This study aimed to construct a predictive model for recurrence and metastasis in patients with localized clear cell renal cell carcinoma (ccRCC) based on multiple preoperative blood indexes and oncological characteristics. Patients and Methods: Overall, 442 patients with localized ccRCC between 2013 and 2015 were included. Using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, the top three risk factors from the peripheral blood indicators were screened to construct a risk score, and a prognostic model was established. Harrell's concordance index (C-index) was applied to evaluate the predictive accuracy of the model for predicting disease-free survival (DFS) in ccRCC. Results: Out of 38 blood indexes, the top three predictors were fibrinogen (FIB), C-reactive protein (CRP) and neutrophil-lymphocyte ratio (NLR). The FIB-CRP-NLR (FCN) score (hazard ratio [HR]: 1.86, 95% confidence interval [CI]: 1.21-2.9, P = 0.005) was an independent prognostic factor in multivariate analysis. Furthermore, the FIB-CRP-NLR-T-Grade (FCNTG) risk model combining FCN score, T stage and Furhman grade achieved a higher prognostic accuracy (mean C-index, 0.728) than both the FCN score alone (mean C-index, 0.675) and the stage, size, grade, and necrosis (SSIGN) score (mean C-index, 0.686) in the validation cohort. Conclusion: The FCN score combining peripheral blood indicators of inflammation and coagulation is an independent prognostic marker of ccRCC. The FCNTG model, which systemically incorporates preoperative blood indexes to oncological characteristics, shows its advantages of convenience and high prediction efficiency.

Hypothesis: Nomogram can be built to predict the pathological T3a upstaging from clinical T1a in patients with localized renal cell carcinoma before surgery. Purpose: Renal cell carcinoma (RCC) patients with clinical T1a (cT1a) disease who are upstaged to pathological T3a (pT3a) have reduced survivals after partial nephrectomy. We aimed to develop a nomogram-based model predicting pT3a upstaging in RCC patients with preoperative cT1a based on multiple preoperative blood indexes and oncological characteristics. Materials and Methods: Between 2010 and 2019, 510 patients with cT1a RCC were individually matched according to pT3a upstaging and pathological T1a (pT1a) at a 1:4 ratio using clinicopathologic features. Least absolute shrinkage and selection operator regression analysis was used to identify the most important risk factor from 40 peripheral blood indicators, and a predictive model was established. Multivariate logistic regression analysis was performed with the screened blood parameters and clinical data to identify significant variables. Harrell's concordance index (C-index) was applied to evaluate the accuracy of the model for predicting pT3a upstaging in patients with cT1a RCC. Results: Out of 40 blood indexes, the top ranked predictor was fibrinogen (FIB). Age, the ratio of the tumor maximum and minimum diameter (ROD), FIB, and tumor size were all independent risk factors for pT3a upstaging in multivariate analysis. A predictive ARFS model (Age, ROD, FIB, tumor Size) was established, and the C-index was 0.756 (95% CI, 0.681-0.831) and 0.712 (95% CI, 0.638-0.785) in the training and validation cohorts, respectively. Conclusions: Older age, higher ROD, increased FIB level, and larger tumor size were independent risk factors for upstaging. The ARFS model has a high prediction efficiency for pT3a upstaging in patients with cT1a RCC.

Objectives Metastatic renal cell carcinoma can occur synchronously or metachronously. We characterized the time from diagnosis to systematic therapy as a categorical variable to analyze its effect on the overall survival and first‐line treatment efficacy of metastatic renal cell carcinoma patients. Methods We initially enrolled 949 consecutive metastatic renal cell carcinoma patients treated with targeted therapies retrospectively from December 2005 to December 2019. X‐tile analysis was used to determine cut‐off values of time from diagnosis to systematic therapy referring to overall survival. Patients were divided into different groups based on the time from diagnosis to systematic therapy and then analyzed for survival. Results Of 358 eligible patients with metastatic renal cell carcinoma, 125 (34.9%) had synchronous metastases followed by cytoreductive nephrectomy, and 233 (65.1%) had metachronous metastases. A total of 28 patients received complete metastasectomy. Three optimal cut‐off values for the time from diagnosis to systematic therapy (months) – 1.1, 7.0 and 35.9 – were applied to divide the population into four groups: the synchro group (time from diagnosis to systematic therapy ≤1.0), early group (1.0 < time from diagnosis to systematic therapy ≤ 7.0), intermediate group (7.0 < time from diagnosis to systematic therapy < 36.0) and late group (time from diagnosis to systematic therapy ≥36.0). The targeted therapy‐related overall survival (P < 0.001) and progression‐free survival (P < 0.001) values were significantly different among the four groups. Patients with longer time from diagnosis to systematic therapy had better prognoses and promising efficacy of targeted therapy. With the prolongation of time from diagnosis to systematic therapy, complete metastasectomy was more likely to achieve and bring a better prognosis. Conclusions The time from diagnosis to systematic therapy impacts the survival of metastatic renal cell carcinoma patients treated with targeted therapy. The cutoff points of 1, 7 and 36 months were statistically significant. The statistical boundaries might be valuable in future model establishment.

