Syndecan Family Gene and Protein Expression and Their Prognostic Values for Prostate Cancer

Exploring the Syndecan-Mediated Cellular Internalization of the SARS-CoV-2 Omicron Variant

SARS-CoV-2 variants evolve to rely more on heparan sulfate (HS) for viral attachment and subsequent infection. In our earlier work, we demonstrated that the Delta variant's spike protein binds more strongly to HS compared to WT SARS-CoV-2, leading to enhanced cell internalization via syndecans (SDCs), a family of transmembrane HS proteoglycans (HSPGs) facilitating the cellular entry of the original strain. Using our previously established ACE2- or SDC-overexpressing cellular models, we now compare the ACE2- and SDC-dependent cellular uptake of heat-inactivated WT SARS-CoV-2 with the Delta and Omicron variants. Internalization studies with inactivated virus particles showed that ACE2 overexpression could not compensate for the loss of HS in Omicron's internalization, suggesting that this variant primarily uses HSPGs to enter cells. Although SDCs increased the internalization of all three viruses, subtle differences could be detected between their SDC isoform preferences. The Delta variant particularly benefitted from SDC1, 2, and 4 overexpression for cellular entry, while SDC4 had the most prominent effect on Omicron internalization. The SDC4 knockdown (KD) in Calu-3 cells reduced the cellular uptake of all three viruses, but the inhibition was the most pronounced for Omicron. The polyanionic heparin also hindered the cellular internalization of all three viruses with a dominant inhibitory effect on Omicron. Omicron's predominant HSPG affinity, combined with its preference for the universally expressed SDC4, might account for its efficient transmission yet reduced pathogenicity.

Role of Syndecans in Ovarian Cancer: New Diagnostic and Prognostic Biomarkers and Potential Therapeutic Targets

Ovarian cancer (OC) is the eighth cancer both in prevalence and mortality in women and represents the deadliest female reproductive cancer. Due to generally vague symptoms, OC is frequently diagnosed only at a late and advanced stage, resulting in high mortality. The tumor extracellular matrix and cellular matrix receptors play a key role in the pathogenesis of tumor progression. Syndecans are a family of four transmembrane heparan sulfate proteoglycans (PG), including syndecan-1, -2, -3, and -4, which are dysregulated in a myriad of cancers, including OC. Many clinicopathological studies suggest that these proteins are promising diagnostic and prognostic biomarkers for OC. Furthermore, functions of the syndecan family in the regulation of cellular processes make it an interesting pharmacological target for anticancer therapies.

Novel Histopathological Biomarkers in Prostate Cancer: Implications and Perspectives

Prostate cancer (PCa) is the second most frequently diagnosed cancer in men. Despite the significant progress in cancer diagnosis and treatment over the last few years, the approach to disease detection and therapy still does not include histopathological biomarkers. The dissemination of PCa is strictly related to the creation of a premetastatic niche, which can be detected by altered levels of specific biomarkers. To date, the risk factors for biochemical recurrence include lymph node status, prostate-specific antigen (PSA), PSA density (PSAD), body mass index (BMI), pathological Gleason score, seminal vesicle invasion, extraprostatic extension, and intraductal carcinoma. In the future, biomarkers might represent another prognostic factor, as discussed in many studies. In this review, we focus on histopathological biomarkers (particularly CD169 macrophages, neuropilin-1, cofilin-1, interleukin-17, signal transducer and activator of transcription protein 3 (STAT3), LIM domain kinase 1 (LIMK1), CD15, AMACR, prostate-specific membrane antigen (PSMA), Appl1, Sortilin, Syndecan-1, and p63) and their potential application in decision making regarding the prognosis and treatment of PCa patients. We refer to studies that found a correlation between the levels of biomarkers and tumor characteristics as well as clinical outcomes. We also hypothesize about the potential use of histopathological markers as a target for novel immunotherapeutic drugs or targeted radionuclide therapy, which may be used as adjuvant therapy in the future.

Clustering and machine learning-based integration identify cancer associated fibroblasts genes’ signature in head and neck squamous cell carcinoma

Background: A hallmark signature of the tumor microenvironment in head and neck squamous cell carcinoma (HNSCC) is abundantly infiltration of cancer-associated fibroblasts (CAFs), which facilitate HNSCC progression. However, some clinical trials showed targeted CAFs ended in failure, even accelerated cancer progression. Therefore, comprehensive exploration of CAFs should solve the shortcoming and facilitate the CAFs targeted therapies for HNSCC.

