A bibliometric and knowledge-map analysis of anoikis from 2003 to 2022.

This analysis covers 4494 anoikis-related publications (2003-2022). It explores annual trends, top countries, core journals, leading institutions, keywords, references, authors, and collaborations. Key findings include the United States leading in publications, Chulalongkorn University as the top institution, and Oncogene as the most prolific journal. The Journal of Biological Chemistry holds the highest influence. Burst keywords like "signal transduction," "apoptosis resistance," "metabolism," and "tumor microenvironment" highlight emerging research areas. This study offers a comprehensive overview, aiding researchers in grasping anoikis research trends, contributors, and prospects.

NCAPG2 promotes prostate cancer malignancy and stemness via STAT3/c-MYC signaling.

BACKGROUND: Prostate cancer (PCa) is the second leading cause of cancer-related mortality among men worldwide, and its incidence has risen substantially in recent years. Therefore, there is an urgent need to identify novel biomarkers and precise therapeutic targets for managing PCa progression and recurrence. METHODS: We investigated the clinical significance of NCAPG2 in PCa by exploring public datasets and our tissue microarray. Receiver operating characteristic (ROC) curve and survival analyses were performed to evaluate the correlation between NCAPG2 and PCa progression. Cell proliferation, wound healing, transwell, flow cytometry, cell cycle, tumor sphere formation, immunofluorescence (IF), co-immunoprecipitation (co-IP), and chromatin immunoprecipitation (ChIP) assays were conducted to further elucidate the molecular mechanism of NCAPG2 in PCa. Subcutaneous and orthotopic xenograft models were applied to investigate the effects of NCAPG2 on PCa proliferation in vivo. Tandem mass tag (TMT) quantitative proteomics was utilized to detect proteomic changes under NCAPG2 overexpression. RESULTS: NCAPG2 was significantly upregulated in PCa, and its overexpression was associated with PCa progression and unfavorable prognosis. Knockdown of NCAPG2 inhibited the malignant behavior of PCa cells, whereas its overexpression promoted PCa aggressiveness. NCAPG2 depletion attenuated the development and growth of PCa in vivo. TMT quantitative proteomics analyses indicated that c-MYC activity was strongly correlated with NCAPG2 expression. The malignancy-promoting effect of NCAPG2 in PCa was mediated via c-MYC. NCAPG2 could directly bind to STAT3 and induce STAT3 occupancy on the MYC promoter, thus to transcriptionally activate c-MYC expression. Finally, we identified that NCAPG2 was positively correlated with cancer stem cell (CSC) markers and enhanced self-renewal capacity of PCa cells. CONCLUSIONS: NCAPG2 is highly expressed in PCa, and its level is significantly associated with PCa prognosis. NCAPG2 promotes PCa malignancy and drives cancer stemness via the STAT3/c-MYC signaling axis, highlighting its potential as a therapeutic target for PCa.

Current Application of Navigation Systems in Robotic-Assisted and Laparoscopic Partial Nephrectomy: Focus on the Improvement of Surgical Performance and Outcomes.

Kidney cancer represents the third most prevalent malignancy among all types of genitourinary cancer worldwide. Currently, there is a growing trend of employing partial nephrectomy for the management of large and complex tumors. Surgical outcomes are associated with some amendable surgical factors, including warm ischemic time, pedicle clamping, preserved volume of renal parenchyma, appropriate surgical strategy, and precise resection of the tumor. Improving surgical performance is pivotal for achieving favorable surgical outcomes. Due to advancements in imaging visualization technology and the shift of the medical paradigm toward precision medicine, an increasing number of navigation systems have been implemented in partial nephrectomy procedures. The navigation system can assist surgeons in formulating optimal surgical strategies and enhance the safety, precision, and feasibility of resecting complex renal tumors. In this review, we provide an overview of currently available navigation systems and their feasible applications, with a focus on how they contribute to the improvement of surgical performance and outcomes during robotic-assisted and laparoscopic partial nephrectomy.

A quick and innovative pipeline for producing chondrocyte-homing peptide-modified extracellular vesicles by three-dimensional dynamic culture of hADSCs spheroids to modulate the fate of remaining ear chondrocytes in the M1 macrophage-infiltrated microenvironment.

