BMP signaling participates in the gastric pre-neoplastic microenvironment. Using a proteomic approach, we determined the changes in the complete matrisome of BmpR1a^△FoxL1+ and control mice, both in total antrum as well as in isolated mesenchyme-enriched antrum fractions. Comparative proteomic analysis revealed that the deconstruction of the gastric antrum led to a more comprehensive analysis of the ECM fraction of gastric tissues microenvironment. These results show that TC^FoxL1+ are key members of the mesenchymal cell population and actively participate in the establishment of the matrisomic fraction of the microenvironment, thus influencing epithelial cell behavior.

Combining Immune-Related Genes For Delineating the Extracellular Matrix and Predicting Hormone Therapy and Neoadjuvant Chemotherapy Benefits In Breast Cancer

Breast cancer (BC) is the most prevalent cancer in women worldwide. A systematic approach to BC treatment, comprising adjuvant and neoadjuvant chemotherapy (NAC), as well as hormone therapy, forms the foundation of the disease’s therapeutic strategy. The extracellular matrix (ECM) is a dynamic network that exerts a robust biological effect on the tumor microenvironment (TME), and it is highly regulated by several immunological components, such as chemokines and cytokines. It has been established that the ECM promotes the development of an immunosuppressive TME. Therefore, while analyzing the ECM of BC, immune-related genes must be considered. In this study, we used bioinformatic approaches to identify the most valuable ECM-related immune genes. We used weighted gene co-expression network analysis to identify the immune-related genes that potentially regulate the ECM and then combined them with the original ECM-related gene set for further analysis. Least absolute shrinkage and selection operator (LASSO) regression and SurvivalRandomForest were used to narrow our ECM-related gene list and establish an ECM index (ECMI) to better delineate the ECM signature. We stratified BC patients into ECMI high and low groups and evaluated their clinical, biological, and genomic characteristics. We found that the ECMI is highly correlated with long-term BC survival. In terms of the biological process, this index is positively associated with the cell cycle, DNA damage repair, and homologous recombination but negatively with processes involved in angiogenesis and epithelial–mesenchymal transition. Furthermore, the tumor mutational burden, copy number variation, and DNA methylation levels were found to be related to the ECMI. In the Metabric cohort, we demonstrated that hormone therapy is more effective in patients with a low ECMI. Additionally, differentially expressed genes from the ECM-related gene list were extracted from patients with a pathologic complete response (pCR) to NAC and with residual disease (RD) to construct a neural network model for predicting the chance of achieving pCR individually. Finally, we performed qRT-PCR to validate our findings and demonstrate the important role of the gene OGN in predicting the pCR rate. In conclusion, delineation of the ECM signature with immune-related genes is anticipated to aid in the prediction of the prognosis of patients with BC and the benefits of hormone therapy and NAC in BC patients.

The Analysis of Potential Diagnostic and Therapeutic Targets for the Occurrence and Development of Gastric Cancer Based on Bioinformatics

Objective

Gastric cancer is among the most common malignant tumors of the digestive system. This study explored the molecular mechanisms and potential therapeutic targets for gastric cancer occurrence and progression using bioinformatics.

Methods

The gastric cancer microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database. The R package was used for data mining and screening differentially expressed genes (DEGs). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Based on the protein-protein interaction (PPI) network analysis, core targets and core subsets were screened. Then, the relationship between the expression level of the core genes and the prognosis of gastric cancer patients was analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) database.

Results

Using the GSE19826 and GSE54129 datasets, a total of 550 DEGs were identified, including 248 upregulated and 302 downregulated genes. GO and KEGG analyses showed that the upregulated DEGs were mainly enriched in the extracellular matrix (ECM) organization of the biological process (BP), the collagen-containing ECM of cellular component (CC), and the ECM structural constituent of molecular function (MF). DEGs were also enriched in human papillomavirus infections, the focal adhesion pathway, PI3K-Akt signaling pathway, and among others. The downregulated DEGs were mainly enriched in digestion, basal part of the cell, and aldo-keto reductase (NADP) activity. And the above pathways were enriched primarily in the metabolism of xenobiotics by cytochrome P450, drug metabolism-cytochrome P450, and retinol metabolism. Five core genes, including COL1A2, COL3A1, BGN, FN1, and VCAN, were significantly highly expressed in gastric cancer patients and were associated with poor prognosis.

Conclusion

This study identified new potential molecular targets closely related to gastric cancer occurrence and development via mining public data using bioinformatics analysis methods.

Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.