Identification of cancer stem cell-related genes through single cells and machine learning for predicting prostate cancer prognosis and immunotherapy.

Background: Cancer stem cells (CSCs) are a subset of cells within tumors that possess the unique ability to self-renew and give rise to diverse tumor cells. These cells are crucial in driving tumor metastasis, recurrence, and resistance to treatment. The objective of this study was to pinpoint the essential regulatory genes associated with CSCs in prostate adenocarcinoma (PRAD) and assess their potential significance in the diagnosis, prognosis, and immunotherapy of patients with PRAD. Method: The study utilized single-cell analysis techniques to identify stem cell-related genes and evaluate their significance in relation to patient prognosis and immunotherapy in PRAD through cluster analysis. By utilizing diverse datasets and employing various machine learning methods for clustering, diagnostic models for PRAD were developed and validated. The random forest algorithm pinpointed HSPE1 as the most crucial prognostic gene among the stem cell-related genes. Furthermore, the study delved into the association between HSPE1 and immune infiltration, and employed molecular docking to investigate the relationship between HSPE1 and its associated compounds. Immunofluorescence staining analysis of 60 PRAD tissue samples confirmed the expression of HSPE1 and its correlation with patient prognosis in PRAD. Result: This study identified 15 crucial stem cell-related genes through single-cell analysis, highlighting their importance in diagnosing, prognosticating, and potentially treating PRAD patients. HSPE1 was specifically linked to PRAD prognosis and response to immunotherapy, with experimental data supporting its upregulation in PRAD and association with poorer prognosis. Conclusion: Overall, our findings underscore the significant role of stem cell-related genes in PRAD and unveil HSPE1 as a novel target related to stem cell.

Histone lactylation-related genes correlate with the molecular patterns and functions of cancer-associated fibroblasts and have significant clinical implications in clear cell renal cell carcinoma.

Recent research emphasised the indispensable role of histone lactylation in the activation of hepatic stellate cells. The VHL mutation is extremely common in clear cell renal cell carcinoma, which normally causes a metabolic shift in cancer cells and increases lactate production, eventually creating a lactate-enriched tumour microenvironment. Cancer-associated fibroblasts (CAFs) promote tumour progression, which is also vital in clear cell renal cell carcinoma. Therefore, this study investigated histone lactylation in CAFs and its impact on patient survival. Multiomics technology was employed to determine the role of histone lactylation-related genes in the evolution of CAFs which correlated with the function and molecular signatures of CAFs. The results suggested that TIMP1 was the hub gene of histone lactylation-related genes in clear cell renal cell carcinoma.

Identification of metastasis-related genes for predicting prostate cancer diagnosis, metastasis and immunotherapy drug candidates using machine learning approaches.

BACKGROUND: Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients. Identifying novel and effective biomarkers is essential for understanding the mechanisms of metastasis in PCa patients and developing successful interventions. METHODS: Using the GSE8511 and GSE27616 data sets, 21 metastasis-related genes were identified through the weighted gene co-expression network analysis (WGCNA) method. Subsequent functional analysis of these genes was conducted on the gene set cancer analysis (GSCA) website. Cluster analysis was utilized to explore the relationship between these genes, immune infiltration in PCa, and the efficacy of targeted drug IC50 scores. Machine learning algorithms were then employed to construct diagnostic and prognostic models, assessing their predictive accuracy. Additionally, multivariate COX regression analysis highlighted the significant role of POLD1 and examined its association with DNA methylation. Finally, molecular docking and immunohistochemistry experiments were carried out to assess the binding affinity of POLD1 to PCa drugs and its impact on PCa prognosis. RESULTS: The study identified 21 metastasis-related genes using the WGCNA method, which were found to be associated with DNA damage, hormone AR activation, and inhibition of the RTK pathway. Cluster analysis confirmed a significant correlation between these genes and PCa metastasis, particularly in the context of immunotherapy and targeted therapy drugs. A diagnostic model combining multiple machine learning algorithms showed strong predictive capabilities for PCa diagnosis, while a transfer model using the LASSO algorithm also yielded promising results. POLD1 emerged as a key prognostic gene among the metastatic genes, showing associations with DNA methylation. Molecular docking experiments supported its high affinity with PCa-targeted drugs. Immunohistochemistry experiments further validated that increased POLD1 expression is linked to poor prognosis in PCa patients. CONCLUSIONS: The developed diagnostic and metastasis models provide substantial value for patients with prostate cancer. The discovery of POLD1 as a novel biomarker related to prostate cancer metastasis offers a promising avenue for enhancing treatment of prostate cancer metastasis.

