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Objectives: To explore an optimal model to predict the response of patients with axillary lymph node (ALN) positive breast cancer to neoadjuvant chemotherapy (NAC) with machine learning using clinical and ultrasound-based radiomic features. Methods: In this study, 1014 patients with ALN-positive breast cancer confirmed by histological examination and received preoperative NAC in the Affiliated Hospital of Qingdao University (QUH) and Qingdao Municipal Hospital (QMH) were included. Finally, 444 participants from QUH were divided into the training cohort (n = 310) and validation cohort (n = 134) based on the date of ultrasound examination. 81 participants from QMH were used to evaluate the external generalizability of our prediction models. A total of 1032 radiomic features of each ALN ultrasound image were extracted and used to establish the prediction models. The clinical model, radiomics model, and radiomics nomogram with clinical factors (RNWCF) were built. The performance of the models was assessed with respect to discrimination and clinical usefulness. Results: Although the radiomics model did not show better predictive efficacy than the clinical model, the RNWCF showed favorable predictive efficacy in the training cohort (AUC, 0.855; 95% CI 0.817–0.893), the validation cohort (AUC, 0.882; 95% CI 0.834–0.928), and the external test cohort (AUC, 0.858; 95% CI 0.782–0.921) compared with the clinical factor model and radiomics model. Conclusions: The RNWCF, a noninvasive, preoperative prediction tool that incorporates a combination of clinical and radiomics features, showed favorable predictive efficacy for the response of node-positive breast cancer to NAC. Therefore, the RNWCF could serve as a potential noninvasive approach to assist personalized treatment strategies, guide ALN management, avoiding unnecessary ALND. [ABSTRACT FROM AUTHOR]

Background: Epidemiological evidence links a close correlation between long-term exposure to air pollutants and autoimmune diseases, while the causality remained unknown. Methods: Two-sample Mendelian randomization (TSMR) was used to investigate the role of PM10, PM2.5, NO2, and NOX (N = 423,796–456,380) in 15 autoimmune diseases (N = 14,890–314,995) using data from large European GWASs including UKB, FINNGEN, IMSGC, and IPSCSG. Multivariable Mendelian randomization (MVMR) was conducted to investigate the direct effect of each air pollutant and the mediating role of common factors, including body mass index (BMI), alcohol consumption, smoking status, and household income. Transcriptome-wide association studies (TWAS), two-step MR, and colocalization analyses were performed to explore underlying mechanisms between air pollution and autoimmune diseases. Results: In TSMR, after correction of multiple testing, hypothyroidism was causally associated with higher exposure to NO2 [odds ratio (OR): 1.37, p = 9.08 × 10–4] and NOX [OR: 1.34, p = 2.86 × 10–3], ulcerative colitis (UC) was causally associated with higher exposure to NOX [OR: 2.24, p = 1.23 × 10–2] and PM2.5 [OR: 2.60, p = 5.96 × 10–3], rheumatoid arthritis was causally associated with higher exposure to NOX [OR: 1.72, p = 1.50 × 10–2], systemic lupus erythematosus was causally associated with higher exposure to NOX [OR: 4.92, p = 6.89 × 10–3], celiac disease was causally associated with lower exposure to NOX [OR: 0.14, p = 6.74 × 10–4] and PM2.5 [OR: 0.17, p = 3.18 × 10–3]. The risky effects of PM2.5 on UC remained significant in MVMR analyses after adjusting for other air pollutants. MVMR revealed several common mediators between air pollutants and autoimmune diseases. Transcriptional analysis identified specific gene transcripts and pathways interconnecting air pollutants and autoimmune diseases. Two-step MR revealed that POR, HSPA1B, and BRD2 might mediate from air pollutants to autoimmune diseases. POR pQTL (rs59882870, PPH4=1.00) strongly colocalized with autoimmune diseases. Conclusion: This research underscores the necessity of rigorous air pollutant surveillance within public health studies to curb the prevalence of autoimmune diseases. [ABSTRACT FROM AUTHOR]

