Prediction of clear cell renal cell carcinoma prognosis based on an immunogenomic landscape analysis.

Immune cell infiltration and tumor-related immune molecules play key roles in tumorigenesis and tumor progression. The influence of immune interactions on the molecular characteristics and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear. A machine learning algorithm was applied to the transcriptome data from The Cancer Genome Atlas database to determine the immunophenotypic and immunological characteristics of ccRCC patients. These algorithms included single-sample gene set enrichment analyses and cell type identification. Using bioinformatics techniques, we examined the prognostic potential and regulatory networks of immune-related genes (IRGs) involved in ccRCC immune interactions. Fifteen IRGs (CCL7, CHGA, CMA1, CRABP2, IFNE, ISG15, NPR3, PDIA2, PGLYRP2, PLA2G2A, SAA1, TEK, TGFA, TNFSF14, and UCN2) were identified as prognostic IRGs associated with overall survival and were used to construct a prognostic model. The area under the receiver operating characteristic curve at 1 year was 0.927; 3 years, 0.822; and 5 years, 0.717, indicating good predictive accuracy. Molecular regulatory networks were found to govern immune interactions in ccRCC. Additionally, we developed a nomogram containing the model and clinical characteristics with high prognostic potential. By systematically examining the sophisticated regulatory mechanisms, molecular characteristics, and prognostic potential of ccRCC immune interactions, we provided an important framework for understanding the molecular mechanisms of ccRCC and identifying new prognostic markers and therapeutic targets for future research.

Assessment of preoperative health-related quality of life in patients undergoing thyroidectomy based on patient-reported outcomes.

Introduction: To evaluate the preoperative health-related quality of life (HRQoL) and influencing factors of HRQoL in patients undergoing thyroidectomy based on patient-reported outcomes. Materials and methods: Patients who were diagnosed and treated in Sichuan Cancer Hospital from February 2022 to December 2022 and were scheduled to undergo thyroidectomy were included. Each participant completed the basic information questionnaire and patient-reported outcome assessment scales before surgery. HRQoL was assessed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 (EORTC QLQ-C30), the Thyroid Cancer-Specific Quality of Life (THYCA-QoL), and the Hamilton Anxiety Scale (HAMA). The Wilcoxon rank sum test or the Kruskal-Wallis test was used to analyze the association between patient characteristics and HRQoL, and the variables with statistical significance were included in multiple linear regression analysis. Results: 450 patients were included in the study. According to the analysis of the THYCA-QoL scores, the psychological subscale was the most complained about. Anxiety was the most common symptom of the HAMA. Factors associated with worse general QoL on the EORTC QLQ-C30 included nondiagnostic/unsatisfactory fine-needle aspiration (FNA) result. Planned lateral neck dissection and nondiagnostic/unsatisfactory FNA result were influential factors for preoperative anxiety. Males and longer sleep duration were associated with better thyroid cancer-specific QoL, better general QoL, and less anxiety. Conclusion: The preoperative HRQoL of patients undergoing thyroidectomy was generally good. Females, insufficient sleep duration, planned lateral neck dissection, and nondiagnostic/unsatisfactory FNA result were associated with worse preoperative HRQoL.

Identification of RBM15 as a prognostic biomarker in prostate cancer involving the regulation of prognostic m6A-related lncRNAs.

BACKGROUND: Long noncoding RNAs (lncRNAs) and N6-methyladenosine (m6A) modification of RNA play pivotal roles in tumorigenesis and cancer progression. However, knowledge regarding the expression patterns of m6A-related lncRNAs and their corresponding m6A regulators in prostate cancer (PCa) is limited. This study aimed to delineate the landscape of m6A-related lncRNAs, develop a predictive model, and identify the critical m6A regulators of prognostic lncRNAs in PCa. METHODS: Clinical and transcriptome data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic m6A-related lncRNAs were subsequently identified through Pearson correlation and univariate Cox regression analyses. The prognostic lncRNAs were clustered into two groups by consensus clustering analysis, and a risk signature model was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis of the lncRNAs. This model was evaluated using survival, clinicopathological, and immunological analyses. Furthermore, based on the constructed lncRNA-m6A regulatory network and RT-qPCR results, RBM15 was identified as a critical regulator of m6A-related lncRNAs. The biological roles of RBM15 in PCa were explored through bioinformatics analysis and biological experiments. RESULTS: Thirty-four prognostic m6A-related lncRNAs were identified and categorized into two clusters with different expression patterns and survival outcomes in PCa patients. Seven m6A lncRNAs (AC105345.1, AL354989.1, AC138028.4, AC022211.1, AC020558.2, AC004076.2, and LINC02666) were selected to construct a risk signature with robust predictive ability for overall survival and were correlated with clinicopathological characteristics and the immune microenvironment of PCa patients. Among them, LINC02666 and AC022211.1 were regulated by RBM15. In addition, RBM15 expression correlated with PCa progression, survival, and the immune response. Patients with elevated RBM15 expression were more susceptible to the drug AMG-232. Moreover, silencing RBM15 decreased the viability of PCa cells and promoted apoptosis. CONCLUSION: RBM15 is involved in the regulation of prognostic lncRNAs in the risk signature and has a robust predictive ability for PCa, making it a promising biomarker in PCa.