Methods: In this study, we identified two CAFs gene expression patterns and performed the single‐sample gene set enrichment analysis (ssGSEA) to quantify the expression and construct score system. We used multi-methods to reveal the potential mechanisms of CAFs carcinogenesis progression. Finally, we integrated 10 machine learning algorithms and 107 algorithm combinations to construct most accurate and stable risk model. The machine learning algorithms contained random survival forest (RSF), elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalised boosted regression modelling (GBM), and survival support vector machine (survival-SVM).

Results: There are two clusters present with distinct CAFs genes pattern. Compared to the low CafS group, the high CafS group was associated with significant immunosuppression, poor prognosis, and increased prospect of HPV negative. Patients with high CafS also underwent the abundant enrichment of carcinogenic signaling pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation. The MDK and NAMPT ligand–receptor cellular crosstalk between the cancer associated fibroblasts and other cell clusters may mechanistically cause immune escape. Moreover, the random survival forest prognostic model that was developed from 107 machine learning algorithm combinations could most accurately classify HNSCC patients.

Conclusion: We revealed that CAFs would cause the activation of some carcinogenesis pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation and revealed unique possibilities to target glycolysis pathways to enhance CAFs targeted therapy. We developed an unprecedentedly stable and powerful risk score for assessing the prognosis. Our study contributes to the understanding of the CAFs microenvironment complexity in patients with head and neck squamous cell carcinoma and serves as a basis for future in-depth CAFs gene clinical exploration.

Aberrant protein expression of Appl1, Sortilin and Syndecan-1 during the biological progression of prostate cancer

Diagnosis and assessment of patients with prostate cancer is dependent on accurate interpretation and grading of histopathology. However, morphology does not necessarily reflect the complex biological changes occurring in prostate cancer disease progression, and current biomarkers have demonstrated limited clinical utility in patient assessment. This study aimed to develop biomarkers that accurately define prostate cancer biology by distinguishing specific pathological features that enable reliable interpretation of pathology for accurate Gleason grading of patients. Online gene expression databases were interrogated and a pathogenic pathway for prostate cancer was identified. The protein expression of key genes in the pathway, including adaptor protein containing a pleckstrin homology (PH) domain, phosphotyrosine-binding (PTB) domain, and leucine zipper motif 1 (Appl1), Sortilin and Syndecan-1, was examined by immunohistochemistry (IHC) in a pilot study of 29 patients with prostate cancer, using monoclonal antibodies designed against unique epitopes. Appl1, Sortilin, and Syndecan-1 expression was first assessed in a tissue microarray cohort of 112 patient samples, demonstrating that the monoclonal antibodies clearly illustrate gland morphologies. To determine the impact of a novel IHC-assisted interpretation (the utility of Appl1, Sortilin, and Syndecan-1 labelling as a panel) of Gleason grading, versus standard haematoxylin and eosin (H&E) Gleason grade assignment, a radical prostatectomy sample cohort comprising 114 patients was assessed. In comparison to H&E, the utility of the biomarker panel reduced subjectivity in interpretation of prostate cancer tissue morphology and improved the reliability of pathology assessment, resulting in Gleason grade redistribution for 41% of patient samples. Importantly, for equivocal IHC-assisted labelling and H&E staining results, the cancer morphology interpretation could be more accurately applied upon re-review of the H&E tissue sections. This study addresses a key issue in the field of prostate cancer pathology by presenting a novel combination of three biomarkers and has the potential to transform clinical pathology practice by standardising the interpretation of the tissue morphology.

Identification of subtypes of hepatocellular carcinoma and screening of prognostic molecular diagnostic markers based on cell adhesion molecule related genes