BACKGROUND: Extracellular vesicles (EVs) derived from human adipose-derived mesenchymal stem cells (hADSCs) have shown great therapeutic potential in plastic and reconstructive surgery. However, the limited production and functional molecule loading of EVs hinder their clinical translation. Traditional two-dimensional culture of hADSCs results in stemness loss and cellular senescence, which is unfavorable for the production and functional molecule loading of EVs. Recent advances in regenerative medicine advocate for the use of three-dimensional culture of hADSCs to produce EVs, as it more accurately simulates their physiological state. Moreover, the successful application of EVs in tissue engineering relies on the targeted delivery of EVs to cells within biomaterial scaffolds. METHODS AND RESULTS: The hADSCs spheroids and hADSCs gelatin methacrylate (GelMA) microspheres are utilized to produce three-dimensional cultured EVs, corresponding to hADSCs spheroids-EVs and hADSCs microspheres-EVs respectively. hADSCs spheroids-EVs demonstrate excellent production and functional molecule loading compared with hADSCs microspheres-EVs. The upregulation of eight miRNAs (i.e. hsa-miR-486-5p, hsa-miR-423-5p, hsa-miR-92a-3p, hsa-miR-122-5p, hsa-miR-223-3p, hsa-miR-320a, hsa-miR-126-3p, and hsa-miR-25-3p) and the downregulation of hsa-miR-146b-5p within hADSCs spheroids-EVs show the potential of improving the fate of remaining ear chondrocytes and promoting cartilage formation probably through integrated regulatory mechanisms. Additionally, a quick and innovative pipeline is developed for isolating chondrocyte homing peptide-modified EVs (CHP-EVs) from three-dimensional dynamic cultures of hADSCs spheroids. CHP-EVs are produced by genetically fusing a CHP at the N-terminus of the exosomal surface protein LAMP2B. The CHP + LAMP2B-transfected hADSCs spheroids were cultured with wave motion to promote the secretion of CHP-EVs. A harvesting method is used to enable the time-dependent collection of CHP-EVs. The pipeline is easy to set up and quick to use for the isolation of CHP-EVs. Compared with nontagged EVs, CHP-EVs penetrate the biomaterial scaffolds and specifically deliver the therapeutic miRNAs to the remaining ear chondrocytes. Functionally, CHP-EVs show a major effect on promoting cell proliferation, reducing cell apoptosis and enhancing cartilage formation in remaining ear chondrocytes in the M1 macrophage-infiltrated microenvironment. CONCLUSIONS: In summary, an innovative pipeline is developed to obtain CHP-EVs from three-dimensional dynamic culture of hADSCs spheroids. This pipeline can be customized to increase EVs production and functional molecule loading, which meets the requirements for regulating remaining ear chondrocyte fate in the M1 macrophage-infiltrated microenvironment.

Identification of subgroups along the glycolysis-cholesterol synthesis axis and the development of an associated prognostic risk model.

BACKGROUND: Skin cutaneous melanoma (SKCM) is one of the most highly prevalent and complicated malignancies. Glycolysis and cholesterogenesis pathways both play important roles in cancer metabolic adaptations. The main aims of this study are to subtype SKCM based on glycolytic and cholesterogenic genes and to build a clinical outcome predictive algorithm based on the subtypes. METHODS: A dataset with 471 SKCM specimens was downloaded from The Cancer Genome Atlas (TCGA) database. We extracted and clustered genes from the Molecular Signatures Database v7.2 and acquired co-expressed glycolytic and cholesterogenic genes. We then subtyped the SKCM samples and validated the efficacy of subtypes with respect to simple nucleotide variations (SNVs), copy number variation (CNV), patients' survival statuses, tumor microenvironment, and proliferation scores. We also constructed a risk score model based on metabolic subclassification and verified the model using validating datasets. Finally, we explored potential drugs for high-risk SKCM patients. RESULTS: SKCM patients were divided into four subtype groups: glycolytic, cholesterogenic, mixed, and quiescent subgroups. The glycolytic subtype had the worst prognosis and MGAM SNV extent. Compared with the cholesterogenic subgroup, the glycolytic subgroup had higher rates of DDR2 and TPR CNV and higher proliferation scores and MK167 expression levels, but a lower tumor purity proportion. We constructed a forty-four-gene predictive signature and identified MST-321, SB-743921, Neuronal Differentiation Inducer III, romidepsin, vindesine, and YM-155 as high-sensitive drugs for high-risk SKCM patients. CONCLUSIONS: Subtyping SKCM patients via glycolytic and cholesterogenic genes was effective, and patients in the glycolytic-gene enriched group were found to have the worst outcome. A robust prognostic algorithm was developed to enhance clinical decisions in relation to drug administration.

