Immune-Related Genes Are Prognostic Markers for Prostate Cancer Recurrence

Predicting prostate cancer recurrence: Introducing PCRPS, an advanced online web server

Background

Prostate cancer (PCa) is one of the leading causes of cancer death in men. About 30% of PCa will develop a biochemical recurrence (BCR) following initial treatment, which significantly contributes to prostate cancer-related deaths. In clinical practice, accurate prediction of PCa recurrence is crucial for making informed treatment decisions. However, the development of reliable models and biomarkers for predicting PCa recurrence remains a challenge. In this study, the aim is to establish an effective and reliable tool for predicting the recurrence of PCa.

Methods

We systematically screened and analyzed potential datasets to predict PCa recurrence. Through quality control analysis, low-quality datasets were removed. Using meta-analysis, differential expression analysis, and feature selection, we identified key genes associated with recurrence. We also evaluated 22 previously published signatures for PCa recurrence prediction. To assess prediction performance, we employed nine machine learning algorithms. We compared the predictive capabilities of models constructed using clinical variables, expression data, and their combinations. Subsequently, we implemented these machine learning models into a user-friendly web server freely accessible to all researchers.

Results

Based on transcriptomic data derived from eight multicenter studies consisting of 733 PCa patients, we screened 23 highly influential genes for predicting prostate cancer recurrence. These genes were used to construct the Prostate Cancer Recurrence Prediction Signature (PCRPS). By comparing with 22 published signatures and four important clinicopathological features, the PCRPS exhibited a robust and significantly improved predictive capability. Among the tested algorithms, Random Forest demonstrated the highest AUC value of 0.72 in predicting PCa recurrence in the testing dataset. To facilitate access and usage of these machine learning models by all researchers and clinicians, we also developed an online web server (https://urology1926.shinyapps.io/PCRPS/) where the PCRPS model can be freely utilized. The tool can also be used to (1) predict the PCa recurrence by clinical information or expression data with high accuracy. (2) provide the possibility of PCa recurrence by nine machine learning algorithms. Furthermore, using the PCRPS scores, we predicted the sensitivity of 22 drugs from GDSC2 and 95 drugs from CTRP2 to the samples. These predictions provide valuable insights into potential drug sensitivities related to the PCRPS score groups.

Conclusion

Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment for PCa.

Overcoming chemoresistance and radio resistance in prostate cancer: The emergent role of non-coding RNAs

Prostate cancer (PCa) continues to be a major health concern worldwide, with its resistance to chemotherapy and radiation therapy presenting major hurdles in successful treatment. While patients with localized prostate cancer generally have a good survival rate, those with metastatic prostate cancer often face a grim prognosis, even with aggressive treatments using various methods. The high mortality rate in severe cases is largely due to the lack of treatment options that can offer lasting results, especially considering the significant genetic diversity found in tumors at the genomic level. This comprehensive review examines the intricate molecular mechanisms governing resistance in PCa, emphasising the pivotal contributions of non-coding RNAs (ncRNAs). We delve into the diverse roles of microRNAs, long ncRNAs, and other non-coding elements as critical regulators of key cellular processes involved in CR & RR. The review emphasizes the diagnostic potential of ncRNAs as predictive biomarkers for treatment response, offering insights into patient stratification and personalized therapeutic approaches. Additionally, we explore the therapeutic implications of targeting ncRNAs to overcome CR & RR, highlighting innovative strategies to restore treatment sensitivity. By synthesizing current knowledge, this review not only provides a comprehension of the chemical basis of resistance in PCa but also identifies gaps in knowledge, paving the way for future research directions. Ultimately, this exploration of ncRNA perspectives offers a roadmap for advancing precision medicine in PCa, potentially transforming therapeutic paradigms and improving outcomes for patients facing the challenges of treatment resistance.

E2F3 renders an immunosuppressive tumor microenvironment in nasopharyngeal carcinoma: Involvements of the transcription activation of PRC1 and BIRC5

BACKGROUND

E2F transcription factors are well-recognized oncogenic molecules, and their correlation with immune cell infiltration has recently been reported. This work studies the impacts and mechanism of E2F transcription factor 3 (E2F3) in the growth and tumor microenvironment (TME) of nasopharyngeal carcinoma (NPC).