13,14-seco withaphysalins from Physalis minima and their inhibitory effects on NLRP3 inflammasome activation.

Seven new 13,14-seco withaphysalins including two new skeletons (1 and 9) were isolated from the whole plants of Physalis minima, together with three known analogues (6-8). Among them, compound 1 was an extremely rare steroid with a 6, 8-cyclo ring. Their structures were established by extensive analysis of spectroscopic data, experimental electronic circular dichroism measurements, and single-crystal X-ray crystallographic analysis. In Raw264.7 cells, compounds 1-3, 5, 6, and 8 demonstrated potent ability to reduce the NLRP3-dependent caspase-1 activation. Among these compounds, 1 and 2 showed a superior potential, consistently concentration-dependent downregulating NLRP3-dependent proinflammatory cytokine IL-1ß production in macrophage. Mechanistically, compounds 1 and 2 reduced the cleavage of caspase-1 and GSDMD, and exhibited no obvious impact both on the NF-κB activation and the expression of NLRP3 and IL-1ß, suggesting that the compounds target the activation of the NLRP3 pathway mainly by inhibiting the NLRP3 inflammasome activation step rather than the priming step.

Anti-inflammatory germacrane-type sesquiterpene lactones from Vernonia sylvatica.

Nine new germacranolides, sylvaticalides A-H (1-9), and three known analogues (10-12) were isolated from the aerial part of Vernonia sylvatica. Their structures were established using comprehensive spectroscopic analysis, including high-resolution electrospray ionization mass spectroscopy (HR-ESI-MS) and 1D and 2D nuclear magnetic resonance (NMR) spectra. Their absolute configurations were determined by X-ray diffraction experiments. The anti-inflammatory activities of all isolated compounds were assessed by evaluating their inhibitory effects on the nuclear factor kappa B (NF-κB) pathway, which was activated by lipopolysaccharide (LPS)-stimulated human THP1-Dual cells, and the interferon-stimulated gene (ISG) pathway, activated by STING agonist MSA-2 in the same cell model. Compounds 1, 2 and 6 showed inhibitory effects on the NF-κB and ISG signaling pathways, with IC50 values ranging from 4.12 to 10.57 µmol·L-1.

Revealing the mechanism of ethyl acetate extracts of Semen Impatientis against prostate cancer based on network pharmacology and transcriptomics.

ETHNOPHARMACOLOGICAL RELEVANCE: Prostate cancer (PCa) is the most common malignancy of the male genitourinary system and currently lacks effective treatment. Semen Impatientis, the dried ripe seed of Impatiens balsamina L., is described by the Chinese Pharmacopoeia as a traditional Chinese medicine (TCM) and is used in clinical practice to treat tumors, abdominal masses, etc. In our previous study, the ethyl acetate extracts of Semen Impatientis (EAESI) was demonstrated to be the most effective extract against PCa among various extracts. However, the biological effects of EAESI against PCa in vivo and the specific antitumor mechanisms involved remain unknown. AIM OF THE STUDY: In this study, we aimed to investigate the antitumor effect of EAESI on PCa in vitro and in vivo by performing network pharmacology analysis, transcriptomic analysis, and experiments to explore and verify the underlying mechanisms involved. MATERIALS AND METHODS: The antitumor effect of EAESI on PCa in vitro and in vivo was investigated via CCK-8, EdU, flow cytometry, and wound healing assays and xenograft tumor models. Network pharmacology analysis and transcriptomic analysis were employed to explore the underlying mechanism of EAESI against PCa. Activating transcription factor 3 (ATF3) and androgen receptor (AR) were confirmed to be the targets of EAESI against PCa by RT‒qPCR, western blotting, and rescue assays. In addition, the interaction between ATF3 and AR was assessed by coimmunoprecipitation, immunofluorescence, and nuclear-cytoplasmic separation assays. RESULTS: EAESI decreased cell viability, inhibited cell proliferation and migration, and induced apoptosis in AR+ and AR- PCa cells. Moreover, EAESI suppressed the growth of xenograft tumors in vivo. Network pharmacology analysis revealed that the hub targets of EAESI against PCa included AR, AKT1, TP53, and CCND1. Transcriptomic analysis indicated that activating transcription factor 3 (ATF3) was the most likely critical target of EAESI. EAESI downregulated AR expression and decreased the transcriptional activity of AR through ATF3 in AR+ PCa cells; and EAESI promoted the expression of ATF3 and exerted its antitumor effect via ATF3 in AR+ and AR- PCa cells. CONCLUSIONS: EAESI exerts good antitumor effects on PCa both in vitro and in vivo, and ATF3 and AR are the critical targets through which EAESI exerts antitumor effects on AR+ and AR- PCa cells.