Overexpression of Karyopherin Subunit alpha 2 (KPNA2) Predicts Unfavorable Prognosis and Promotes Bladder Cancer Tumorigenicity via the P53 Pathway.

BACKGROUND We sought to investigate the expression of KPNA2 in bladder cancer (BC) and its relationship with prognosis, and to analyze the potential mechanism of KPNA2 in promoting BC progression. MATERIAL AND METHODS The RNA-seq data on BC from The Cancer Genome Atlas (TCGA) database were imported into R statistical software for differential analysis. The clinical data for patients with BC were screened and analyzed with R software. The survival curve was drawn with the Kaplan-Meier Plotter. The expression of KPNA2 in 4 human BC cell lines and a human bladder epithelial cell line was detected by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting (WB). The proliferation of BC cells was detected with Cell Counting Kit-8 (CCK8), detection of apoptosis, and flow cytometry, and the migration and invasion of BC cells were detected through Transwell assays. WB was used to detect proteins involved in the P53 pathway. RESULTS The expression of KPNA2 was higher in BC. The difference in KPNA2 expression was associated with many clinicopathological factors, and high expression of KPNA2 was associated with shorter survival time. After KPNA2 knockout, the proliferation, migration, and invasion ability decreased significantly, the cell cycle was clearly arrested in the G0/G1 phase, and the number of apoptotic cells increased. Moreover, CyclinD1, BCL2, and pro-caspase3 decreased significantly, whereas P53, P21, BAX, and cleaved-caspase3 increased significantly. The results in the overexpression group were the opposite of results in the knockdown group. CONCLUSIONS KPNA2 is an oncogenic factor that facilitates BC tumorigenicity through the P53 pathway.

Engineered hsa-miR-455-3p-Abundant Extracellular Vesicles Derived from 3D-Cultured Adipose Mesenchymal Stem Cells for Tissue-Engineering Hyaline Cartilage Regeneration.

Efforts are made to enhance the inherent potential of extracellular vesicles (EVs) by utilizing 3D culture platforms and engineered strategies for functional cargo-loading. Three distinct types of adipose mesenchymal stem cells-derived EVs (ADSCs-EVs) are successfully isolated utilizing 3D culture platforms consisting of porous gelatin methacryloyl (PG), PG combined with sericin methacryloyl (PG/SerMA), or PG combined with chondroitin sulfate methacryloyl (PG/ChSMA). These correspond to PG-EVs, PG/SerMA-EVs, and PG/ChSMA-EVs, respectively. Unique microRNA (miRNA) profiles are observed in each type of ADSCs-EVs. Notably, PG-EVs encapsulate higher levels of hsa-miR-455-3p and deliver more hsa-miR-455-3p to chondrocytes, which results in the activation of the hsa-miR-455-3p/PAK2/Smad2/3 axis and the subsequent hyaline cartilage regeneration. Furthermore, the functionality of PG-EVs is optimized through engineered strategies, including agomir/lentivirus transfection, electroporation, and Exo-Fect transfection. These strategies, referred to as Agomir-EVs, Lentivirus-EVs, Electroporation-EVs, and Exo-Fect-EVs, respectively, are ranked based on their efficacy in encapsulating hsa-miR-455-3p, delivering hsa-miR-455-3p to chondrocytes, and promoting cartilage formation via the hsa-miR-455-3p/PAK2/Smad2/3 axis. Notably, Exo-Fect-EVs exhibit the highest efficiency. Collectively, the 3D culture conditions and engineered strategies have an impact on the miRNA profiles and cartilage regeneration capabilities of ADSCs-EVs. The findings provide valuable insights into the mechanisms underlying the promotion of cartilage regeneration by ADSCs-EVs.

