This study is aimed at exploring the potential mechanism of angiogenesis, a biological process-related gene in breast cancer (BRCA), and constructing a risk model related to the prognosis of BRCA patients. We used multiple bioinformatics databases and multiple bioinformatics analysis methods to complete our exploration in this research. First, we use the RNA-seq transcriptome data in the TCGA database to conduct a preliminary screening of angiogenesis-related genes through univariate Cox curve analysis and then use LASSO regression curve analysis for secondary screening. We successfully established a risk model consisting of seven angiogenesis-related genes in BRCA. The results of ROC curve analysis show that the risk model has good prediction accuracy. We can successfully divide BRCA patients into the high-risk and low-risk groups with significant prognostic differences based on this risk model. In addition, we used angiogenesis-related genes to perform cluster analysis in BRCA patients and successfully divided BRCA patients into three clusters with significant prognostic differences, namely, cluster 1, cluster 2, and cluster 3. Subsequently, we combined the clinical-pathological data for correlation analysis, and there is a significant correlation between the risk model and the patient’s T and stage. Multivariate Cox regression curve analysis showed that the age of BRCA patients and the risk score of the risk model could be used as independent risk factors in the progression of BRCA. In particular, based on this angiogenesis-related risk model, we have drawn a matching nomogram that can predict the 5-, 7-, and 10-year overall survival rates of BRCA patients. Subsequently, we performed a series of pan-cancer analyses of CNV, SNV, OS, methylation, and immune infiltration for this risk model gene and used GDSC data to explore drug sensitivity. Subsequently, to gain insight into the protein expression of these risk model genes in BRCA, we used the immunohistochemical data in the THPA database for verification. The results showed that the protein expressions of IL18, RUNX1, SCG2, and THY1 molecules in BRCA tissues were significantly higher than those in normal breast tissues, while the protein expressions of PF4 and TNFSF12 molecules in BRCA tissues were significantly lower than those in normal breast tissues. Finally, we conducted multiple GSEA analyses to explore the biological pathways these risk model genes can cross in cancer progression. In summary, we believe that this study can provide valuable data and clues for future studies on angiogenesis in BRCA.
Background The aim of this study was to evaluate the relationship between pre-treatment plasma fibrinogen (Fib) level and pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and to assess the role of plasma Fib as a predictive factor. Methods Data from 1004 consecutive patients with invasive breast cancer who received NAC and subsequent surgery were retrospectively analysed. Both univariate and multivariate analyses based on logistic regression model were performed to identify clinicopathological factors associated with pCR to NAC. Cox regression model was used to determine the correlation between clinical or pathological parameters and recurrence-free survival (RFS). The Kaplan-Meier method and the log-rank test were applied in the survival analysis. Results The median value of Fib, rather than other plasma coagulation parameters, was significantly increased in non-pCR patients compared with pCR patients (P = 0.002). Based on the cut-off value estimated by the receiver operating characteristic (ROC) curve analysis, patients were divided into low or high Fib groups (Fib < 3.435 g/L or ≥ 3.435 g/L). Low Fib levels were significantly associated with premenopausal or perimenopausal status (P < 0.001), tumour size ≤5 cm (P = 0.002), and positive hormone receptor status (P = 0.002). After adjusted for other clinicopathological factors in the multivariate logistic regression model, low Fib status was strongly associated with pCR to NAC (OR = 3.038, 95% CI 1.667–5.537, P < 0.001). Survival analysis showed that patients with low Fib levels exhibited better 3-year RFS compared with patients with high Fib levels in the tumour size>5 cm group (77.5% vs 58.4%, log-rank, P = 0.0168). Conclusions This study demonstrates that low pre-treatment plasma Fib (Fib < 3.435 g/L) is an independent predictive factor for pCR to NAC in breast cancer patients. Moreover, T3-featured breast cancer patients with lower Fib level exhibit better RFS outcomes after NAC compared with high Fib status.
