Cartilage oligomeric matrix protein acts as a molecular biomarker in multiple cancer types

Osteoarthritis as a Systemic Disease Promoted Prostate Cancer In Vivo and In Vitro

Osteoarthritis (OA) is increasing worldwide, and previous work found that OA increases systemic cartilage oligomeric matrix protein (COMP), which has also been implicated in prostate cancer (PCa). As such, we sought to investigate whether OA augments PCa progression. Cellular proliferation and migration of RM1 murine PCa cells treated with interleukin (IL)-1α, COMP, IL-1α + COMP, or conditioned media from cartilage explants treated with IL-1α (representing OA media) and with inhibitors of COMP were assessed. A validated murine model was used for tumor growth and marker expression analysis. Both proliferation and migration were greater in PCa cells treated with OA media compared to controls (p < 0.001), which was not seen with direct application of the stimulants. Migration and proliferation were not negatively affected when OA media was mixed with downstream and COMP inhibitors compared to controls (p > 0.05 for all). Mice with OA developed tumors 100% of the time, whereas mice without OA only 83.4% (p = 0.478). Tumor weight correlated with OA severity (Pearson correlation = 0.813, p = 0.002). Moreover, tumors from mice with OA demonstrated increased Ki-67 expression compared to controls (mean 24.56% vs. 6.91%, p = 0.004) but no difference in CD31, PSMA, or COMP expression (p > 0.05). OA appears to promote prostate cancer in vitro and in vivo.

Serum proteomic profiling of precancerous gastric lesions and early gastric cancer reveals signatures associated with systemic inflammatory response and metaplastic differentiation

The noninvasive detection technique using serum for large-scale screening is useful for the early diagnosis of gastric cancer (GC). Herein, we employed liquid chromatography mass spectrometry to determine the serum proteome signatures and related pathways in individuals with gastric precancerous (pre-GC) lesions and GC and explore the effect of Helicobacter pylori (H. pylori) infection. Differentially expressed proteins in GC and pre-GC compared with non-atrophic gastritis (NAG) group were identified. APOA4, a protein associated with metaplastic differentiation, and COMP, an extracellular matrix protein, were increased in the serum of patients with pre-GC lesions and GC. In addition, several inflammation-associated proteins, such as component C3, were decreased in the GC and pre-GC groups, which highlight a tendency for the inflammatory response to converge at the gastric lesion site during the GC cascade. Moreover, the abundance of proteins associated with oxidant detoxification was higher in the GC group compared with that in the NAG group, and these proteins were also increased in the serum of the H. pylori-positive GC group compared with that in the H. pylori-negative GC patients, reflecting the importance of oxidative stress pathways in H. pylori infection. Collectively, the findings of this study highlight pathways that play important roles in GC progression, and may provide potential diagnostic biomarkers for the detection of pre-GC lesions.

Thrombospondins in the tumor microenvironment

Many cancers begin with the formation of a small nest of transformed cells that can remain dormant for years. Thrombospondin-1 (TSP-1) initially promotes dormancy by suppressing angiogenesis, a key early step in tumor progression. Over time, increases in drivers of angiogenesis predominate, and vascular cells, immune cells, and fibroblasts are recruited to the tumor mass forming a complex tissue, designated the tumor microenvironment. Numerous factors, including growth factors, chemokine/cytokine, and extracellular matrix, participate in the desmoplastic response that in many ways mimics wound healing. Vascular and lymphatic endothelial cells, and cancer-associated pericytes, fibroblasts, macrophages and immune cells are recruited to the tumor microenvironment, where multiple members of the TSP gene family promote their proliferation, migration and invasion. The TSPs also affect the immune signature of tumor tissue and the phenotype of tumor-associated macrophages. Consistent with these observations, expression of some TSPs has been established to correlate with poor outcomes in specific types of cancer.