Cell adhesion molecules can predict liver hepatocellular carcinoma (LIHC) metastasis and determine prognosis, while the mechanism of the role of cell adhesion molecules in LIHC needs to be further explored. LIHC-related expression data were sourced from The Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) databases, and genes related to cell adhesion were sourced from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. First, the TCGA-LIHC dataset was clustered by the nonnegative matrix factorization (NMF) algorithm to find different subtypes of LIHC. Then the difference of prognosis and immune microenvironment between patients of different subtypes was evaluated. In addition, a prognostic risk model was obtained by least shrinkage and selection operator (LASSO) and Cox analysis, while a nomogram was drawn. Furthermore, functional enrichment analysis between high and low risk groups was conducted. Finally, the expressions of model genes were explored by quantitative real-time polymerase chain reaction (qRT-PCR). The 371 LIHC patients were classified into four subtypes by NMF clustering, and survival analysis revealed that disease-free survival (DFS) of these four subtypes were clearly different. Cancer-related pathways and immune microenvironment among these four subtypes were dysregulated. Moreover, 58 common differentially expressed genes (DEGs) between four subtypes were identified and were mainly associated with PPAR signaling pathway and amino acid metabolism. Furthermore, a prognostic model consisting of IGSF11, CD8A, ALCAM, CLDN6, JAM2, ITGB7, SDC3, CNTNAP1, and MPZ was built. A nomogram consisting of pathologic T and riskScore was built, and the calibration curve illustrated that the nomogram could better forecast LIHC prognosis. Gene Set Enrichment Analysis (GSEA) demonstrated that DEGs between high and low risk groups were mainly involved in cell cycle. Finally, the qRT-PCR illustrated the expressions of nine model genes between normal and LIHC tissue. A prognostic model consisting of IGSF11, CD8A, ALCAM, CLDN6, JAM2, ITGB7, SDC3, CNTNAP1, and MPZ was obtained, which provides an important reference for the molecular diagnosis of patient prognosis.

Development of an Independent Prognostic Signature Based on Three Hypoxia-Related Genes for Breast Cancer

Background. Hypoxia was considered to be a prognostic indicator in a variety of solid tumors. This study aims at identifying the hypoxia-related genes (HRGs) in breast cancer (BC) and the feasibility of HRGs as a prognostic indicator. Methods. We downloaded the mRNA expression data of BC patients from TCGA and GEO databases. The LASSO Cox regression analysis was applied to screen the hub HRGs to establish a prognostic Risk Score. The independence of Risk Score was assessed by multivariate Cox regression analysis. And the immune checkpoint analysis was also performed. In addition, we also detected the expression level of hub HRGs in MCF-10A cells, MCF-7 cells, and SK-BR-3 cells by RT-qPCR. Results. Three HRGs were identified as hub genes with prognostic value in BC, including CA9, PGK1, and SDC1. The Risk Score constructed by these three genes could efficiently distinguish the prognosis of different BC patients and has been shown to be an independent prognostic indicator. In the high-risk group, patients had lower overall survival and poorer prognosis. In addition, the expression levels of five immune checkpoints (PD1, CTLA4, TIGIT, LAG3, and TIM3) in the high-risk group were significantly higher than those in the low-risk group. Moreover, the expression levels of PGK1 and SDC1 in BC cells were significantly increased. Conclusion. In this study, we established an efficiently model based on three optimal HRGs (CA9, PGK1, and SDC1) could clearly distinguish the prognosis of different BC patients.

High expression of syndecan-4 is related to clinicopathological features and poor prognosis of pancreatic adenocarcinoma

Objective

Pancreatic adenocarcinoma (PAAD) is a leading cause of cancer-related mortality in adults. Syndecan-4 (SDC4) is involved in cancer pathogenesis. Therefore, this study aimed to explore the expression and clinical significance of SDC4 in PAAD.

Methods

Differentially expressed genes (DEGs) between PAAD and normal pancreas were screened from the GTEx and TCGA databases, and the correlationship between the DEGs and prognosis were analyzed. The prognostic value of the screened SDC4, SERPINE1, and SLC2A1 was evaluated using the Kaplan–Meier curve and SDC4 was subsequently selected as the better candidate. Also, SDC4 expression was analyzed in PAAD tissues, the other risk factors affecting postoperative survival were analyzed using Cox regression analysis, and SDC4-mediated pathways enrichment was identified by GSVA and GSEA. SDC4 expression in PAAD tissues and adjacent normal tissues of selected PAAD patients was detected by RT-qPCR and immunohistochemistry. The correlation between SDC4 and clinical features was evaluated by the χ² test.

Results

SDC4 was highly expressed in PAAD tissues. Elevated SDC4 was correlated with reduced overall survival. SDC4 enrichment pathways included spliceosome function, proteasome activity, pentose phosphate pathway, base excision repair, mismatch repair, DNA replication, oxidative phosphorylation, mitotic spindle formation, epithelial-mesenchymal transition, and G2M checkpoints. SDC4 was elevated in PAAD tissues of PAAD patients compared with adjacent normal tissues. High SDC4 expression was related to metastatic differentiation, TNM stage, lymphatic metastasis, and lower 3-year survival rate. SDC4 was an independent risk factor affecting postoperative survival.