METHODS

Aberrantly expressed transcription factors in NPC were screened by abundant bioinformatics analyses. Gene expression in NPC cells was analyzed by reverse transcription-quantitative polymerase chain reaction and Western blot analyses. Malignant behaviors of NPC cells were analyzed by cell counting kit-8, 5-ethynyl-2'-deoxyuridine labeling, Transwell assays, and xenograft tumor models. TPA-induced THP-1 cells (macrophages) were cultured in the conditioned medium of NPC cells to mimic tumor-associated macrophages (TAMs) in vivo, and these TAMs were cocultured with CD8+ T cells. Regulation of E2F3 on protein regulator of cytokinesis 1 (PRC1) and baculoviral IAP repeat containing 5 (BIRC5) was validated by chromatin immunoprecipitation and luciferase reporter assays.

RESULTS

E2F3 was highly expressed in NPC cells, and its knockdown suppressed malignant behavior and tumorigenic ability of the cells. The E2F3 knockdown condition downregulated M2 cytokines CD163 and interleukin-10 in TAMs, which further enhanced proliferation and activation of the cocultured CD8+ T cells. E2F3 promoted transcription of PRC1 and BRIC5. Furthermore, PRC1 or BRIC5 upregulation in NPC cells restored the malignant properties of NPC cells, reprogrammed the TAMs to M2 phenotype, and suppressed the CD8+ T cell proliferation and activation.

CONCLUSION

This work suggests that E2F3 renders an immunosuppressive TME in NPC by activating PRC1 and BIRC5. Suppression of any member involved might favor tumor elimination.

Identification, and Experimental and Bioinformatics Validation of an Immune-Related Prognosis Gene Signature for Low-Grade Glioma Based on mRNAsi

BACKGROUND

Low-grade gliomas (LGGs), which are the second most common intracranial tumor, are diagnosed in seven out of one million people, tending to develop in younger people. Tumor stem cells and immune cells are important in the development of tumorigenesis. However, research on prognostic factors linked to the immune microenvironment and stem cells in LGG patients is limited. We critically need accurate related tools for assessing the risk of LGG patients.

METHODS

In this study, we aimed to identify immune-related genes (IRGs) in LGG based on the mRNAsi score. We employed differentially expressed gene (DEG) methods and weighted correlation network analysis (WGCNA). The risk signature was then further established using a lasso Cox regression analysis and a multivariate Cox analysis. Next, we used immunohistochemical sections (HPA) and a survival analysis to identify the hub genes. A nomogram was built to assess the prognosis of patients based on their clinical information and risk scores and was validated using a DCA curve, among other methods.

RESULTS

Four hub genes were obtained: C3AR1 (HR = 0.98, p < 0.001), MSR1 (HR = 1.02, p < 0.001), SLC11A1 (HR = 1.01, p < 0.01), and IL-10 (HR = 1.01, p < 0.001). For LGG patients, we created an immune-related prognostic signature (IPS) based on mRNAsi for estimating risk scores; different risk groups showed significantly different survival rates (p = 3.3 × 10^-16). Then, via an evaluation of the IRG-related signature, we created a nomogram for predicting LGG survival probability.

CONCLUSION

The outcome suggests that, when predicting the prognosis of LGG patients, our nomogram was more effective than the IPS. In this study, four immune-related predictive biomarkers for LGG were identified and proven to be IRGs. Therefore, the development of efficient immunotherapy techniques can be facilitated by the creation of the IPS.

Accurate prognosis for localized prostate cancer through coherent voting networks with multi-omic and clinical data

Localized prostate cancer is a very heterogeneous disease, from both a clinical and a biological/biochemical point of view, which makes the task of producing stratifications of patients into risk classes remarkably challenging. In particular, it is important an early detection and discrimination of the indolent forms of the disease, from the aggressive ones, requiring post-surgery closer surveillance and timely treatment decisions. This work extends a recently developed supervised machine learning (ML) technique, called coherent voting networks (CVN) by incorporating a novel model-selection technique to counter the danger of model overfitting. For the challenging problem of discriminating between indolent and aggressive types of localized prostate cancer, accurate prognostic prediction of post-surgery progression-free survival with a granularity within a year is attained, improving accuracy with respect to the current state of the art. The development of novel ML techniques tailored to the problem of combining multi-omics and clinical prognostic biomarkers is a promising new line of attack for sharpening the capability to diversify and personalize cancer patient treatments. The proposed approach allows a finer post-surgery stratification of patients within the clinical high-risk category, with a potential impact on the surveillance regime and the timing of treatment decisions, complementing existing prognostic methods.

