Discovery of CASP8 as a potential biomarker for high-risk prostate cancer through a high-multiplex immunoassay

Mining bone metastasis related key genes of prostate cancer from the STING pathway based on machine learning

Background

Prostate cancer (PCa) is the second most prevalent malignant tumor in male, and bone metastasis occurs in about 70% of patients with advanced disease. The STING pathway, an innate immune signaling mechanism, has been shown to play a key role in tumorigenesis, metastasis, and cancerous bone pain. Hence, exploring regulatory mechanism of STING in PCa bone metastasis will bring novel opportunities for treating PCa bone metastasis.

Methods

First, key genes were screened from STING-related genes (SRGs) based on random forest algorithm and their predictive performance was evaluated. Subsequently, a comprehensive analysis of key genes was performed to explore their roles in prostate carcinogenesis, metastasis and tumor immunity. Next, cellular experiments were performed to verify the role of RELA in proliferation and migration in PCa cells, meanwhile, based on immunohistochemistry, we verified the difference of RELA expression between PCa primary foci and bone metastasis. Finally, based on the key genes to construct an accurate and reliable nomogram, and mined targeting drugs of key genes.

Results

In this study, three key genes for bone metastasis were mined from SRGs based on the random forest algorithm. Evaluation analysis showed that the key genes had excellent prediction performance, and it also showed that the key genes played a key role in carcinogenesis, metastasis and tumor immunity in PCa by comprehensive analysis. In addition, cellular experiments and immunohistochemistry confirmed that overexpression of RELA significantly inhibited the proliferation and migration of PCa cells, and RELA was significantly low-expression in bone metastasis. Finally, the constructed nomogram showed excellent predictive performance in Receiver Operating Characteristic (ROC, AUC = 0.99) curve, calibration curve, and Decision Curve Analysis (DCA) curve; and the targeted drugs showed good molecular docking effects.

Conclusion

In sum, this study not only provides a new theoretical basis for the mechanism of PCa bone metastasis, but also provides novel therapeutic targets and novel diagnostic tools for advanced PCa treatment.

The impact of circulating protein levels identified by affinity proteomics on short-term, overall breast cancer risk

OBJECTIVE

Current breast cancer risk prediction scores and algorithms can potentially be further improved by including molecular markers. To this end, we studied the association of circulating plasma proteins using Proximity Extension Assay (PEA) with incident breast cancer risk.

SUBJECTS

In this study, we included 1577 women participating in the prospective KARMA mammographic screening cohort.

RESULTS

In a targeted panel of 164 proteins, we found 8 candidates nominally significantly associated with short-term breast cancer risk (P < 0.05). Similarly, in an exploratory panel consisting of 2204 proteins, 115 were found nominally significantly associated (P < 0.05). However, none of the identified protein levels remained significant after adjustment for multiple testing. This lack of statistically significant findings was not due to limited power, but attributable to the small effect sizes observed even for nominally significant proteins. Similarly, adding plasma protein levels to established risk factors did not improve breast cancer risk prediction accuracy.

CONCLUSIONS

Our results indicate that the levels of the studied plasma proteins captured by the PEA method are unlikely to offer additional benefits for risk prediction of short-term overall breast cancer risk but could provide interesting insights into the biological basis of breast cancer in the future.

Study of Therapeutic Mechanisms of Bupi Yichang Formula against Colon Cancer Based on Network Pharmacology, Machine Learning, and Experimental Verification

Bupi Yichang formula (BPYCF) has shown the anti-cancer potential; however, its effects on colon cancer and the mechanisms remain unknown. This study intended to explore the effects of BPYC on colon cancer and its underlying mechanisms. BPYCF-related and colon cancer-related targets were acquired from public databases, followed by differentially expressed genes (DEG) identification. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using clusterProfiler. A protein-protein interaction (PPI) network was constructed using STRING database. CytoHubba and MCODE to screen the hub targets. A diagnostic model was built using random forest algorithm. Molecular docking was conducted using PyMOL and AutoDock. High-performance liquid chromatograph-mass spectrometry (HPLC-MS) analysis and in vitro validation were performed. Forty-six overlapping targets of BPYCF-related, colon cancer-related targets, and DEGs were obtained. GO and KEGG analyses showed that the targets were mainly enriched in response to lipopolysaccharide, neuronal cell body, protein serine/threonine/tyrosine, as well as C-type lectin receptor, NOD-like receptor, and TNF signaling pathways. Five targets were identified as the pivotal targets, among which, NOS3, CASP8, RIPK3, and TNFRSF10B were stably docked with the core active component, naringenin. Naringenin was also identified from the BPYCF sample through HPLC-MS analysis. In vitro experiments showed that BPYCF inhibited cell viability, reduced NOS3 expression, and elevated CASP8, RIPK3, and TNFRSF10B expression in colon cancer cells. BPYCF might treat colon cancer mainly by regulating NOS3, CASP8, RIPK3, and TN-FRSF10B. This study first revealed the therapeutic effects and mechanisms of BPYCF against colon cancer, paving the path for the development of targeted therapeutic strategies for this cancer in the clinic.