BackgroundPrevious observational studies have showed that certain psychiatric disorders may be linked to breast cancer risk, there is, however, little understanding of relationships between mental disorders and a variety of breast diseases. This study aims to investigate if mental disorders influence the risks of overall breast cancer, the two subtypes of breast cancer (ER+ and ER-), breast benign tumors and breast inflammatory diseases.MethodsDuring our research, genome-wide association study (GWAS) data for seven psychiatric disorders (schizophrenia, major depressive disorder, bipolar disorder, post-traumatic stress disorder, panic disorder, obsessive-compulsive disorder and anorexia nervosa) from the Psychiatric Genomics Consortium (PGC) and the UK Biobank were selected, and single-nucleotide polymorphisms (SNPs) significantly linked to these mental disorders were identified as instrumental variables. GWAS data for breast diseases came from the Breast Cancer Association Consortium (BCAC) as well as the FinnGen consortium. We performed two-sample Mendelian randomization (MR) analyses and multivariable MR analyses to assess these SNPs’ effects on various breast diseases. Both heterogeneity and pleiotropy were evaluated by sensitivity analyses.ResultsWhen the GWAS data of psychiatric disorders were derived from the PGC, our research found that schizophrenia significantly increased the risks of overall breast cancer (two-sample MR: OR 1.05, 95%CI [1.03-1.07], p = 3.84 × 10−6; multivariable MR: OR 1.06, 95%CI [1.04-1.09], p = 2.34 × 10−6), ER+ (OR 1.05, 95%CI [1.02-1.07], p = 5.94 × 10−5) and ER- (two-sample MR: OR 1.04, 95%CI [1.01-1.07], p = 0.006; multivariable MR: OR 1.06, 95%CI [1.02-1.10], p = 0.001) breast cancer. Nevertheless, major depressive disorder only showed significant positive association with overall breast cancer (OR 1.12, 95%CI [1.04-1.20], p = 0.003) according to the two-sample MR analysis, but not in the multivariable MR analysis. In regards to the remainder of the mental illnesses and breast diseases, there were no significant correlations. While as for the data from the UK Biobank, schizophrenia did not significantly increase the risk of breast cancer.ConclusionsThe correlation between schizophrenia and breast cancer found in this study may be false positive results caused by underlying horizontal pleiotropy, rather than a true cause-and-effect relationship. More prospective studies are still needed to be carried out to determine the definitive links between mental illnesses and breast diseases.

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Background Breast cancer presents as one of the top health threats to women around the world. Myeloid cells are the most abundant cells and the major immune coordinator in breast cancer tumor microenvironment (TME), target therapies that harness the anti-tumor potential of myeloid cells are currently being evaluated in clinical trials. However, the landscape and dynamic transition of myeloid cells in breast cancer TME are still largely unknown. Methods Myeloid cells were characterized in the single-cell data and extracted with a deconvolution algorithm to be assessed in bulk-sequencing data. We used the Shannon index to describe the diversity of infiltrating myeloid cells. A 5-gene surrogate scoring system was then constructed and evaluated to infer the myeloid cell diversity in a clinically feasible manner. Results We dissected the breast cancer infiltrating myeloid cells into 15 subgroups including macrophages, dendritic cells (DCs), and monocytes. Mac_CCL4 had the highest angiogenic activity, Mac_APOE and Mac_CXCL10 were highly active in cytokine secretion, and the DCs had upregulated antigen presentation pathways. The infiltrating myeloid diversity was calculated in the deconvoluted bulk-sequencing data, and we found that higher myeloid diversity was robustly associated with more favorable clinical outcomes, higher neoadjuvant therapy responses, and a higher rate of somatic mutations. We then used machine learning methods to perform feature selection and reduction, which generated a clinical-friendly scoring system consisting of 5 genes (C3, CD27, GFPT2, GMFG, and HLA-DPB1) that could be used to predict clinical outcomes in breast cancer patients. Conclusions Our study explored the heterogeneity and plasticity of breast cancer infiltrating myeloid cells. By using a novel combination of bioinformatic approaches, we proposed the myeloid diversity index as a new prognostic metric and constructed a clinically practical scoring system to guide future patient evaluation and risk stratification. Graphical Abstract