A Bibliometric Analysis of Research on Decellularized Matrix for Two Decades.

The articles and reviews in the field of decellularized extracellular matrix (dECM) from 2001 to 2021 were retrieved and extracted from the Web of Science Core Collection. The R package Bibliometrix, CiteSpace, VOSviewer, and the online BIBLIOMETRC platform were utilized for bibliometric analysis, including specific characteristics of annual publications, influential countries/regions, core journals, leading institutions, keywords, key references, cocited authors, journals and institutions, cooperation, and historical direct citations. Our study concluded core references that fueled the development of dECM and highlighted current research directions, hotpots, and trends. From 2001 to 2021, 3,046 publications were retrieved in total, including 2,700 articles and 349 reviews. The United States (n = 895) produced the majority of publications, and the University of Pittsburgh (n = 318) published most productions. Biomaterials were identified as the most productive and influential journal in the dECM field considering the number of publications (n = 194), and total citations (n = 15,694). Immunomodulation, bioreactors, aging, three-dimensional (3D) bioprinting, bone tissue engineering, bioink, hydrogel, biomaterials, and regeneration were the latest high-frequency keywords, indicating the emerging frontiers of dECM. In the field, decellularization techniques lay the foundation. Orthotopic transplantation of recellularized dECM and induction of specific cell differentiation promoted the bursts of research. The 3D bioprinting and hydrogel based on dECM were extensively studied in recent years. The present study provided developmental trajectories, current research status, global collaboration patterns, hotpots, and trending topics of dECM. Decellularization techniques, tissue engineering to regenerate organs, and improvements in application are the major themes over the past two decades. Impact Statement The review article is significant because decellularized extracellular matrix (dECM), which derived from biological tissues and removal of immunogenic cells, is characterized by safety, biocompatibility, and low in toxicity. Showing great application prospects, dECM has been applied in multiple scenarios of tissue repairment and reconstruction, among the most popular topics in tissue engineering. Thus, analyzing and concluding the development, current condition and future trends are of great significance. Comparing to conventional review, this review article systemically and comprehensively concluded the historical development, current status, and research trending topics. Thus, it allows scholars to get a rapid overview of the dECM field, and plan research directions.

NEDD4L in human tumors: regulatory mechanisms and dual effects on anti-tumor and pro-tumor.

Tumorigenesis and tumor development are closely related to the abnormal regulation of ubiquitination. Neural precursor cell expressed developmentally downregulated 4-like (NEDD4L), an E3 ubiquitin ligase critical to the ubiquitination process, plays key roles in the regulation of cancer stem cells, as well as tumor cell functions, including cell proliferation, apoptosis, cell cycle regulation, migration, invasion, epithelial-mesenchymal transition (EMT), and tumor drug resistance, by controlling subsequent protein degradation through ubiquitination. NEDD4L primarily functions as a tumor suppressor in several tumors but also plays an oncogenic role in certain tumors. In this review, we comprehensively summarize the relevant signaling pathways of NEDD4L in tumors, the regulatory mechanisms of its upstream regulatory molecules and downstream substrates, and the resulting functional alterations. Overall, therapeutic strategies targeting NEDD4L to treat cancer may be feasible.

Construction of an lncRNA model for prognostic prediction of bladder cancer.