Comparing upfront surgery with neoadjuvant treatments in patients with resectable, borderline resectable or locally advanced pancreatic cancer: a systematic review and network meta-analysis of randomized clinical trials.

BACKGROUND: The aim was to explore the optimal neoadjuvant therapy strategy for resectable, borderline resectable, and locally advanced pancreatic cancer, in order to provide a theoretical basis for the development of new neoadjuvant treatment protocols for clinical use. PATIENTS AND METHODS: The authors reviewed literature titles and abstracts comparing three treatment strategies (neoadjuvant chemoradiotherapy, neoadjuvant chemotherapy, and upfront surgery) in PubMed, Embase, The Cochrane Library, Web of Science from 2009 to 2023 to estimate relative odds ratios for resection rate and hazard ratios (HRs) for overall survival (OS) in all include trials. RESULTS: A total of nine studies involving 889 patients were included in the analysis. The treatment methods included upfront surgery, neoadjuvant chemotherapy, and neoadjuvant chemoradiotherapy followed by surgery. The network meta-analysis results demonstrated that neoadjuvant chemoradiotherapy followed by surgery was an effective approach in improving OS for resectable and borderline resectable pancreatic cancer (RPC) patients compared to upfront surgery (HR: 0.79, 95% CI: 0.64-0.98) and neoadjuvant chemotherapy (HR: 0.79, 95% CI: 0.64-0.98). Additionally, neoadjuvant chemoradiotherapy significantly increased the margin negative resection (R0) rate and pathological negative lymph node (pN0) rate in patients with resectable and borderline RPC. However, it is worth noting that neoadjuvant chemoradiotherapy increased the risk of grade 3 or higher treatment-related adverse events, including in patients with locally advanced pancreatic cancer. CONCLUSIONS: The current evidence suggests that neoadjuvant chemoradiotherapy followed by surgery is the optimal choice for treating patients with resectable and borderline RPC. Future research should focus on optimizing neoadjuvant chemoradiotherapy regimens to effectively improve OS while reducing the occurrence of adverse events.

Small Molecule-Inducible and Photoactivatable Cellular RNA N1-Methyladenosine Editing.

N1-methyladenosine (m1A) modification is one of the most prevalent epigenetic modifications on RNA. Given the vital role of m1A modification in RNA processing such as splicing, stability and translation, developing a precise and controllable m1A editing tool is pivotal for in-depth investigating the biological functions of m1A. In this study, we developed an abscisic acid (ABA)-inducible and reversible m1A demethylation tool (termed AI-dm1A), which targets specific transcripts by combining the chemical proximity-induction techniques with the CRISPR/dCas13b system and ALKBH3. We successfully employed AI-dm1A to selectively demethylate the m1A modifications at A8422 of MALAT1 RNA, and this demethylation process could be reversed by removing ABA. Furthermore, we validated its demethylation function on various types of cellular RNAs including mRNA, rRNA and lncRNA. Additionally, we used AI-dm1A to specifically demethylate m1A on ATP5D mRNA, which promoted ATP5D expression and enhanced the glycolysis activity of tumor cells. Conversely, by replacing the demethylase ALKBH3 with methyltransferase TRMT61A, we also developed a controllable m1A methylation tool, namely AI-m1A. Finally, we caged ABA by 4,5-dimethoxy-2-nitrobenzyl (DMNB) to achieve light-inducible m1A methylation or demethylation on specific transcripts. Collectively, our m1A editing tool enables us to flexibly study how m1A modifications on specific transcript influence biological functions and phenotypes.