Plasma protein changes reflect colorectal cancer development and associated inflammation

Introduction

Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of death worldwide. Efficient non-invasive blood-based biomarkers for CRC early detection and prognosis are urgently needed.

Methods

To identify novel potential plasma biomarkers, we applied a proximity extension assay (PEA), an antibody-based proteomics strategy to quantify the abundance of plasma proteins in CRC development and cancer-associated inflammation from few μL of plasma sample.

Results

Among the 690 quantified proteins, levels of 202 plasma proteins were significantly changed in CRC patients compared to age-and-sex-matched healthy subjects. We identified novel protein changes involved in Th17 activity, oncogenic pathways, and cancer-related inflammation with potential implications in the CRC diagnosis. Moreover, the interferon γ (IFNG), interleukin (IL) 32, and IL17C were identified as associated with the early stages of CRC, whereas lysophosphatidic acid phosphatase type 6 (ACP6), Fms-related tyrosine kinase 4 (FLT4), and MANSC domain-containing protein 1 (MANSC1) were correlated with the late-stages of CRC.

Discussion

Further study to characterize the newly identified plasma protein changes from larger cohorts will facilitate the identification of potential novel diagnostic, prognostic biomarkers for CRC.

Expression analysis of hsa_circ_0020397, hsa_circ_0005986, hsa_circ_0003028, and hsa_circ_0006990 in renal cell carcinoma

Renal cell carcinoma (RCC) is a prevalent heterogeneous kidney cancer. So far, different genes have been reported for RCC development. However, its particular molecular mechanism remains unclear. Circular RNAs (circRNAs), a class of non-coding RNAs, are involved in numerous biological processes in different malignancies such as RCC. This study aims to assess the expression and underlying mechanism of four circRNAs (hsa_circ_0020397, hsa_circ_0005986, hsa_circ_0003028, hsa_circ_0006990) with possible new roles in RCC. In the experimental step, we investigated the expression of these four circRNAs in our RCC samples using quantitative real-time polymerase chain reaction. In the bioinformatics step, the differential expressed mRNAs (DEmRNAs), and miRNAs (DEmiRNAs) were obtained from the GEO datasets using the GEO2R tool. A protein-protein interaction network was constructed using the STRING database, and hub genes were identified by Cytoscape. Molecular pathways associated with hub genes were detected using KEGG pathway enrichment analysis. Then, we utilized the ToppGene database to detect the relationships between DEmiRNAs and hub genes. Furthermore, interactions between circRNAs and DEmiRNAs were predicted by the StarBase and circinteractome databases. Finally, a circRNA-DEmiRNA-hub gene triple network was constructed. Our results revealed that the expression of hsa_circ_0020397, hsa_circ_0005986, and hsa_circ_0006990 was downregulated in RCC tissues. Moreover, these circRNAs had a significantly lower expression in patients with a history of kidney disease. Furthermore, hsa_circ_0003028 and hsa_circ_0006990 showed higher expression in the tumor of participants with Lymphovascular/perineural invasion and oncocytoma type, respectively. Based on bioinformatic results, 15 circRNA-DEmiRNA-hub gene ceRNA regulatory axes were predicted, which included three hub genes, five miRNAs, and four selected circRNAs. In conclusion, the current work is the first to emphasize the expression of the hsa_circ_0020397, hsa_circ_0005986, hsa_circ_0003028, and hsa_circ_0006990 in RCC patients presents a novel perspective on the molecular processes underlying the pathogenic mechanisms of RCC.