OBJECTIVE: We aimed to investigate the role and potential mechanisms of long non-coding RNAs (lncRNAs) in bladder cancer (BC), as well as determine their prognostic value. METHODS: LncRNA expression data and clinical data from BC patients were downloaded from The Cancer Genome Atlas (TCGA) database. R software was used to carry out principal component analysis (PCA), differential analysis, and prognostic analysis. Lasso regression and multivariate Cox regression analyses were performed to identify potential prognostic genes. The expression of five identified genes and their correlation with prognosis were verified using TCGA and GSE13507 datasets. In addition, quantitative real-time polymerase chain reaction (qRT-PCR) was used to confirm the expression of these five genes in cell lines (two human BC cell lines and one human bladder epithelial cell line) and tissues (84 pairs of BC tissues and the corresponding paracancerous tissues). Risk scores that had been generated from the five genes and their prognostic ability were assessed by receiver operating characteristic (ROC) and Kaplan-Meier (KM) curves. Co-expressed genes were screened by WGCNA and analyzed by GO and KEGG, while functional enrichment and immune infiltration analyses were performed using STRING ( https://cn.string-db.org/ ) and TIMER2.0 ( http://timer.cistrome.org/ ) online tools, respectively. RESULTS: CYP4F8, FAR2P1, LINC01518, LINC01764, and DTNA were identified as potential prognostic genes. We found that these five genes were differentially expressed in BC tissue, as well as in BC cell lines, and were significantly correlated with the prognosis of BC patients. KM analysis considering risk scores as independent parameters revealed differences in overall survival (OS) by subgroups. The ROC curve revealed that a combined model consisting of all five genes had good predictive ability at 1, 3, and 5 years. GO and KEGG analyses of 567 co-expressed genes revealed that these genes were significantly associated with muscle function. CONCLUSION: LncRNAs can be good predictors of BC development and prognosis, and may act as potential tumor markers and therapeutic targets that may be beneficial in helping clinicians decide the most effective treatment strategies.

A Combined CRISP3 and SPINK1 Prognostic Grade in EPS-Urine and Establishment of Models to Predict Prognosis of Patients With Prostate Cancer.

BACKGROUND: Prostate cancer (PCa) is characterized by significant heterogeneity. Thus, novel prognostic indicators are required to improve prognosis and treatment. METHODS: Cysteine rich secretory protein 3 (CRISP3) and serine peptidase inhibitor Kazal type 1 (SPINK1) levels in expressed prostatic secretion (EPS)-urine collected during digital rectal examination of 496 patients histologically diagnosed with PCa were detected via enzyme-linked immunosorbent assay. A combined CRISP3 and SPINK1 prognostic grade (CSPG) was defined using cut-off values from receiver operating characteristic curves. Log-rank Kaplan-Meier survival curves investigated differences in prognosis between groups. Univariate and multivariate Cox analyses investigated the CSPG relationship with biochemical recurrence (BCR), cancer-specific survival (CSS), and overall survival (OS). Three prognostic models were developed and validated. CONCLUSIONS: CRISP3 and SPINK1 levels increased with Gleason score progression, pathological T stage, and metastasis status. CSPG in EPS-urine, which was an effective independent prognostic variable, accurately predicted the prognosis of patients with PCa. Three clinical prognostic models using the CSPG for BCR, CSS, and OS were developed and validated.

Neoadjuvant immunotherapy, chemotherapy, and combination therapy in muscle-invasive bladder cancer: A multi-center real-world retrospective study.

To parallelly compare the efficacy of neoadjuvant immunotherapy (tislelizumab), neoadjuvant chemotherapy (gemcitabine and cisplatin), and neoadjuvant combination therapy (tislelizumab + GC) in patients with muscle-invasive bladder cancer (MIBC) and explore the efficacy predictors, we perform a multi-center, real-world cohort study that enrolls 253 patients treated with neoadjuvant treatments (combination therapy: 98, chemotherapy: 107, and immunotherapy: 48) from 15 tertiary hospitals. We demonstrate that neoadjuvant combination therapy achieves the highest complete response rate and pathological downstaging rate compared with neoadjuvant immunotherapy or chemotherapy. We develop and validate an efficacy prediction model consisting of pretreatment clinical characteristics, which can pinpoint candidates to receive neoadjuvant combination therapy. We also preliminarily reveal that patients who achieve pathological complete response after neoadjuvant treatments plus maximal transurethral resection of the bladder tumor may be safe to receive bladder preservation therapy. Overall, this study highlights the benefit of neoadjuvant combination therapy based on tislelizumab for MIBC.