Background: Immunotherapies targeting glioblastoma (GBM) have led to significant improvements in patient outcomes. TOX is closely associated with the immune environment surrounding tumors, but its role in gliomas is not fully understood.Methods: Using data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), we analyzed the transcriptomes of 1691 WHO grade I-IV human glioma samples. The R language was used to perform most of the statistical analyses. Somatic mutations and somatic copy number variation (CNV) were analyzed using GISTIC 2.0.Results: TOX was down-regulated in malignant gliomas compared to low grade gliomas, and upregulated in the proneural and IDH mutant subtypes of GBM. TOXlow tumours are associated with the loss of PTEN and amplification of EGFR, while TOXhigh tumours harbor frequent mutations in IDH1 (91%). TOX was highly expressed in leading edge regions of tumours. Gene ontology and pathway analyses demonstrated that TOX was enriched in multiple immune related processes including lymphocyte migration in GBM. Finally, TOX had a negative association with the infiltration of several immune cell types in the tumour microenvironment.Conclusion: TOX has the potential to be a new prognostic marker for GBM. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: The cancer-immunity cycle (CI cycle) provides a theoretical framework to illustrate the process of the anticancer immune response. Recently, the update of the CI cycle theory emphasizes the importance of tumor's immunological phenotype. However, there is lack of immunological phenotype of pan-cancer based on CI cycle theory. Methods: Here, we applied a visualizing method termed 'cancer immunogram' to visualize the state of CI cycle of 8460 solid tumors from TCGA cohort. Unsupervised clustering of the cancer immunogram was performed using the nonnegative matrix factorization (NMF) analysis. We applied an evolutionary genomics approach (dN/dS ratio) to evaluate the clonal selection patterns of tumors with distinct immunogram subtypes. Results: We defined four major CI cycle patterns across 32 cancer types using a cancer immunogram approach. Immunogram-I was characterized by 'hot' and 'exhausted' features, indicating a favorable prognosis. Strikingly, immunogram-II, immunogram-III, and immunogram-IV represented distinct immunosuppressive patterns of 'cold' tumor. Immunogram-II was characterized by 'cold' and 'radical' features, which represented increased expression of immune inhibitor molecules and high levels of positive selection, indicating the worst prognosis. Immunogram-III was characterized by 'cold' and 'recognizable' features and upregulated expression of MHC I molecules. Immunogram-IV was characterized by 'cold' and 'inert' features, which represented overall immunosuppression, lower levels of immunoediting and positive selection, and accumulation of more tumor neoantigens. In particular, favorable overall survival was observed in metastatic urothelial cancer patients with immunogram-I and immunogram-IV after immune checkpoint inhibitor (ICI) therapy. Meanwhile, a higher response rate to ICI therapy was observed in metastatic gastric cancer patients with immunogram-I phenotype. Conclusions: Our findings provide new insight into the interaction between immunity and cancer evolution, which may contribute to optimizing immunotherapy strategies. [ABSTRACT FROM AUTHOR]

Background: Accurate etiology diagnosis is crucial for central nervous system infections (CNS infections). The diagnostic value of metagenomic next-generation sequencing (mNGS), an emerging powerful platform, remains to be studied in CNS infections.Methods: We conducted a single-center prospective cohort study to compare mNGS with conventional methods including culture, smear and etc. 248 suspected CNS infectious patients were enrolled and clinical data were recorded.Results: mNGS reported a 90.00% (9/10) sensitivity in culture-positive patients without empirical treatment and 66.67% (6/9) in empirically-treated patients. Detected an extra of 48 bacteria and fungi in culture-negative patients, mNGS provided a higher detection rate compared to culture in patients with (34.45% vs. 7.56%, McNemar test, p < 0.0083) or without empirical therapy (50.00% vs. 25.00%, McNemar test, p > 0.0083). Compared to conventional methods, positive percent agreement and negative percent agreement was 75.00% and 69.11% separately. mNGS detection rate was significantly higher in patients with cerebrospinal fluid (CSF) WBC > 300 * 106/L, CSF protein > 500 mg/L or glucose ratio ≤ 0.3. mNGS sequencing read is correlated with CSF WBC, glucose ratio levels and clinical disease progression.Conclusion: mNGS showed a satisfying diagnostic performance in CNS infections and had an overall superior detection rate to culture. mNGS may held diagnostic advantages especially in empirically treated patients. CSF laboratory results were statistically relevant to mNGS detection rate, and mNGS could dynamically monitor disease progression. [ABSTRACT FROM AUTHOR]

