Immune-related biomarker risk score predicts prognosis in prostate cancer

Develop prediction model to help forecast advanced prostate cancer patients' prognosis after surgery using neural network

Background

The effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet.

Methods

We investigate the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients' overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model's performance.

Results

6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software.

Conclusion

Patients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making.

Explore the impact of hypoxia-related genes (HRGs) in Cutaneous melanoma

BACKGROUND

Cutaneous melanoma (CM) has an overall poor prognosis due to a high rate of metastasis. This study aimed to explore the role of hypoxia-related genes (HRGs) in CM.

METHODS

We first used on-negative matrix factorization consensus clustering (NMF) to cluster CM samples and preliminarily analyzed the relationship of HRGs to CM prognosis and immune cell infiltration. Subsequently, we identified prognostic-related hub genes by univariate COX regression analysis and the least absolute shrinkage and selection operator (LASSO) and constructed a prognostic model. Finally, we calculated a risk score for patients with CM and investigated the relationship between the risk score and potential surrogate markers of response to immune checkpoint inhibitors (ICIs), such as TMB, IPS values, and TIDE scores.

RESULTS

Through NMF clustering, we identified high expression of HRGs as a risk factor for the prognosis of CM patients, and at the same time, increased expression of HRGs also indicated a poorer immune microenvironment. Subsequently, we identified eight gene signatures (FBP1, NDRG1, GPI, IER3, B4GALNT2, BGN, PKP1, and EDN2) by LASSO regression analysis and constructed a prognostic model.

CONCLUSION

Our study identifies the prognostic significance of hypoxia-related genes in melanoma and shows a novel eight-gene signature to predict the potential efficacy of ICIs.

A Novel L-Phenylalanine Dipeptide Inhibits the Growth and Metastasis of Prostate Cancer Cells via Targeting DUSP1 and TNFSF9

Prostate cancer (PCa) is a common malignant cancer of the urinary system. Drug therapy, chemotherapy, and radical prostatectomy are the primary treatment methods, but drug resistance and postoperative recurrence often occur. Therefore, seeking novel anti-tumor compounds with high efficiency and low toxicity from natural products can produce a new tumor treatment method. Matijin-Su [N-(N-benzoyl-L-phenylalanyl)-O-acetyl-L-phenylalanol, MTS] is a phenylalanine dipeptide monomer compound that is isolated from the Chinese ethnic medicine Matijin (Dichondra repens Forst.). Its derivatives exhibit various pharmacological activities, especially anti-tumor. Among them, the novel MTS derivative HXL131 has a significant inhibitory effect against prostate tumor growth and metastasis. This study is designed to investigate the effects of HXL131 on the growth and metastasis of human PCa cell lines PC3 and its molecular mechanism through in vitro experiments combined with proteomics, molecular docking, and gene silencing. The in vitro results showed that HXL131 concentration dependently inhibited PC3 cell proliferation, induced apoptosis, arrested cell cycle at the G2/M phase, and inhibited cell migration capacity. A proteomic analysis and a Western blot showed that HXL131 up-regulated the expression of proliferation, apoptosis, cell cycle, and migration-related proteins CYR61, TIMP1, SOD2, IL6, SERPINE2, DUSP1, TNFSF9, OSMR, TNFRSF10D, and TNFRSF12A. Molecular docking, a cellular thermal shift assay (CETSA), and gene silencing showed that HXL131 had a strong binding affinity with DUSP1 and TNFSF9, which are important target genes for inhibiting the growth and metastasis of PC3 cells. This study demonstrates that HXL131 exhibited excellent anti-prostate cancer activity and inhibited the growth and metastasis of prostate cancer cells by regulating the expression of DUSP1 and TNFSF9.

Pathology of Urologic Cancers

We are pleased to present this Special Issue of Cancers, entitled “Pathology of Urologic Cancers” [...]

A Novel Gene Signature Associated With “E2F Target” Pathway for Predicting the Prognosis of Prostate Cancer

Background: The effect of the adenoviral early region 2 binding factors (E2Fs) target pathway on prostate cancer is not clear. It is necessary to establish an E2F target-related gene signature to predict prognosis and facilitate clinical decision-making.