Multi-omics comprehensive analysis reveals the predictive value of N6-methyladenosine- related genes in prognosis and immune escape of bladder cancer.

BACKGROUND: N6-methyladenosine (m6A) is the most frequent RNA modification in mammals, and its role in bladder cancer (BC) remains rarely revealed. OBJECTIVE: To predict the value of m6A-related genes in prognosis and immunity in BC. METHODS: We performed multiple omics analysis of 618 TCGA and GEO patients and used principal component analysis (PCA) to calculate the m6A score for BC patients. RESULTS: We described the multiple omics status of 23 m6A methylation-related genes (MRGs), and four m6A clusters were identified, which showed significant differences in immune infiltration and biological pathways. Next, we intersected the differential genes among m6A clusters, and 11 survival-related genes were identified, which were used to calculate the m6A score for the patients. We found that the high-score (HS) group showed lower tumor mutation burden (TMB) and TP53 mutations and better prognosis than the low-score (LS) group. Lower immune infiltration, higher expression of PD-L1, PD-1, and CTLA4, and higher immune dysfunction and immune exclusion scores were identified in the LS group, suggesting a higher possibility of immune escape. Finally, the experimental verification shows that the m6A related genes, such as IGFBP1, plays an important role in the growth and metastasis of bladder cancer. CONCLUSIONS: These findings revealed the important roles of m6A MRGs in predicting prognosis, TMB status, TP53 mutation, immune functions and immunotherapeutic response in BC.

JAM3: A prognostic biomarker for bladder cancer via epithelial-mesenchymal transition regulation.

Understanding the intricate relationship between prognosis, immune function, and molecular markers in bladder cancer (BC) demands sophisticated analytical methods. To identify novel biomarkers for predicting prognosis and immune function in BC patients, we combined weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis. This was conducted using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Ultimately, we screened the junctional adhesion molecule 3 (JAM3) as an independent risk factor in BC. High levels of JAM3 were linked to adverse clinical parameters, such as higher T and N stages. Additionally, a JAM3-based nomogram model accurately predicted 1-, 3- and 5-year survival rates of BC patients, indicating potential clinical utility. Functional enrichment analysis revealed that high JAM3 expression activated the calcium signaling pathway, the extracellular matrix (ECM)-receptor interaction, and the PI3K-Akt signaling pathway, and was positively correlated with genes associated with epithelial-mesenchymal transition (EMT). Subsequently, we found that overexpression of JAM3 promoted the migration and invasion abilities in BC cells, regulating the expression levels of N-cadherin, matrix metallopeptidase 2 (MMP2), and Claudin-1 thereby promoting EMT levels. Additionally, we showed that JAM3 was negatively correlated with anti-tumor immune cells such as CD8+ T cells, while positively correlated with pro-tumor immune cells such as M2 macrophages, suggesting its involvement in immune cell infiltration. The immune checkpoint CD200 also showed a positive correlation with JAM3. Our findings revealed that elevated JAM3 levels are predictive of poor prognosis and immune cell infiltration in BC patients by regulating the EMT process.

Formation of Different Polyploids Through Disrupting Meiotic Crossover Frequencies Based on cntd1 Knockout in Zebrafish.