Follow-Up Biomarkers in the Evolution of Prostate Cancer, Levels of S100A4 as a Detector in Plasma

The management and screening of prostate cancer (PC) is still the main problem in clinical practice. In this study, we investigated the role of aggressiveness genetic markers for PC stratification. We analyzed 201 plasma samples from PC patients and controls by digital PCR. For selection and validation, 26 formalin-fixed paraffin-embedded tissues, 12 fresh tissues, and 24 plasma samples were characterized by RNA-Seq, immunochemistry, immunofluorescence, Western blot, and extracellular-vesicles analyses. We identified three novel non-invasive biomarkers; all with an increased expression pattern in patients (PCA3: p = 0.002, S100A4: p ≤ 0.0001 and MRC2: p = 0.005). S100A4 presents the most informative AUC (area under the curve) (0.735). Combination of S100A4, MRC2, and PCA3 increases the discriminatory power between patients and controls and between different more and less aggressive stages (AUC = 0.761, p ≤ 0.0001). However, although a sensitivity of 97.47% in PCA3 and a specificity of 90.32% in S100A4 was reached, the detection signal level could be variable in some analyses owing to tumor heterogeneity. This is the first time that the role of S100A4 and MRC2 has been described in PC aggressiveness. Moreover, the combination of S100A4, MRC2, and PCA3 has never been described as a non-invasive biomarker for PC screening and aggressiveness.

The cGAS-STING pathway-related gene signature can predict patient prognosis and immunotherapy responses in prostate adenocarcinoma

The cyclic GMP-AMP synthase-stimulator of the interferon genes (cGAS-STING) pathway is essential in inflammation-driven tumor occurrence and progression. However, the prognostic roles and immune functions of cGAS-STING pathway-related genes in patients with prostate adenocarcinoma (PRAD) remain unclear. cGAS-STING pathway-related genes were obtained from the gene set enrichment analysis (GSEA) website. Univariate Cox regression analysis was performed to screen the prognosis-related hub genes in the cancer genome atlas (TCGA) and GSE116918 datasets. Unsupervised clustering analysis was performed to identify different clusters. The least absolute shrinkage and selection operator and multivariate Cox regression analyses were applied to develop a prognostic risk model. The prognostic values and predictive performance of risk signature were assessed by the Kaplan-Meier curve and receiver operating characteristic curve. The IMvigor210 cohort was used to investigate the potential values of the risk score in immunotherapeutic responses. Two clusters were identified based on the expression matrix of 12 prognosis-related genes. Specifically, better overall survival was observed in cluster 2 than cluster 1 in both datasets. Inflammation-related pathway enrichment and immune cell infiltration levels were altered between 2 clusters. Moreover, 6 genes (CASP8, GRK6, IL3RA, PLCB1, TBKBP1, and TNFSF10) were identified to generate a cGAS-STING pathway-related signature (CPRS). Survival analysis showed that patients in the high-risk group showed a more dismal survival than those in the low-risk group in TCGA and GSE116918 datasets. Notably, the CPRS can differentiate responsive patients from non-responsive individuals treated with PD-L1 blockades in an independent cohort. In addition, higher CPRS was associated with a more favorable prognosis. The proposed risk model was developed based on 6 cGAS-STING pathway related-genes, which can be used as a promising predictor for patient survival and immunotherapeutic responses in PRAD, contributing to treatment strategy-related decision-making.

Prognostic Impact of Caspase-8, CDK9 and Phospho-CDK9 (Thr 186) Expression in Patients with Uterine Cervical Cancer Treated with Definitive Chemoradiation and Brachytherapy

Introduction: After primary platinum-based chemoradiation of locally advanced uterine cervical cancer, a substantial proportion of women present with persistent, recurrent or metastatic disease, indicating an unmet need for biomarker development. Methods: We evaluated the clinical records of 69 cervical cancer patients (Federation of Gynecology and Obstetrics, FIGO Stage > IB3) who were subjected to definitive CRT. Immunohistochemical scoring of caspase-8, cyclin dependent kinase 9 (CDK9) and phosphorylated (phospho-)CDK9 (threonine (Thr) 186) was performed on pretreatment samples and correlated with the histopathological and clinical endpoints, including relapse-free survival (RFS), distant metastasis-free survival (DMFS), cancer-specific survival (CSS) and overall survival (OS). Results: Lower levels of caspase-8 were more prevalent in patients with a higher T-stage (p = 0.002) and a higher FIGO stage (p = 0.003), and were significantly correlated with CDK9 expression (p = 0.018) and inversely with pCDK9 detection (p = 0.014). Increased caspase-8 levels corresponded to improved RFS (p = 0.005), DMFS (p = 0.038) and CSS (p = 0.017) in the univariate analyses. Low CDK9 expression was associated with worse RFS (p = 0.008), CSS (p = 0.015) and OS (p = 0.007), but not DMFS (p = 0.083), and remained a significant prognosticator for RFS (p = 0.003) and CSS (p = 0.009) in the multivariate analyses. Furthermore, low pCDK9 staining was significantly associated with superior RFS (p = 0.004) and DMFS (p = 0.001), and increased CSS (p = 0.022), and remained significant for these endpoints in the multivariate analyses. Conclusion: Increased caspase-8 and CDK9 levels correlate with improved disease-related outcomes in cervical cancer patients treated with CRT, whereas elevated pCDK9 levels predict worse survival in this patient population.

