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Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common gynecologic tumor and patients with advanced and recurrent disease usually have a poor clinical outcome. Angiogenesis is involved in the biological processes of tumors and can promote tumor growth and invasion. In this paper, we created a signature for predicting prognosis based on angiogenesis-related lncRNAs (ARLs). This provides a prospective direction for enhancing the efficacy of immunotherapy in CESC patients. We screened seven OS-related ARLs by univariate and multivariate regression analyses and Lasso analysis and developed a prognostic signature at the same time. Then, we performed an internal validation in the TCGA-CESC cohort to increase the precision of the study. In addition, we performed a series of analyses based on ARLs, including immune cell infiltration, immune function, immune checkpoint, tumor mutation load, and drug sensitivity analysis. Our created signature based on ARLs can effectively predict the prognosis of CESC patients. To strengthen the prediction accuracy of the signature, we built a nomogram by combining signature and clinical features.
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Purpose: In recent years, traditional Chinese medicine has received widespread attention in the field of cancer pain treatment. This meta-analysis is the first to evaluate the effectiveness and safety of acupuncture point stimulation in the treatment of stomach cancer pain. Methods: For this systematic review and meta-analysis, we searched PubMed, Web of Science, Cochrane Library, Embase, WANFANG, China National Knowledge Infrastructure (CNKI), and Chinese Journal of Science and Technology (VIP) databases as well as forward and backward citations to studies published between database creation to July 27, 2023. All randomized controlled trials (RCTs) on acupuncture point stimulation for the treatment of patients with stomach cancer pain were included without language restrictions. We assessed all outcome indicators of the included trials. The evidence from the randomized controlled trials was synthesized as the standardized mean difference (SMD) of symptom change. The quality of the evidence was assessed using the Cochrane Risk of Bias tool. This study is registered on PROSPERO under the number CRD42023457341. Results: Eleven RCTs were included. The study included 768 patients, split into 2 groups: acupuncture point stimulation treatment group (n = 406), medication control group (n = 372). The results showed that treatment was more effective in the acupuncture point stimulation treatment group than in the medication control group (efficacy rate, RR = 1.63, 95% CI 1.37 to 1.94, p < 0.00001), decreasing in NRS score was greater in acupuncture point stimulation treatment group than in the medication control group (SMD = -1.30, 95% CI -1.96 to -0.63, p < 0.001). Systematic Review Registration: https://clinicaltrials.gov/, identifier CRD42023457341.
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Purpose: Central obesity may contribute to breast cancer (BC); however, there is no dose-response relationship. This meta-analysis examined the effects of central obesity on BC and their potential dose-response relationship. Methods: In the present study, PubMed, Medline, Embase, and Web of Science were searched on 1 August 2022 for published articles. We included the prospective cohort and case-control studies that reported the relationship between central obesity and BC. Summary effect size estimates were expressed as risk ratios (RRs) or odds ratios (ORs) with 95% confidence intervals (95% CI) and were evaluated using random-effect models. The inconsistency index (I2) was used to quantify the heterogeneity magnitude derived from the random-effects Mantel-Haenszel model. Results: This meta-analysis included 57 studies (26 case-control and 31 prospective cohort) as of August 2022. Case-control studies indicated that waist circumference (WC) (adjusted OR = 1.18; 95% CI: 1.00-1.38; P = 0.051) and waist-to-hip ratio (WHR) (adjusted OR = 1.28; 95% CI: 1.07-1.53; P = 0.008) were significantly positively related to BC. Subgroup analysis showed that central obesity measured by WC increased the premenopausal (adjusted OR = 1.15; 95% CI: 0.99-1.34; P = 0.063) and postmenopausal (adjusted OR = 1.18; 95% CI: 1.03-1.36; P = 0.018) BC risk and the same relationship appeared in WHR between premenopausal (adjusted OR = 1.38; 95% CI: 1.19-1.59; P < 0.001) and postmenopausal (adjusted OR = 1.41; 95% CI: 1.22-1.64; P < 0.001). The same relationship was observed in hormone receptor-positive (HR+) (adjusted ORWC = 1.26; 95% CI: 1.02-1.57; P = 0.035, adjusted ORWHR = 1.41; 95% CI: 1.00-1.98; P = 0.051) and hormone receptor-negative (HR-) (adjusted ORWC = 1.44; 95% CI: 1.13-1.83; P = 0.003, adjusted ORWHR = 1.42; 95% CI: 0.95-2.13; P = 0.087) BCs. Prospective cohort studies indicated that high WC (adjusted RR = 1.12; 95% CI: 1.08-1.16; P < 0.001) and WHR (adjusted RR = 1.05; 95% CI: 1.018-1.09; P = 0.017) may increase BC risk. Subgroup analysis demonstrated a significant correlation during premenopausal (adjusted RR = 1.08; 95% CI: 1.02-1.14; P = 0.007) and postmenopausal (adjusted RR = 1.14; 95% CI: 1.10-1.19; P < 0.001) between BC and central obesity measured by WC, and WHR was significantly positively related to BC both premenopausal (adjusted RRpre = 1.04; 95% CI: 0.98-1.11; P = 0.169) and postmenopausal (adjusted RRpost = 1.04; 95% CI: 1.02-1.07; P = 0.002). Regarding molecular subtype, central obesity was significantly associated with HR+ (adjusted ORWC = 1.13; 95% CI: 1.07-1.19; P < 0.001, adjusted ORWHR = 1.03; 95% CI: 0.98-1.07; P = 0.244) and HR- BCs (adjusted ORWC =1.11; 95% CI: 0.99-1.24; P = 0.086, adjusted ORWHR =1.01; 95% CI: 0.91-1.13; P = 0.808). Our dose-response analysis revealed a J-shaped trend in the relationship between central obesity and BC (measured by WC and WHR) in case-control studies and an inverted J-shaped trend between BMI (during premenopausal) and BC in the prospective cohort. Conclusion: Central obesity is a risk factor for premenopausal and postmenopausal BC, and WC and WHR may predict it. Regarding the BC subtype, central obesity is proven to be a risk of ER+ and ER- BCs. The dose-response analysis revealed that when BMI (during premenopausal) exceeded 23.40 kg/m2, the risk of BC began to decrease, and WC higher than 83.80 cm or WHR exceeded 0.78 could efficiently increase the BC risk. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022365788.
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Background: Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications. Methods: In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method. Results: Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age. Conclusion: Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.