A united risk model of 11 immune­related gene pairs and clinical stage for prediction of overall survival in clear cell renal cell carcinoma patients.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer. Currently, we lack effective risk models for the prognosis of ccRCC patients. Given the significant role of cancer immunity in ccRCC, we aimed to establish a novel united risk model including clinical stage and immune-related gene pairs (IRGPs) to assess the prognosis. The gene expression profile and clinical data of ccRCC patients from The Cancer Genome Atlas and Arrayexpress were divided into training cohort (n = 381), validation cohort 1 (n = 156), and validation cohort 2 (n = 101). Through univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator analysis, 11 IRGPs were obtained. After further analysis, it was found that clinical stage could be an independent prognostic factor; hence, we used it to construct a united prognostic model with 11 IRGPs. Based on this model, patients were divided into high-risk and low-risk groups. In Kaplan-Meier analysis, a significant difference was observed in overall survival (OS) among all three cohorts (p < 0.001). The calibration curve revealed that the signature model is in high accordance with the observed values of each data cohort. The 1-year, 3-year, and 5-year receiver operating characteristic curves of each data cohort showed better performance than only IRGP signatures. The results of immune infiltration analysis revealed significantly (p < 0.05) higher abundance of macrophages M0, T follicular helper cells, and other tumor infiltrating cells. In summary, we successfully established a united prognostic risk model, which can effectively assess the OS of ccRCC patients.

Identification of Novel Metabolism-Associated Subtypes for Pancreatic Cancer to Establish an Eighteen-Gene Risk Prediction Model.

Pancreatic cancer (PanC) is an intractable malignancy with a high mortality. Metabolic processes contribute to cancer progression and therapeutic responses, and histopathological subtypes are insufficient for determining prognosis and treatment strategies. In this study, PanC subtypes based on metabolism-related genes were identified and further utilized to construct a prognostic model. Using a cohort of 171 patients from The Cancer Genome Atlas (TCGA) database, transcriptome data, simple nucleotide variants (SNV), and clinical information were analyzed. We divided patients with PanC into metabolic gene-enriched and metabolic gene-desert subtypes. The metabolic gene-enriched subgroup is a high-risk subtype with worse outcomes and a higher frequency of SNVs, especially in KRAS. After further characterizing the subtypes, we constructed a risk score algorithm involving multiple genes (i.e., NEU2, GMPS, PRIM2, PNPT1, LDHA, INPP4B, DPYD, PYGL, CA12, DHRS9, SULT1E1, ENPP2, PDE1C, TPH1, CHST12, POLR3GL, DNMT3A, and PGS1). We verified the reproducibility and reliability of the risk score using three validation cohorts (i.e., independent datasets from TCGA, Gene Expression Omnibus, and Ensemble databases). Finally, drug prediction was completed using a ridge regression model, yielding nine candidate drugs for high-risk patients. These findings support the classification of PanC into two metabolic subtypes and further suggest that the metabolic gene-enriched subgroup is associated with worse outcomes. The newly established risk model for prognosis and therapeutic responses may improve outcomes in patients with PanC.

Establishment of Novel Prostate Cancer Risk Subtypes and A Twelve-Gene Prognostic Model.

Prostate cancer (PCa) is the most common malignancy among men worldwide. However, its complex heterogeneity makes treatment challenging. In this study, we aimed to identify PCa subtypes and a gene signature associated with PCa prognosis. In particular, nine PCa-related pathways were evaluated in patients with PCa by a single-sample gene set enrichment analysis (ssGSEA) and an unsupervised clustering analysis (i.e., consensus clustering). We identified three subtypes with differences in prognosis (Risk_H, Risk_M, and Risk_L). Differences in the proliferation status, frequencies of known subtypes, tumor purity, immune cell composition, and genomic and transcriptomic profiles among the three subtypes were explored based on The Cancer Genome Atlas database. Our results clearly revealed that the Risk_H subtype was associated with the worst prognosis. By a weighted correlation network analysis of genes related to the Risk_H subtype and least absolute shrinkage and selection operator, we developed a 12-gene risk-predicting model. We further validated its accuracy using three public datasets. Effective drugs for high-risk patients identified using the model were predicted. The novel PCa subtypes and prognostic model developed in this study may improve clinical decision-making.

Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature.

Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites. Using an unsupervised clustering analysis, called consensus clustering, we found that patients with PCa could be divided into two subtypes (Methylation_H and Methylation_L) based on the DNA methylation status at these CpG sites. Differences in the epigenome, genome, transcriptome, disease status, immune cell composition, and function between the identified subtypes were explored using The Cancer Genome Atlas database. This analysis clearly revealed the risk characteristics of the Methylation_H subtype. Using a weighted correlation network analysis to select risk-related genes and least absolute shrinkage and selection operator, we constructed a prediction signature for prognosis based on the subtype classification. We further validated its effectiveness using four public datasets. The two novel PCa subtypes and risk predictive signature developed in this study may be effective indicators of prognosis.