The Potential of Extracellular Matrix- and Integrin Adhesion Complex-Related Molecules for Prostate Cancer Biomarker Discovery

Prostate cancer is among the top five cancer types according to incidence and mortality. One of the main obstacles in prostate cancer management is the inability to foresee its course, which ranges from slow growth throughout years that requires minimum or no intervention to highly aggressive disease that spreads quickly and resists treatment. Therefore, it is not surprising that numerous studies have attempted to find biomarkers of prostate cancer occurrence, risk stratification, therapy response, and patient outcome. However, only a few prostate cancer biomarkers are used in clinics, which shows how difficult it is to find a novel biomarker. Cell adhesion to the extracellular matrix (ECM) through integrins is among the essential processes that govern its fate. Upon activation and ligation, integrins form multi-protein intracellular structures called integrin adhesion complexes (IACs). In this review article, the focus is put on the biomarker potential of the ECM- and IAC-related molecules stemming from both body fluids and prostate cancer tissue. The processes that they are involved in, such as tumor stiffening, bone turnover, and communication via exosomes, and their biomarker potential are also reviewed.

C1QA and COMP: plasma-based biomarkers for early diagnosis of pancreatic neuroendocrine tumors

Pancreatic Neuroendocrine tumors (PanNET) are challenging to diagnose and often detected at advanced stages due to a lack of specific and sensitive biomarkers. This study utilized proteomics as a valuable approach for cancer biomarker discovery; therefore, mass spectrometry-based proteomic profiling was conducted on plasma samples from 12 subjects (3 controls; 5 Grade I, 4 Grade II PanNET patients) to identify potential proteins capable of effectively distinguishing PanNET from healthy controls. Data are available via ProteomeXchange with the identifier PXD045045. 13.2% of proteins were uniquely identified in PanNET, while 60% were commonly expressed in PanNET and controls. 17 proteins exhibiting significant differential expression between PanNET and controls were identified with downstream analysis. Further, 5 proteins (C1QA, COMP, HSP90B1, ITGA2B, and FN1) were selected by pathway analysis and were validated using Western blot analysis. Significant downregulation of C1QA (p = 0.001: within groups, 0.03: control vs. grade I, 0.0013: grade I vs. grade II) and COMP (p = 0.011: within groups, 0.019: control vs grade I) were observed in PanNET Grade I & II than in controls. Subsequently, ELISA on 38 samples revealed significant downregulation of C1QA and COMP with increasing disease severity. This study shows the potential of C1QA and COMP in the early detection of PanNET, highlighting their role in the search for early-stage (Grade-I and Grade-II) diagnostic markers and therapeutic targets for PanNET.

Identification and validation of a dysregulated TME-related gene signature for predicting prognosis, and immunological properties in bladder cancer

Background

During tumor growth, tumor cells interact with their tumor microenvironment (TME) resulting in the development of heterogeneous tumors that promote tumor occurrence and progression. Recently, there has been extensive attention on TME as a possible therapeutic target for cancers. However, an accurate TME-related prediction model is urgently needed to aid in the assessment of patients' prognoses and therapeutic value, and to assist in clinical decision-making. As such, this study aimed to develop and validate a new prognostic model based on TME-associated genes for BC patients.

Methods

Transcriptome data and clinical information for BC patients were extracted from The Cancer Genome Atlas (TCGA) database. Gene Expression Omnibus (GEO) and IMvigor210 databases, along with the MSigDB, were utilized to identify genes associated with TMEs (TMRGs). A consensus clustering approach was used to identify molecular clusters associated with TMEs. LASSO Cox regression analysis was conducted to establish a prognostic TMRG-related signature, with verifications being successfully conducted internally and externally. Gene ontology (GO), KEGG, and single-sample gene set enrichment analyses (ssGSEA) were performed to investigate the underlying mechanisms. The potential response to ICB therapy was estimated using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and Immunophenoscore (IPS). Additionally, it was found that the expression level of certain genes in the model was significantly correlated with objective responses to anti-PD-1 or anti-PD-L1 treatment in the IMvigor210, GSE111636, GSE176307, or Truce01 (registration number NCT04730219) cohorts. Finally, real-time PCR validation was performed on 10 paired tissue samples, and in vitro cytological experiments were also conducted on BC cell lines.