Background: In patients undergoing percutaneous coronary intervention (PCI), contrast-induced acute kidney injury (CIAKI) is a frequent complication, especially in diabetics, and is connected with severe mortality and morbidity in the short and long term. Therefore, we aimed to develop a CIAKI predictive model for diabetic patients. Methods: 3514 patients with diabetes from four hospitals were separated into three cohorts: training, internal validation, and external validation. We developed six machine learning (ML) algorithms models: random forest (RF), gradient-boosted decision trees (GBDT), logistic regression (LR), least absolute shrinkage and selection operator with LR, extreme gradient boosting trees (XGBT), and support vector machine (SVM). The area under the receiver operating characteristic curve (AUC) of ML models was compared to the prior score model, and developed a brief CIAKI prediction model for diabetes (BCPMD). We also validated BCPMD model on the prospective cohort of 172 patients from one of the hospitals. To explain the prediction model, the shapley additive explanations (SHAP) approach was used. Results: In the six ML models, XGBT performed best in the cohort of internal (AUC: 0.816 (95% CI 0.777–0.853)) and external validation (AUC: 0.816 (95% CI 0.770–0.861)), and we determined the top 15 important predictors in XGBT model as BCPMD model variables. The features of BCPMD included acute coronary syndromes (ACS), urine protein level, diuretics, left ventricular ejection fraction (LVEF) (%), hemoglobin (g/L), congestive heart failure (CHF), stable Angina, uric acid (umol/L), preoperative diastolic blood pressure (DBP) (mmHg), contrast volumes (mL), albumin (g/L), baseline creatinine (umol/L), vessels of coronary artery disease, glucose (mmol/L) and diabetes history (yrs). Then, we validated BCPMD in the cohort of internal validation (AUC: 0.819 (95% CI 0.783–0.855)), the cohort of external validation (AUC: 0.805 (95% CI 0.755–0.850)) and the cohort of prospective validation (AUC: 0.801 (95% CI 0.688–0.887)). SHAP was constructed to provide personalized interpretation for each patient. Our model also has been developed into an online web risk calculator. MissForest was used to handle the missing values of the calculator. Conclusion: We developed a novel risk calculator for CIAKI in diabetes based on the ML model, which can help clinicians achieve real-time prediction and explainable clinical decisions. [ABSTRACT FROM AUTHOR]

Background: PolyC-RNA-binding protein 1 (PCBP1) functions as a tumour suppressor and RNA regulator that is downregulated in human cancers. Here, we aimed to reveal the biological function of PCBP1 in lung adenocarcinoma (LUAD).Methods: First, PCBP1 was identified as an important biomarker that maintains LUAD through The Cancer Genome Atlas (TCGA) project screening and confirmed by immunohistochemistry and qPCR. Via colony formation, CCK8, IncuCyte cell proliferation, wound healing and Transwell assays, we confirmed that PCBP1 was closely related to the proliferation and migration of LUAD cells. The downstream gene DKK1 was discovered by RNA sequencing of PCBP1 knockdown cells. The underlying mechanisms were further investigated using western blot, qPCR, RIP, RNA pulldown and mRNA stability assays.Results: We demonstrate that PCBP1 is downregulated in LUAD tumour tissues. The reduction in PCBP1 promotes the proliferation, migration and invasion of LUAD in vitro and in vivo. Mechanistically, the RNA-binding protein PCBP1 represses LUAD by stabilizing DKK1 mRNA. Subsequently, decreased expression of the DKK1 protein relieves the inhibitory effect on the Wnt/β-catenin signalling pathway. Taken together, these results show that PCBP1 acts as a tumour suppressor gene, inhibiting the tumorigenesis of LUAD.Conclusions: We found that PCBP1 inhibits LUAD development by upregulating DKK1 to inactivate the Wnt/β-catenin pathway. Our findings highlight the potential of PCBP1 as a promising therapeutic target. [ABSTRACT FROM AUTHOR]