Identification and validation of an individualized metabolic prognostic signature for predicting the biochemical recurrence of prostate cancer based on the immune microenvironment.

BACKGROUND: Prostate cancer (PCa) is the most prevalent genitourinary malignancy in men, with a significant proportion of patients developing biochemical recurrence (BCR) after treatment. The immune microenvironment and metabolic alterations have crucial implications for the tumorigenesis and progression of PCa. Therefore, identifying metabolic genes associated with the immune microenvironment holds promise for predicting BCR and improving PCa prognosis. METHODS: In this study, ssGSEA and hierarchical clustering analysis were first conducted to evaluate and group PCa samples, followed by the use of the ESTIMATE and CIBERSORT algorithms to characterize the immunophenotypes and tumor microenvironment. The differential metabolic genes (MTGs) between groups were utilized to develop a prognostic-related signature. The predictive performance of the signature was assessed by principal component analysis (PCA), receiver operating characteristic (ROC) curve analysis, survival analysis, and the TIDE algorithm. A miRNA-MTGs regulatory network and predictive nomogram were constructed. Moreover, the expression of prognostic MTGs in PCa was detected by RT‒qPCR. RESULTS: PCa samples from the TCGA cohort were separated into two groups: the immune-low group and immune-high group. Forty-eight differentially expressed MTGs between the groups were identified, including 37 up-regulated and 11 down-regulated MTGs. Subsequently, CEL, CYP3A4, and PDE6G were identified as the genes most strongly associated with the BCR of PCa patients and these genes were utilized to establish the MTGs-based prognostic signatures. PCA, ROC curves analysis, Kaplan-Meier survival analysis, and the nomogram all showed the good predictive ability of the signature regardless of clinical variables. Furthermore, the MTGs-based signature was indicated as a potential predictive biomarker for immunotherapy response. Nine miRNAs involved in the regulation of prognostic MTGs were determined. In addition to the CEL gene, the PDE6G and CYP3A4 genes were expressed at higher levels in PCa samples. CONCLUSIONS: The MTGs-based signature represents a novel approach with promising potential for predicting BCR in PCa patients.

Comparison of cytokine levels in prostatic secretion between the IIIa and IIIb subtypes of prostatitis.

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), also known as National Institutes of Health (NIH) type III prostatitis, is a common disorder with an unclear etiology and no known curative treatments. Based on the presence or absence of leukocytes in expressed prostatic secretion (EPS), CP/CPPS is classified further into IIIa (inflammatory) and IIIb (noninflammatory) subtypes. However, the severity of symptoms is not entirely consistent with the white blood cell (WBC) count. Following the preliminary finding of a link between inflammatory cytokines and CP/CPPS, we performed this clinical study with the aim of identifying cytokines that are differentially expressed according to whether the prostatitis subtype is IIIa or IIIb. We found that granulocyte colony-stimulating factor (G-CSF), interleukin-18 (IL-18), and monocyte chemoattractant protein-1 (MCP-1) levels were significantly elevated and interferon-inducible protein-10 (IP-10) and platelet-derived growth factor-BB (PDGF-BB) levels were downregulated in the EPS of patients with type IIIa prostatitis. In a word, it is a meaningful study in which we investigate the levels of various cytokines in EPS according to whether prostatitis is the IIIa or IIIb subtype. The combination of G-CSF, IL-18, MCP-1, IP-10, and PDGF-BB expression levels could form a basis for classification, diagnosis, and therapeutic targets in clinical CP/CPPS.