Results

In BC patients, 133 genes differentially expressed that were associated with prognosis in TME. Consensus clustering analysis revealed three distinct clinicopathological characteristics and survival outcomes. A novel prognostic model based on nine TMRGs (including C3orf62, DPYSL2, GZMA, SERPINB3, RHCG, PTPRR, STMN3, TMPRSS4, COMP) was identified, and a TMEscore for OS prediction was constructed, with its reliable predictive performance in BC patients being validated. MultiCox analysis showed that the risk score was an independent prognostic factor. A nomogram was developed to facilitate the clinical viability of TMEscore. Based on GO and KEGG enrichment analyses, biological processes related to ECM and collagen binding were significantly enriched among high-risk individuals. In addition, the low-risk group, characterized by a higher number of infiltrating CD8+ T cells and a lower burden of tumor mutations, demonstrated a longer survival time. Our study also found that TMEscore correlated with drug susceptibility, immune cell infiltration, and the prediction of immunotherapy efficacy. Lastly, we identified SERPINB3 as significantly promoting BC cells migration and invasion through differential expression validation and in vitro phenotypic experiments.

Conclusion

Our study developed a prognostic model based on nine TMRGs that accurately and stably predicted survival, guiding individual treatment for patients with BC, and providing new therapeutic strategies for the disease.

Identification of the circRNA-miRNA-mRNA Regulatory Network in Pterygium-Associated Conjunctival Epithelium

To investigate the regulatory mechanism of pterygium formation, we detected differentially expressed messenger RNAs (DE-mRNAs) and differentially expressed circular RNAs (DE-circRNAs) in pterygium-associated conjunctival epithelium (PCE) and normal conjunctival epithelium (NCE). Genome-wide mRNA and circRNA expression profiles of PCE and NCE were determined using high-throughput sequencing. Bioinformatics analyses, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, gene set enrichment analysis (GSEA), and protein-protein interaction (PPI) analysis, were conducted. The microRNAs (miRNAs) interacting with the hub DE-mRNAs and DE-circRNAs were predicted and verified using real-time quantitative PCR (RT-qPCR). The data showed that there were 536 DE-mRNAs (280 upregulated and 256 downregulated mRNAs) and 78 DE-circRNAs (20 upregulated and 58 downregulated circRNAs) in PCE. KEGG enrichment analysis indicated that the DE-mRNAs were mainly involved in the following biological processes: IL-17 signalling pathway, viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, ECM-receptor interaction, and focal adhesion. The GSEA results revealed that the epithelial mesenchymal transition (EMT) process was significantly enriched in upregulated mRNAs. The pterygium-associated circRNA-miRNA-mRNA network was established based on the top 10 DE-circRNAs, 4 validated miRNAs (upregulated miR-376a-5p and miR-208a-5p,downregulated miR-203a-3p and miR-200b-3p), and 31 DE-mRNAs. We found that miR-200b-3p, as a regulator of FN1, SDC2, and MEX3D, could be regulated by 5 upregulated circRNAs. In addition, we screened out EMT-related DE-mRNAs, including 6 upregulated DE-mRNAs and 6 downregulated DE-mRNAs. The EMT-related circRNA-miRNA-mRNA network was established with the top 10 circRNAs, 8 validated miRNAs (upregulated miR-17-5p, miR-181a-5p, and miR-106a-5p, downregulated miR-124-3p, miR-9-5p, miR-130b-5p, miR-1-3p, and miR-26b-5P), and 12 EMT-related DE-mRNAs. We found that hsa_circ_0002406 might upregulate FN1 and ADAM12 by sponging miR-26b-5p and miR-1-3p, respectively, thus promoting EMT in pterygium. Briefly, the study provides a novel viewpoint on the molecular pathological mechanisms in pterygium formation. CircRNA-miRNA-mRNA regulatory networks participate in the pathogenesis of pterygium and might become promising targets for pterygium prevention and treatment.