Conclusion

SDC4 was highly expressed in PAAD and was related to clinicopathological features and poor prognosis, which might be an important index for PAAD early diagnosis and prognosis.

Regulatory T-cells-related signature for identifying a prognostic subtype of hepatocellular carcinoma with an exhausted tumor microenvironment

Regulatory T-Cells (Tregs) are important in the progression of hepatocellular cancer (HCC). The goal of this work was to look into Tregs-related genes and develop a Tregs-related prognostic model. We used the weighted gene co-expression network analysis (WGCNA) to look for Tregs-related genes in the TCGA, ICGC, and GSE14520 cohorts and then used the non-negative matrix factorization (NMF) algorithm to find Tregs-related subpopulations. The LASSO-Cox regression approach was used to determine Tregs-related genes, which were then condensed into a risk score. A total of 153 overlapping genes among the three cohorts were considered Tregs-related genes. Based on these genes, two Tregs-associated clusters that varied in both prognostic and biological characteristics were identified. When compared with Cluster 1, Cluster 2 was a TME-exhausted HCC subpopulation with substantial immune cell infiltration but a poor prognosis. Five Tregs-related genes including HMOX1, MMP9, CTSC, SDC3, and TNFRSF11B were finally used to construct a prognostic model, which could accurately predict the prognosis of HCC patients in the three datasets. Patients in the high-risk scores group with bad survival outcomes were replete with immune/inflammatory responses, but exhausted T cells and elevated PD-1 and PD-L1 expression. The results of qRT-PCR and immunohistochemical staining (IHC) analysis in clinical tissue samples confirmed the above findings. Moreover, the signature also accurately predicted anti-PD-L1 antibody responses in the IMvigor210 dataset. Finally, HMOX1, MMP9, and TNFRSF11B were expressed differently in Hep3B and Huh7 cells after being treated with a PD1/PD-L1 inhibitor. In conclusion, our study uncovered a Tregs-related prognostic model that could identify TME- exhausted subpopulations and revealed that PD1/PD-L1 inhibitors could alter the expression levels of HMOX1, MMP9, and TNFRSF11B in Hep3B and Huh7 cells, which might help us better understand Tregs infiltration and develop personalized immunotherapy treatments for HCC patients.

Two novel predictive biomarkers for osteosarcoma and glycolysis pathways: A profiling study on HS2ST1 and SDC3

INTRODUCTION

Prognostic biomarkers for osteosarcoma (OS) are still very few, and this study aims to examine 2 novel prognostic biomarkers for OS through combined bioinformatics and experimental approach.

MATERIALS AND METHODS

Expression profile data of OS and paraneoplastic tissues were downloaded from several online databases, and prognostic genes were screened by differential expression analysis, Univariate Cox analysis, least absolute shrinkage and selection operator regression analysis, and multivariate Cox regression analysis to construct prognostic models. The accuracy of the model was validated using principal component analysis, constructing calibration plots, and column line plots. We also analyzed the relationship between genes and drug sensitivity. Gene expression profiles were analyzed by immunocytotyping. Also, protein expressions of the constructed biomarkers in OS and paraneoplastic tissues were verified by immunohistochemistry.

RESULTS

Heparan sulfate 2-O-sulfotransferase 1 (HS2ST1) and Syndecan 3 (SDC3, met all our requirements after screening. The constructed prognostic model indicated that patients in the high-risk group had a much lower patient survival rate than in the low-risk group. Moreover, these genes were closely related to immune cells (P < .05). Drug sensitivity analysis showed that the 2 genes modeled were strongly correlated with multiple drugs. Immunohistochemical analysis showed significantly higher protein expression of both genes in OS than in paraneoplastic tissues.

CONCLUSIONS

HS2ST1 and SDC3 are significantly dysregulated in OS, and the prognostic models constructed based on these 2 genes have much lower survival rates in the high-risk group than in the low-risk group. HS2ST1 and SDC3 can be used as glycolytic and immune-related prognostic biomarkers in OS.