Exploring a ferroptosis and oxidative stress-based prognostic model for clear cell renal cell carcinoma.

Background: Ferroptosis is a newly defined cell death process triggered by increased iron load and tremendous lipid reactive oxygen species (ROS). Oxidative stress-related ferroptosis is of great important to the occurrence and progression of clear cell renal cell carcinoma (ccRCC), which is particularly susceptibility to ferroptosis agonist. Therefore, exploring the molecular features of ferroptosis and oxidative stress might guide the clinical treatment and prognosis prediction for ccRCC patients. Methods: The differentially expressed ferroptosis and oxidative stress-associated genes (FPTOSs) between normal renal and ccRCC tissues were identified based on The Cancer Genome Atlas (TCGA) database, and those with prognostic significances were applied to develop a prognostic model and a risk scoring system (FPTOS_score). The clinical parameter, miRNA regulation, tumor mutation burden (TMB), immune cell infiltration, immunotherapy response, and drug susceptibility between two FPTOS-based risk stratifications were determined. Results: We have identified 5 prognosis-associated FPTOSs (ACADSB, CDCA3, CHAC1, MYCN, and TFAP2A), and developed a reliable FPTOS_socre system to distinguish patients into low- and high-risk groups. The findings implied that patients from the high-risk group performed poor prognoses, even after stratified analysis of various clinical parameters. A total of 30 miRNA-FPTOS regulatory pairs were recognized to identify the possible molecular mechanisms. Meanwhile, patients from the high-risk group exhibited higher TMB levels than those from the low-risk groups, and the predominant mutated driver genes were VHL, PBRM1 and TTN in both groups. The main infiltrating immune cells of high- and low-risk groups were CD8+ T cells and resting mast cells, respectively, and patients from the high-risk groups showed preferable drug responsiveness to anti-PD-1 immunotherapy. Eventually, potential sensitive drugs (cisplatin, BI-D1870, and docetaxel) and their enrichment pathways were identified to guide the treatment of ccRCC patients with high-risk. Conclusion: Our study comprehensively analyzed the expression profiles of FPTOSs and constructed a scoring system with considerable prognostic value, which would supply novel insights into the personalized treatment strategies and prognostic evaluation of ccRCC patient.

Identification of a novel senescence-associated signature to predict biochemical recurrence and immune microenvironment for prostate cancer.

Background: Prostate cancer (PCa) is an age-associated malignancy with high morbidity and mortality rate, posing a severe threat to public health. Cellular senescence, a specialized cell cycle arrest form, results in the secretion of various inflammatory mediators. In recent studies, senescence has shown an essential role in tumorigenesis and tumor development, yet the extensive effects of senescence in PCa have not been systematically investigated. Here, we aimed to develop a feasible senescence-associated prognosis model for early identification and appropriate management in patients with PCa. Method: The RNA sequence results and clinical information available from The Cancer Genome Atlas (TCGA) and a list of experimentally validated senescence-related genes (SRGs) from the CellAge database were first obtained. Then, a senescence-risk signature related with prognosis was constructed using univariate Cox and LASSO regression analysis. We calculated the risk score of each patient and divided them into high-risk and low-risk groups in terms of the median value. Furthermore, two datasets (GSE70770 and GSE46602) were used to assess the effects of the risk model. A nomogram was built by integrating the risk score and clinical characteristics, which was further verified using ROC curves and calibrations. Finally, we compared the differences in the tumor microenvironment (TME) landscape, drug susceptibility, and the functional enrichment among the different risk groups. Results: We established a unique prognostic signature in PCa patients based on eight SRGs, including CENPA, ADCK5, FOXM1, TFAP4, MAPK, LGALS3, BAG3, and NOX4, and validated well prognosis-predictive power in independent datasets. The risk model was associated with age and TNM staging, and the calibration chart presented a high consistency in nomogram prediction. Additionally, the prognostic signature could serve as an independent prediction factor due to its high accuracy. Notably, we found that the risk score was positively associated with tumor mutation burden (TMB) and immune checkpoint, whereas negatively correlated with tumor immune dysfunction and exclusion (TIDE), suggesting that these patients with risk scores were more sensitive to immunotherapy. Drug susceptibility analysis revealed differences in the responses to general drugs (docetaxel, cyclophosphamide, 5-Fluorouracil, cisplatin, paclitaxel, and vincristine) were yielded between the two risk groups. Conclusion: Identifying the SRG-score signature may become a promising method for predicting the prognosis of patients with PCa and tailoring appropriate treatment strategies.