Cuprotosis Programmed-Cell-Death-Related lncRNA Signature Predicts Prognosis and Immune Landscape in PAAD Patients

In terms of mortality and survival, pancreatic cancer is one of the worst malignancies. Known as a unique type of programmed cell death, cuprotosis contributes to tumor cell growth, angiogenesis, and metastasis. Cuprotosis programmed-cell-death-related lncRNAs (CRLs) have been linked to PAAD, although their functions in the tumor microenvironment and prognosis are not well understood. This study included data from the TCGA-PAAD cohort. Random sampling of PAAD data was conducted, splitting the data into two groups for use as a training set and test set (7:3). We searched for differentially expressed genes that were substantially linked to prognosis using univariate Cox and Lasso regression analysis. Through the use of multivariate Cox proportional risk regression, a risk-rating system for prognosis was developed. Correlations between the CRL signature and clinicopathological characteristics, tumor microenvironment, immunotherapy response, and chemotherapy sensitivity were further evaluated. Lastly, qRT-PCR was used to compare CRL expression in healthy tissues to that in tumors. Some CRLs are thought to have strong correlations with PAAD outcomes. These CRLs include AC005332.6, LINC02041, LINC00857, and AL117382.1. The CRL-based signature construction exhibited outstanding predictive performance and offers a fresh approach to evaluating pre-immune effectiveness, paving the way for future studies in precision immuno-oncology.

Phosphorylated Proteins from Serum: A Promising Potential Diagnostic Biomarker of Cancer

Cancer is a fatal disease worldwide. Each year ten million people are diagnosed around the world, and more than half of patients eventually die from it in many countries. A majority of cancer remains asymptomatic in the earlier stages, with specific symptoms appearing in the advanced stages when the chances of adequate treatment are low. Cancer screening is generally executed by different imaging techniques like ultrasonography (USG), mammography, CT-scan, and magnetic resonance imaging (MRI). Imaging techniques, however, fail to distinguish between cancerous and non-cancerous cells for early diagnosis. To confirm the imaging result, solid and liquid biopsies are done which have certain limitations such as invasive (in case of solid biopsy) or missed early diagnosis due to extremely low concentrations of circulating tumor DNA (in case of liquid biopsy). Therefore, it is essential to detect certain biomarkers by a noninvasive approach. One approach is a proteomic or glycoproteomic study which mostly identifies proteins and glycoproteins present in tissues and serum. Some of these studies are approved by the Food and Drug Administration (FDA). Another non-expensive and comparatively easier method to detect glycoprotein biomarkers is by ELISA, which uses lectins of diverse specificities. Several of the FDA approved proteins used as cancer biomarkers do not show optimal sensitivities for precise diagnosis of the diseases. In this regard, expression of phosphoproteins is associated with a more specific stage of a particular disease with high sensitivity and specificity. In this review, we discuss the expression of different serum phosphoproteins in various cancers. These phosphoproteins are detected either by phosphoprotein enrichment by immunoprecipitation using phosphospecific antibody and metal oxide affinity chromatography followed by LC-MS/MS or by 2D gel electrophoresis followed by MALDI-ToF/MS analysis. The updated knowledge on phosphorylated proteins in clinical samples from various cancer patients would help to develop these serum phophoproteins as potential diagnostic/prognostic biomarkers of cancer.