Oscillatory shear stress promotes vein graft intimal hyperplasia via NADPH oxidase-related pathways

Background

Uncontrolled intimal hyperplasia (IH) after autologous saphenous vein grafting triggers a high restenosis rate; however, its association with the activation of NADPH oxidase (NOX)-related pathways is unclear. Here, we investigated the effects and mechanism of oscillatory shear stress (OSS) on grafted vein IH.

Methods

Thirty male New Zealand rabbits were randomly divided into control, high-OSS (HOSS), and low-OSS (LOSS) groups, and the vein grafts were harvested after 4 weeks. Hematoxylin and eosin staining and Masson staining assays were used to observe morphological and structural changes. Immunohistochemical staining was used to detect α-SMA, PCNA, MMP-2, and MMP-9 expression. Immunofluorescence staining was used to observe reactive oxygen species (ROS) production in the tissues. Western blotting was used to determine the expression levels of pathway-related proteins (NOX1, NOX2, AKT, p-AKT, and BIRC5), PCNA, BCL-2, BAX, and caspase-3/cleaved caspase-3 in tissues.

Results

Blood flow velocity was lower in the LOSS group than in the HOSS group, while vessel diameter did not change significantly. Shear rate was elevated in both HOSS and LOSS groups but was higher in the HOSS group. Additionally, vessel diameter increased with time in the HOSS and LOSS groups, whereas flow velocity did not. Intimal hyperplasia was significantly lower in the LOSS group than in the HOSS group. IH was dominated by smooth muscle fibers in the grafted veins and collagen fibers in the media. OSS restriction significantly reduced the α-SMA, PCNA, MMP-2, and MMP-9 levels. Moreover, ROS production and the expression of NOX1, NOX2, p-AKT, BIRC5, PCNA, BCL-2, BAX, and cleaved caspase-3 were phase-reduced in LOSS compared to the levels in the HOSS group. Total AKT was not differentially expressed among the three groups.

Conclusion

OSS promotes the proliferation, migration, and survival of subendothelial vascular smooth muscle cells in grafted veins, which may be related to the regulation of downstream p-AKT/BIRC5 levels through the increased production of ROS by NOX. Drugs inhibiting this pathway might be used to prolong vein graft survival time.

A lncRNA-immune checkpoint-related gene signature predicts metastasis-free survival in prostate adenocarcinoma

Background

The 5-year overall survival rate in metastatic prostate adenocarcinoma (PRAD) is extremely low. Genomic studies of PRAD have improved our understanding of disease biology. However, the role of immune checkpoint genes (ICGs) in PRAD remains unclear.

Methods

Univariate and multivariate analyses were used to analyze genes associated with metastasis-free survival (MFS) in The Cancer Genome Atlas (TCGA)-PRAD dataset. The expressions of ADORA2A and TNFRSF18 were detected via immunohistochemical assay and real-time fluorescence quantitative PCR (RT-PCR) assay in our in-house cohort. The expression of long non-coding RNAs (lncRNAs) AL139287.1, SLC9A3-AS1, and SNHG12 were detected via RT-PCR assay in our in-house cohort. Stepwise regression, Cox regression, and nomogram analyses were used to evaluate the prognostic role of these genes in both the TCGA dataset and in-house cohort. The "pRRophetic" R package was used to evaluate drug sensitivity in the TCGA cohort according to the gene mRNA expression level.

Results

In our study, univariate and multivariate analyses revealed that the mRNA expressions of two ICGs, ADORA2A and TNFRSF18, were independent factors affecting MFS in PRAD patients. A prognostic 2-ICG model predicted the MFS of PRAD patients with medium-to-high accuracy in the TCGA dataset and in-house cohort. The expressions of AL139287.1, SLC9A3-AS1, and SNHG12 were correlated with ADORA2A and TNFRSF18. A prognostic lncRNA-ICG model predicted the MFS of PRAD patients with medium-to-high accuracy in the TCGA dataset and in-house cohort. In addition, correlation analyses between the sensitivity of doxorubicin, erlotinib, gemcitabine, or vinorelbine and AL139287.1, SLC9A3-AS1, SNHG12, ADORA2A, and TNFRSF18 were conducted.