Polyploidy, a significant catalyst for speciation and evolutionary processes in both plant and animal kingdoms, has been recognized for a long time. However, the exact molecular mechanism that leads to polyploid formation, especially in vertebrates, is not fully understood. Our study aimed to elucidate this phenomenon using the zebrafish model. We successfully achieved an effective knockout of the cyclin N-terminal domain containing 1 (cntd1) using CRISPR/Cas9 technology. This resulted in impaired formation of meiotic crossovers, leading to cell-cycle arrest during meiotic metaphase and triggering apoptosis of spermatocytes in the testes. Despite these defects, the mutant (cntd1-/-) males were still able to produce a limited amount of sperm with normal ploidy and function. Interestingly, in the mutant females, it was the ploidy not the capacity of egg production that was altered. This resulted in the production of haploid, aneuploid, and unreduced gametes. This alteration enabled us to successfully obtain triploid and tetraploid zebrafish from cntd1-/- and cntd1-/-/- females, respectively. Furthermore, the tetraploid-heterozygous zebrafish produced reduced-diploid gametes and yielded all-triploid or all-tetraploid offspring when crossed with wild-type (WT) or tetraploid zebrafish, respectively. Collectively, our findings provide direct evidence supporting the crucial role of meiotic crossover defects in the process of polyploidization. This is particularly evident in the generation of unreduced eggs in fish and, potentially, other vertebrate species.

Multi-Functional Formaldehyde-Nitrate Batteries for Wastewater Refining, Electricity Generation, and Production of Ammonia and Formate.

As bulky pollutants in industrial and agricultural wastewater, nitrate and formaldehyde pose serious threats to the human health and ecosystem. Current purification technologies including chemical and bio-/photo-/electro-chemical methods, are generally high-cost, time-consuming, or energy-intensive. Here, we report a novel formaldehyde-nitrate battery by pairing anodic formaldehyde oxidation with cathodic nitrate reduction, which simultaneously enables wastewater purification, electricity generation, and the production of high-value-added ammonia and formate. As a result, the formaldehyde-nitrate battery remarkably exhibits an open-circuit voltage of 0.75 V, a peak power density of 3.38 mW cm-2 and the yield rates of 32.7 mg h-1 cm-2 for ammonia and 889.4 mg h-1 cm-2 for formate. In a large-scale formaldehyde-nitrate battery (25 cm2 ), 99.9 % of nitrate and 99.8 % of formaldehyde are removed from simulated industrial wastewater and the electricity of 2.03 W⋅h per day is generated. Moreover, the design of such a multi-functional battery is universally applicable to the coupling of NO3 - or NO2 - reduction with various aldehyde oxidization, paving a new avenue for wastewater purification and chemical manufacturing.

A new immune-related gene signature predicts the prognosis and immune escape of bladder cancer.

BACKGROUND: The biological roles of immune-related genes (IRGs) in bladder cancer (BC) need to be further elucidated. OBJECTIVE: To elucidate the predictive value of IRGs for prognosis and immune escape in BC. METHODS: We comprehensively analyzed the transcriptomic and clinical information of 430 cases, including 19 normal and 411 BC patients from the TCGA database, and verified 165 BC cases in the GSE13507 dataset. The risk model was constructed based on IRGs by applying LASSO Cox regression and exploring the relationship between the risk score and prognosis, gene mutations, and immune escape in BC patients. RESULTS: We identified 4 survival-related genes (PSMC1, RAC3, ROBO2 and ITGB3) among 6,196 IRGs in both the TCGA and GES13507 datasets,, which were used to establish a gene risk model by applying LASSO Cox regression. The results showed that the high-risk (HR) group was closely associated with poor survival or advanced pathological stage of BC. Furthermore, the risk score was found to be an independent risk factor for prognosis of BC patients. In addition, high-risk individuals showed a greater prevalence of TP53 mutations lower CD8+ T-cell and NK cell infiltration, higher Treg cell infiltration, higher expression of PD-L1, and higher immune exclusion scores than those in the low-risk (LR) group. Finally, the experimental verification shows that the model construction gene, especially PMSC1, plays an important role in the growth and metastasis of bladder cancer. CONCLUSIONS: These evidences revealed the vital role of IRGs in predicting prognosis, TP53 mutation and immune escape in BC patients.