A risk prediction model of DNA methylation improves prognosis evaluation and indicates gene targets in prostate cancer.

Aim: Prostate cancer (PCa) is the most common malignancy found in males worldwide. Although it is mostly indolent, PCa still poses a serious threat to long-term health. Materials & methods: The Cancer Genome Atlas data were randomly divided into training and validation groups. Least absolute shrinkage and selection operator regression on DNA methylation data in the training group was conducted to build the model, which was validated in the validation group. Weighted correlation network analysis was conducted on RNA-seq data to identify the therapy target. Functional validation (western blot, quantitative real-time PCR, cell transfection, Cell Counting Kit-8 assay, colony formation assay, wound healing assay and transwell invasion assay) for the target was conducted. Results: The model is an independent predictor of prognosis. The knockdown of FOXD1 inhibits cell proliferation, migration and invasion of PCa. Conclusion: The risk of patients could be evaluated by the model, which revealed that FOXD1 might promote poor prognosis.

An overview of advances in multi-omics analysis in prostate cancer.

Prostate cancer (PCa) is a deadly disease for men, and studies of all types of omics data are necessary to promote precision medicine. The maturity of sequencing technology, the improvements of computer processing power, and the progress achieved in omics analysis methods have improved research efficiency and saved research costs. The occurrence and development of PCa is due to multisystem and multilevel pathological changes. Although omics research at a single level is important, this approach often has limitations. In contrast, the combined analysis of multiple types of omics data can better analyze PCa changes as a whole, thus ensuring the validity of research results to the greatest extent. This paper introduces the applications of single omics in PCa and then summarizes research progress in the combined analysis of two or more types of omics data, so as to systematically and comprehensively analyze the necessity of combined analysis of multiple omics data in PCa.

Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer.

BACKGROUND: There is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk of PCa. METHODS: We downloaded the data of 487 PCa patients from The Cancer Genome Atlas (TCGA) database. We used immunologically relevant genes as input for consensus clustering and applied survival analysis and principal component analysis to determine the properties of the subtypes. We also explored differences of somatic variations, copy number variations, TMPRSS2-ERG fusion, and androgen receptor (AR) scores among the subtypes. Then, we examined the infiltration of different immune cells into the tumor microenvironment in each subtype. We next performed Gene Set Enrichment Analysis (GSEA) to illustrate the characteristics of the subtypes. Finally, based on the subtypes, we constructed a risk predictive model and verified it in TCGA, Gene Expression Omnibus (GEO), cBioPortal, and International Cancer Genome Consortium (ICGC) databases. RESULTS: Four PCa subtypes (C1, C2, C3, and C4) were identified on immune status. Patients with the C3 subtype had the worst prognosis, while the other three groups did not differ significantly from each other in terms of their prognosis. Principal component analysis clearly distinguished high-risk (C3) and low-risk (C1 + 2 + 4) patients. Compared with the case in the low-risk subtype, the Speckle-type POZ Protein (SPOP) had a higher mutation frequency and lower transcriptional level in the high-risk subtype. In C3, there was also a higher frequency of copy number alterations (CNA) of Clusterin (CLU) and lower CLU expression. In addition, C3 had a higher frequency of TMPRSS2-ERG fusion and higher AR scores. M2 macrophages also showed significantly higher infiltration in the high-risk subtype, while CD8+ T cells and dendritic cells had significantly higher infiltration in the low-risk subtype. GSEA revealed that MYC, androgen, and KRAS were relatively activated and p53 was relatively suppressed in high-risk subtype, compared with the levels in the low-risk subtype. Finally, we trained a six-gene signature risk predictive model, which performed well in TCGA, GEO, cBioPortal, and ICGC databases. CONCLUSION: PCa can be divided into four subtypes based on immune-related genes, among which the C3 subtype is associated with a poor prognosis. Based on these subtypes, a risk predictive model was developed, which could indicate patient prognosis.