Methods: An E2F target-related gene signature was established by univariate and LASSO Cox regression analyses, and its predictive ability was verified in multiple cohorts. Moreover, the enrichment pathway, immune microenvironment, and drug sensitivity of the activated E2F target pathway were also explored.

Results: The E2F target-related gene signature consisted of MXD3, PLK1, EPHA10, and KIF4A. The patients with high-risk scores showed poor prognosis, therapeutic resistance, and immunosuppression, along with abnormal growth characteristics of cells. Tinib drugs showed high sensitivity to the expression of MXD3 and EPHA10 genes.

Conclusion: Our research established an E2F target-related signature for predicting the prognosis of prostate cancer. This study provides insights into formulating individualized detection and treatment as well as provides a theoretical basis for future research.

Inflammation and Prostate Cancer: A Multidisciplinary Approach to Identifying Opportunities for Treatment and Prevention

Prostate cancer is a major cause of disease for men globally. Inflammation, an established hallmark of cancer, is frequently observed in the prostate, though its contribution to prostate cancer risks and outcomes is not fully understood. Prostate cancer is biologically and clinically heterogeneous, and there is now evidence that inflammation and immunological characteristics vary by the genomic and mutational landscape of the tumor. Moreover, it is now recognized that risk factor profiles vary between tumor subgroups, as defined by histopathological and molecular features. Here, we provide a review centered around the relationship between inflammation and prostate cancer, with a consideration of molecular tumor features and a particular focus on the advanced and lethal stages of disease. We summarize findings from epidemiological studies of the etiology and role of inflammation in prostate cancer. We discuss the pathology of prostate inflammation, and consider approaches for assessing the tumor immune microenvironment in epidemiological studies. We review emerging clinical therapies targeting immune biology within the context of prostate cancer. Finally, we consider potentially modifiable risk factors and corresponding lifestyle interventions that may affect prostate inflammation, impacting outcomes. These emerging insights will provide some hints for the development of treatment and prevention strategies for advanced and lethal prostate cancer.

Construction and Comprehensive Prognostic Analysis of a Novel Immune-Related lncRNA Signature and Immune Landscape in Gastric Cancer

Gastric cancer (GC) is a malignant tumor with high mortality and poor prognosis. Immunotherapies, especially immune checkpoint inhibitors (ICI), are widely used in various tumors, but patients with GC do not benefit much from immunotherapies. Therefore, effective predictive biomarkers are urgently needed for GC patients to realize the benefits of immunotherapy. Recent studies have indicated that long noncoding RNAs (lncRNAs) could be used as biomarkers in the immune landscape of multiple tumors. In this study, we constructed a novel immune-related lncRNA (irlncRNA) risk model to predict the survival and immune landscape of GC patients. First, we identified differentially expressed irlncRNAs (DEirlncRNAs) from RNA-Seq data of The Cancer Genome Atlas (TCGA). By using various algorithms, we constructed a risk model with 11 DEirlncRNA pairs. We then tested the accuracy of the risk model, demonstrating that the risk model has good efficiency in predicting the prognosis of GC patients. Inner validation sets were further used to confirm the effectiveness of the risk model. In addition, our risk model has a preferable performance in predicting the immune infiltration status of tumors, immune checkpoint status of the patients, and immunotherapy score. In conclusion, our risk model may provide insights into the prognosis of and immunotherapy strategy for GC.

Screening to Identify an Immune Landscape-Based Prognostic Predictor and Therapeutic Target for Prostate Cancer

Objectives

Existing prognostic risk assessment strategies for prostate cancer (PCa) remain unsatisfactory. Similar treatments for patients at the same disease stage can lead to different survival outcomes. Thus, we aimed to explore a novel immune landscape-based prognostic predictor and therapeutic target for PCa patients.

Methods

A total of 490 PCa patients from The Cancer Genome Atlas Project (TCGA) cohort were analyzed to obtain immune landscape-based prognostic features. Then, analyses at different levels were performed to explore the relevant survival mechanisms, prognostic predictors, and therapeutic targets. Finally, experimental verification was performed using a tissue microarray (TMA) from 310 PCa patients. Furthermore, a nomogram was constructed to provide a quantitative approach for predicting the prognosis of patients with PCa.

Results

The immune landscape-based risk score (ILBRS) was obtained. Then, VAV1, which presented a significant positive correlation with Treg infiltration and ILBRS, was screened and identified to be significantly related to the prognosis of PCa. Finally, experimental verification confirmed the prognostic value of VAV1 for PCa prognosis at the protein level.