Defining novel causal SNPs and linked phenotypes at melanoma-associated loci

A number of genomic regions have been associated with melanoma risk through genome-wide association studies; however, the causal variants underlying the majority of these associations remain unknown. Here, we sequenced either the full locus or the functional regions including exons of 19 melanoma-associated loci in 1959 British melanoma cases and 737 controls. Variant filtering followed by Fisher's exact test analyses identified 66 variants associated with melanoma risk. Sequential conditional logistic regression identified the distinct haplotypes on which variants reside, and massively parallel reporter assays provided biological insights into how these variants influence gene function. We performed further analyses to link variants to melanoma risk phenotypes and assessed their association with melanoma-specific survival. Our analyses replicate previously known associations in the melanocortin 1 receptor (MC1R) and tyrosinase (TYR) loci, while identifying novel potentially causal variants at the MTAP/CDKN2A and CASP8 loci. These results improve our understanding of the architecture of melanoma risk and outcome.

Comparing Urinary Glycoproteins among Three Urogenital Cancers and Identifying Prostate Cancer-Specific Glycoproteins

Prostate cancer, bladder cancer, and renal cancers are major urogenital cancers. Of which, prostate cancer is the most commonly diagnosed and second leading cause of cancer death for men in the United States. For urogenital cancers, urine is considered as proximate body fluid to the tumor site for developing non-invasiveness tests. However, the specific molecular signatures from different urogenital cancers are needed to relate changes in urine to various cancer detections. Herein, we utilized a previously published C4-Tip and C18/MAX-Tip workflow for enrichment of glycopeptides from urine samples and evaluated urinary glycopeptides for its cancer specificity. We analyzed 66 urine samples from bladder cancer (n = 27), prostate cancer (n = 4), clear cell renal cell carcinoma (ccRCC, n = 3), and benign plastic hyperplasia (BPH, n = 32) and then compared them with a previous publication that reported glycopeptides associated with aggressive prostate cancer (Gleason score ≥ 8). We further demonstrated the cancer specificity of the glycopeptides associated with aggressive prostate cancer. In this study, a total of 33 glycopeptides were identified to be specifically differentially expressed in prostate cancer compared to other urogenital cancer types as well as BPH urines. By cross-comparison with our previous urinary glycoproteomic dataset for aggressive prostate cancer, we reported a total of four glycopeptides from glycoproteins DSC2, MGAM, PIK3IP1, and CD55, commonly identified to be prostate cancer-specific. Together, these results deepen our understanding of the urinary glycoproteins associated with urogenital cancer types and expand our knowledge of the cancer specificity of urinary glycoproteins among urogenital cancer progression.

Metastasis Model to Test the Role of Notch Signaling in Prostate Cancer

Distant metastasis is the main cause of death in prostate cancer patients. Notch signaling plays an important role in driving prostate cancer aggressiveness and metastasis. In this chapter, we describe a protocol to measure prostate cancer metastatic colonization, incidences of metastasis, accurately quantify the burden of metastasis, and test the role of NOTCH1 receptor on prostate cancer metastatic colonization and homing to distant sites. The metastasis model presented here is established by intracardiac injection of control human prostate cancer cells and NOTCH1 downregulated cells. The cells are engineered to express both red fluorescent protein (RFP) and luciferase. In this model, whole body bioluminescence imaging, high-resolution, and quantitative fluorescence imaging are utilized for quantitative assessment of metastatic colonization and metastasis burden. Further, histopathology analyses of diverse metastatic organs are performed. This model is a powerful and versatile tool to investigate the mechanisms underlying the function of NOTCH receptors in metastatic colonization in prostate cancer.

On the Road to Accurate Protein Biomarkers in Prostate Cancer Diagnosis and Prognosis: Current Status and Future Advances

Prostate cancer (PC) is a leading cause of morbidity and mortality among men worldwide. Molecular biomarkers work in conjunction with existing clinicopathologic tools to help physicians decide who to biopsy, re-biopsy, treat, or re-treat. The past decade has witnessed the commercialization of multiple PC protein biomarkers with improved performance, remarkable progress in proteomic technologies for global discovery and targeted validation of novel protein biomarkers from clinical specimens, and the emergence of novel, promising PC protein biomarkers. In this review, we summarize these advances and discuss the challenges and potential solutions for identifying and validating clinically useful protein biomarkers in PC diagnosis and prognosis. The identification of multi-protein biomarkers with high sensitivity and specificity, as well as their integration with clinicopathologic parameters, imaging, and other molecular biomarkers, bodes well for optimal personalized management of PC patients.