Conclusions

Our results provide new targets for predicting tumor metastasis in PRAD and treating patients with metastatic PRAD.

Identification of an Immune-Related Prognostic Signature for Glioblastoma by Comprehensive Bioinformatics and Experimental Analyses

Background: Glioblastoma (GBM), which has a poor prognosis, accounts for 31% of all cancers in the brain and central nervous system. There is a paucity of research on prognostic indicators associated with the tumor immune microenvironment in GBM patients. Accurate tools for risk assessment of GBM patients are urgently needed. Methods: In this study, we used weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) methods to screen out GBM-related genes among immune-related genes (IRGs). Then, we used survival analysis and Cox regression analysis to identify prognostic genes among the GBM-related genes to further establish a risk signature, which was validated using methods including ROC analysis, stratification analysis, protein expression level validation (HPA), gene expression level validation based on public cohorts, and RT-qPCR. In order to provide clinicians with a useful tool to predict survival, a nomogram based on an assessment of IRGs and clinicopathological features was constructed and further validated using DCA, time-dependent ROC curve, etc. Results: Three immune-related genes were found: PPP4C (p < 0.001, HR = 0.514), C5AR1 (p < 0.001, HR = 1.215), and IL-10 (p < 0.001, HR = 1.047). An immune-related prognostic signature (IPS) was built to calculate risk scores for GBM patients; patients classified into different risk groups had significant differences in survival (p = 0.006). Then, we constructed a nomogram based on an assessment of the IRG-based signature, which was validated as a potential prediction tool for GBM survival rates, showing greater accuracy than the nomogram without the IPS when predicting 1-year (0.35 < Pt < 0.50), 3-year (0.65 < Pt < 0.80), and 5-year (0.65 < Pt < 0.80) survival. Conclusions: In conclusion, we integrated bioinformatics and experimental approaches to construct an IPS and a nomogram based on IPS for predicting GBM prognosis. The signature showed strong potential for prognostic prediction and could help in developing more precise diagnostic approaches and treatments for GBM.

Integrated identification of key immune related genes and patterns of immune infiltration in calcified aortic valvular disease: A network based meta-analysis

Background: As the most prevalent valvular heart disease, calcific aortic valve disease (CAVD) has become a primary cause of aortic valve stenosis and insufficiency. We aim to illustrate the roles of immune related genes (IRGs) and immune cells infiltration in the occurrence of CAVD.

Methods: Integrative meta-analysis of expression data (INMEX) was adopted to incorporate multiple gene expression datasets of CAVD from Gene Expression Omnibus (GEO) database. By matching the differentially expressed genes (DEGs) to IRGs from “ImmPort” database, differentially expressed immune related genes (DEIRGs) were screened out. We performed enrichment analysis and found that DEIRGs in CAVD were closely related to inflammatory response and immune cells infiltration. We also constructed protein–protein interaction (PPI) network of DEIRGs and identified 5 key DEIRGs in CAVD according to the mixed character calculation results. Moreover, CIBERSORT algorithm was used to explore the profile of infiltrating immune cells in CAVD. Based on Spearman’s rank correlation method, correlation analysis between key DEIRGs and infiltrating immune cells was performed.

Results: A total of 220 DEIRGs were identified and the enrichment analysis of DEIRGs showed that they were significantly enriched in inflammatory responses. PPI network was constructed and PTPN11, GRB2, SYK, PTPN6 and SHC1 were identified as key DEIRGs. Compared with normal aortic valve tissue samples, the proportion of neutrophils, T cells CD4 memory activated and macrophages M0 was elevated in calcified aortic valves tissue samples, as well as reduced infiltration of macrophages M2 and NK cells activated. Furthermore, key DEIRGs identified in the present study, including PTPN11, GRB2, PTPN6, SYK, and SHC1, were all significantly correlated with infiltration of various immune cells.

Conclusion: This meta-analysis suggested that PTPN11, GRB2, PTPN6, SYK, and SHC1 might be key DEIRGs associated with immune cells infiltration, which play a pivotal role in pathogenesis of CAVD.