Prostate Cancer Secretome and Membrane Proteome from Pten Conditional Knockout Mice Identify Potential Biomarkers for Disease Progression

Prostate cancer (PCa) is the second most common cause of mortality among men. Tumor secretome is a promising strategy for understanding the biology of tumor cells and providing markers for disease progression and patient outcomes. Here, transcriptomic-based secretome analysis was performed on the PCa tumor transcriptome of Genetically Engineered Mouse Model (GEMM) Pb-Cre4/Pten^f/f mice to identify potentially secreted and membrane proteins—PSPs and PMPs. We combined a selection of transcripts from the GSE 94574 dataset and a list of protein-coding genes of the secretome and membrane proteome datasets using the Human Protein Atlas Secretome. Notably, nine deregulated PMPs and PSPs were identified in PCa (DMPK, PLN, KCNQ5, KCNQ4, MYOC, WIF1, BMP7, F3, and MUC1). We verified the gene expression patterns of Differentially Expressed Genes (DEGs) in normal and tumoral human samples using the GEPIA tool. DMPK, KCNQ4, and WIF1 targets were downregulated in PCa samples and in the GSE dataset. A significant association between shorter survival and KCNQ4, PLN, WIF1, and F3 expression was detected in the MSKCC dataset. We further identified six validated miRNAs (mmu-miR-6962-3p, mmu-miR- 6989-3p, mmu-miR-6998-3p, mmu-miR-5627-5p, mmu-miR-15a-3p, and mmu-miR-6922-3p) interactions that target MYOC, KCNQ5, MUC1, and F3. We have characterized the PCa secretome and membrane proteome and have spotted new dysregulated target candidates in PCa.

Energy Metabolism-Related Gene Prognostic Index Predicts Biochemical Recurrence for Patients With Prostate Cancer Undergoing Radical Prostatectomy

Background

We aimed to construct and validate an energy metabolism-related gene prognostic index (EMRGPI) to predict biochemical recurrence (BCR) in patients undergoing radical prostatectomy.

Methods

We used Lasso and COX regression analysis to orchestrate the EMRGPI in the TCGA database, and the prognostic value of EMRGPI was further validated externally using the GSE46602. All analyses were conducted with R version 3.6.3 and its suitable packages.

Results

SDC1 and ADH1B were finally used to construct the risk formula. We classified the 430 tumor patients in the TCGA database into two groups, and patients in the high-risk group had a higher risk of BCR than those in the low-risk group (HR: 1.98, 95%CI: 1.18-3.32, p=0.01). Moreover, in the GSE46602, we confirmed that the BCR risk in the high-risk group was 3.86 times higher than that in the low-risk group (95%CI: 1.61-9.24, p=0.001). We found that patients in the high-risk group had significantly higher proportions of residual tumor, older age, and T stage. SDC1 and ADH1B were significantly expressed low in the normal tissues when compared to the tumor tissues, which were opposite at the protein level. The spearman analysis showed that EMRGPI was significantly associated with B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, dendritic cells, stromal score, immune score, and estimate score. In addition, the EMRGPI was positively associated with the 54 immune checkpoints, among which CD80, ADORA2A, CD160, and TNFRSF25 were significantly related to the BCR-free survival of PCa patients undergoing RP.

Conclusions

The EMRGPI established in this study might serve as an independent risk factor for PCa patients undergoing radical prostatectomy.

Mucinous metaplasia in Pten conditional knockout mice and mucin family genes as prognostic markers for prostate cancer

AIMS

This study evaluated the association of mucinous metaplasia (MM) with tumor cell proliferation, androgen receptor (AR) expression and invasiveness in Pten conditional knockout mice and the prognostic value of MM markers for patients with PCa.

MAIN METHODS

Prostatic lobes samples from genetic engineered mouse model Pten^f/f and Pb-Cre4/Pten^f/f were submitted for histopathological analysis and tissue expression of AR, the proliferation marker Ki67, alpha-smooth muscle actin, and laminin. RNAseq data of prostatic lobes samples were analyzed searching for MM gene expression patterns. We also investigated gene and protein expression related to MM in human PCa public databases.

KEY FINDINGS

All knockout animals analyzed showed at least one area of stroma-invading MM, which was absent in the control animals. The tumoral regions of MM showed a proliferative index 5 times higher than other tumoral areas and low expression of the AR (less than 20% of the cells were AR-positive). Disrupted basement membrane areas were observed in MM. The mouse and human PCa transcriptomes exhibited increased expression of the MM markers such as MUC1, MUC19, MUC4, MUC5AC, MUC5B, and TFF3. Gene expression profile was associated with castration-resistant prostate cancer (CRPC) and with a lower probability of freedom from biochemical recurrence.

SIGNIFICANCE

The expression of goblet cell genes, such as MUC1, MUC5AC, MUC5B, and TFF3 have significant prognostic value for PCa patients and represent another class of potential therapeutic targets.