Based on TCGA, GTEx, and TIMER databases and various bioinformatics analysis methods, the potential biological roles of cuprotosis-related genes in pancreatic cancer were deeply explored, and a predictive model for pancreatic cancer patients was constructed. We downloaded the RNA-Seq data and clinicopathological and predictive data of 179 pancreatic cancer tissues and 332 adjacent normal tissues from TCGA and GTEx databases. The differential expression of cuprotosis-related genes in pancreatic cancer tissue and adjacent normal tissue was analyzed, and the LASSO regression algorithm was used to construct a prediction model and verify the validity of the model prediction. Based on the LASSO regression algorithm, a predictive model composed of three genes LIPT1, LIAS, and DLAT was screened. The corresponding survival curves showed that the constructed prediction model could significantly distinguish the prognosis of pancreatic cancer patients, and the prognosis of patients in the high-risk group was worse ( P = 0.00557). The ROC curve showed that the area under the curve of the predictive model for predicting the 4-, 5-, and 6-year survival rates in pancreatic cancer was 0.816, 0.836, and 0.956, respectively. The AUC value of this risk model was significantly higher than 0.7, which could more accurately predict the prognosis of pancreatic cancer patients. This study determined a risk-scoring model of cuprotosis-related genes, which can provide an essential basis for judging the prognosis of pancreatic cancer patients.
BackgroundMetastatic rectal cancer (mRC) of the breast is an extremely rare clinical situation. There are few reported cases in domestic or foreign literature. The clinicopathologic characteristics along with the diagnostic and therapeutic strategies of such cases remain relatively unclear. Here, we would like to provide our comprehensive insights into this rare entity.MethodsWe present a case that till now is the first reported breast metastasis from rectal cancer pathologically diagnosed as a signet-ring cell carcinoma, and we review the current literature on this rare event. The detailed clinical data, histopathology, management, and follow-up aspects were gathered for analysis.ResultsA total of 15 cases were collected including the current case. Breast metastases from rectal cancer present at an average age of 47.7 years (range, 28 to 69 years) and appear with an average interval of 28.4 months (range, 5 months to 18 years) following primary tumor diagnoses. Of the 15 cases, 8 and 5 are pathologically diagnosed as adenocarcinomas and mucinous adenocarcinomas, respectively. Most cases (11/15) are accompanied by extramammary metastases. About half of the breast metastases (7/15) were to the left. In all cases, the main complaints were palpable mass. The average maximum diameter of the metastatic mass is 2.7 cm (range, 1–11 cm). The majority (8/12) of cases with accessible therapy information exclude the option of local surgery.ConclusionPrevious cancer history and accurate immunohistochemistry data play critical roles to distinguish mammary metastasis from a primary neoplasm of the breast. Mastectomy and molecular-targeted drugs should be considered with priority if systemic condition supports them.
Purpose: Pathological complete response (pCR), the goal of NAC, is considered a surrogate for favorable outcomes in breast cancer (BC) patients administrated neoadjuvant chemotherapy (NAC). This study aimed to develop and assess a novel nomogram model for predicting the probability of pCR based on the core biopsy. Methods: This was a retrospective study involving 920 BC patients administered NAC between January 2012 and December 2018. The patients were divided into a primary cohort (769 patients from January 2012 to December 2017) and a validation cohort (151 patients from January 2017 to December 2018). After converting continuous variables to categorical variables, variables entering the model were sequentially identified via univariate analysis, a multicollinearity test, and binary logistic regression analysis, and then, a nomogram model was developed. The performance of the model was assessed concerning its discrimination, accuracy, and clinical utility. Results: The optimal predictive threshold for estrogen receptor (ER), Ki67, and p53 were 22.5%, 32.5%, and 37.5%, respectively (all p < 0.001). Five variables were selected to develop the model: clinical T staging (cT), clinical nodal (cN) status, ER status, Ki67 status, and p53 status (all p ≤ 0.001). The nomogram showed good discrimination with the area under the curve (AUC) of 0.804 and 0.774 for the primary and validation cohorts, respectively, and good calibration. Decision curve analysis (DCA) showed that the model had practical clinical value. Conclusions: This study constructed a novel nomogram model based on cT, cN, ER status, Ki67 status, and p53 status, which could be applied to personalize the prediction of pCR in BC patients treated with NAC.
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