Conclusions

VAV1 has the potential to be developed as an immune landscape-based PCa prognostic predictor and therapeutic target and will help improve prognosis by enabling the selection of individualized, targeted therapy.

Immune-Related Genes Are Prognostic Markers for Prostate Cancer Recurrence

Background

Prostate cancer (PCa) is an immune-responsive disease. The current study sought to explore a robust immune-related prognostic gene signature for PCa.

Methods

Data were retrieved from the tumor Genome Atlas (TCGA) database and GSE46602 database for performing the least absolute shrinkage and selection operator (LASSO) cox regression model analysis. Immune related genes (IRGs) data were retrieved from ImmPort database.

Results

The weighted gene co-expression network analysis (WGCNA) showed that nine functional modules are correlated with the biochemical recurrence of PCa, including 259 IRGs. Univariate regression analysis and survival analysis identified 35 IRGs correlated with the prognosis of PCa. LASSO Cox regression model analysis was used to construct a risk prognosis model comprising 18 IRGs. Multivariate regression analysis showed that risk score was an independent predictor of the prognosis of PCa. A nomogram comprising a combination of this model and other clinical features showed good prediction accuracy in predicting the prognosis of PCa. Further analysis showed that different risk groups harbored different gene mutations, differential transcriptome expression and different immune infiltration levels. Patients in the high-risk group exhibited more gene mutations compared with those in the low-risk group. Patients in the high-risk groups showed high-frequency mutations in TP53. Immune infiltration analysis showed that M2 macrophages were significantly enriched in the high-risk group implying that it affected prognosis of PCa patients. In addition, immunostimulatory genes were differentially expressed in the high-risk group compared with the low-risk group. BIRC5, as an immune-related gene in the prediction model, was up-regulated in 87.5% of prostate cancer tissues. Knockdown of BIRC5 can inhibit cell proliferation and migration.

Conclusion

In summary, a risk prognosis model based on IGRs was developed. A nomogram comprising a combination of this model and other clinical features showed good accuracy in predicting the prognosis of PCa. This model provides a basis for personalized treatment of PCa and can help clinicians in making effective treatment decisions.

Lemur Tyrosine Kinases and Prostate Cancer: A Literature Review

The members of the Lemur Tyrosine Kinases (LMTK1-3) subfamily constitute a group of three membrane-anchored kinases. They are known to influence a wide variety of key cellular events, often affecting cell proliferation and apoptosis. They have been discovered to be involved in cancer, in that they impact various signalling pathways that influence cell proliferation, migration, and invasiveness. Notably, in the context of genome-wide association studies, one member of the LMTK family has been identified as a candidate gene which could contribute to the development of prostate cancer. In this review, of published literature, we present evidence on the role of LMTKs in human prostate cancer and model systems, focusing on the complex network of interacting partners involved in signalling cascades that are frequently activated in prostate cancer malignancy. We speculate that the modulators of LMTK enzyme expression and activity would be of high clinical relevance for the design of innovative prostate cancer treatment.

Blood-Derived Biomarkers of Diagnosis, Prognosis and Therapy Response in Prostate Cancer Patients

Prostate cancer is among the most frequent cancers in men worldwide. Despite the fact that multiple therapeutic alternatives are available for its treatment, it is often discovered in an advanced stage as a metastatic disease. Prostate cancer screening is based on physical examination of prostate size and prostate-specific antigen (PSA) level in the blood as well as biopsy in suspect cases. However, these markers often fail to correctly identify the presence of cancer, or their positivity might lead to overdiagnosis and consequent overtreatment of an otherwise silent non-progressing disease. Moreover, these markers have very limited if any predictive value regarding therapy response or individual risk for therapy-related toxicities. Therefore, novel, optimally liquid biopsy-based (blood-derived) markers or marker panels are needed, which have better prognostic and predictive value than the ones currently used in the everyday routine. In this review the role of circulating tumour cells, extracellular vesicles and their microRNA content, as well as cellular and soluble immunological and inflammation- related blood markers for prostate cancer diagnosis, prognosis and prediction of therapy response is discussed. A special emphasis is placed on markers predicting response to radiotherapy and radiotherapy-related late side effects.