UTRN as a potential biomarker in breast cancer: a comprehensive bioinformatics and in vitro study.

Utrophin (UTRN), known as a tumor suppressor, potentially regulates tumor development and the immune microenvironment. However, its impact on breast cancer's development and treatment remains unstudied. We conducted a thorough examination of UTRN using both bioinformatic and in vitro experiments in this study. We discovered UTRN expression decreased in breast cancer compared to standard samples. High UTRN expression correlated with better prognosis. Drug sensitivity tests and RT-qPCR assays revealed UTRN's pivotal role in tamoxifen resistance. Furthermore, the Kruskal-Wallis rank test indicated UTRN's potential as a valuable diagnostic biomarker for breast cancer and its utility in detecting T stage of breast cancer. Additionally, our results demonstrated UTRN's close association with immune cells, inhibitors, stimulators, receptors, and chemokines in breast cancer (BRCA). This research provides a novel perspective on UTRN's role in breast cancer's prognostic and therapeutic value. Low UTRN expression may contribute to tamoxifen resistance and a poor prognosis. Specifically, UTRN can improve clinical decision-making and raise the diagnosis accuracy of breast cancer.

Development and testing of a random forest-based machine learning model for predicting events among breast cancer patients with a poor response to neoadjuvant chemotherapy.

BACKGROUND: Breast cancer (BC) is the most common malignant tumor around the world. Timely detection of the tumor progression after treatment could improve the survival outcome of patients. This study aimed to develop machine learning models to predict events (defined as either (1) the first tumor relapse locally, regionally, or distantly; (2) a diagnosis of secondary malignant tumor; or (3) death because of any reason.) in BC patients post-treatment. METHODS: The patients with the response of stable disease (SD) and progressive disease (PD) after neoadjuvant chemotherapy (NAC) were selected. The clinicopathological features and the survival data were recorded in 1 year and 5 years, respectively. Patients were randomly divided into the training set and test set in the ratio of 8:2. A random forest (RF) and a logistic regression were established in both of 1-year cohort and the 5-year cohort. The performance was compared between the two models. The models were validated using data from the Surveillance, Epidemiology, and End Results (SEER) database. RESULTS: A total of 315 patients were included. In the 1-year cohort, 197 patients were divided into a training set while 87 were into a test set. The specificity, sensitivity, and AUC were 0.800, 0.833, and 0.810 in the RF model. And 0.520, 0.833, and 0.653 of the logistic regression. In the 5-year cohort, 132 patients were divided into the training set while 33 were into the test set. The specificity, sensitivity, and AUC were 0.882, 0.750, and 0.829 in the RF model. And 0.882, 0.688, and 0.752 of the logistic regression. In the external validation set, of the RF model, the specificity, sensitivity, and AUC were 0.765, 0.812, and 0.779. Of the logistics regression model, the specificity, sensitivity, and AUC were 0.833, 0.376, and 0.619. CONCLUSION: The RF model has a good performance in predicting events among BC patients with SD and PD post-NAC. It may be beneficial to BC patients, assisting in detecting tumor recurrence.

Comparison of the pCR Rate and DFS Among Breast Cancer Patients with Different Hormone Receptor and HER2 Statuses.

Background: Recent studies have investigated the features of breast cancer (BC) with low human epidermal growth factor receptor 2 (HER2) expression or HER2-0 expression. However, the results were inconsistent. In this study, we investigated the differences in the pathological complete response (pCR) rate and disease-free survival (DFS) between HER2-low and HER2-0 BC patients and between subgroups. Methods: HER2-negative BC patients who received neoadjuvant chemotherapy between January 2013 and December 2019 in our hospital were retrospectively reviewed. First, the pCR rate and DFS were compared between HER2-low and HER2-0 patients and among different hormone receptor (HR) and HER2 statuses. Subsequently, DFS was compared between different HER2 status populations with or without pCR. Finally, a Cox regression model was used to identify the prognostic factors. Results: Overall, 693 patients were selected: 561 were HER2-low, and 132 were HER2-0. Between the two groups, there were significant differences in N stage (P = 0.008) and HR status (P = 0.007). No significant difference in the pCR rate (12.12% vs 14.39%, P = 0.468) or DFS was observed, independent of HR status. HR+/HER2-low patients had a significantly worse pCR rate (P < 0.001) and longer DFS (P < 0.001) than HR-/HER2-low or HER2-0 patients. In addition, a longer DFS was found in HER2-low patients versus HER2-0 patients among those who did not achieve pCR. Cox regression showed that N stage and HR status were prognostic factors in the overall and HER2-low populations, while no prognostic factor was found in the HER2-0 group. Conclusion: This study suggested that HER2 status is not associated with the pCR rate or DFS. Longer DFS was found only among patients who did not achieve pCR in the HER2-low versus HER2-0 population. We speculated that the interaction of HR and HER2 might have played a crucial role in this process.

Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy.

PURPOSE: While a pathologic complete response (pCR) is regarded as a surrogate endpoint for pos-itive outcomes in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC), fore-casting the prognosis of non-pCR patients is still an open issue. This study aimed to create and evaluate nomogram models for estimating the likelihood of disease-free survival (DFS) for non-pCR patients. METHODS: A retrospective analysis of 607 non-pCR BC patients was conducted (2012-2018). After converting continuous variables to categorical variables, variables entering the model were progressively identified by univariate and multivariate Cox regression analyses, and then pre-NAC and post-NAC nomogram models were developed. Regarding their discrimination, ac-curacy, and clinical value, the performance of the models was evaluated by internal and external validation. Two risk assessments were performed for each patient based on two models; patients were separated into different risk groups based on the calculated cut-off values for each model, including low-risk (assessed by the pre-NAC model) to low-risk (assessed by the post-NAC model), high-risk to low-risk, low-risk to high-risk, and high-risk to high-risk groups. The DFS of different groups was assessed using the Kaplan-Meier method. RESULTS: Both pre-NAC and post-NAC nomogram models were built with clinical nodal (cN) status and estrogen receptor (ER), Ki67, and p53 status (all p < 0.05), showing good discrimination and calibration in both internal and external validation. We also assessed the performance of the two models in four subtypes, with the tri-ple-negative subtype showing the best prediction. Patients in the high-risk to high-risk subgroup have significantly poorer survival rates (p < 0.0001). CONCLUSION: Two robust and effective nomo-grams were developed to personalize the prediction of DFS in non-pCR BC patients treated with NAC.

Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer.

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.

The impact of postmastectomy radiotherapy on cT1-2N1 breast cancer patients with ypN0 after neoadjuvant chemotherapy: a retrospective study based on real-world data.

BACKGROUND: The role of postmastectomy radiation therapy (PMRT) in clinical T1-2N1 breast cancer patients who achieve axillary pathological complete response (ypN0) after neoadjuvant chemotherapy (NAC) is controversial. METHODS: Data from cT1-2N1 breast cancer patients who converted to ypN0 after NAC and subsequent surgery were retrospectively analyzed. Disease-free survival (DFS) and overall survival (OS) were estimated using the Kaplan‒Meier method. Univariate and multivariate Cox regression models were applied to investigate the correlations between clinical or pathological parameters and survival. RESULTS: From 2012-2019, we identified 116 cases for analysis, including 31 (26.7%) who received PMRT and 85 (73.3%) who did not. At a median follow-up time of 56.4 months, the 5-year DFS and OS rates were 90.2% and 96.7% with PMRT and 93.7% and 97.3% without PMRT, respectively. PMRT did not affect either DFS (p = 0.234) or OS (p = 0.878). On multivariate analyses, no differences in DFS or OS between the two groups were detected, taking into consideration the following factors: age, molecular subtype, Ki67 index, cT stage, and in-breast pathologic complete response (DFS: HR 2.260; 95% CI 0.465-10.982; p = 0.312. OS: HR 1.400; 95% CI 0.138-14.202; p = 0.776). This nonsignificant difference was also consistent in subgroup analyses (all p > 0.05). CONCLUSIONS: PMRT has limited ability to confer DFS or OS benefits for cT1-2N1 breast cancer patients who achieved axillary pathological complete response after NAC and total mastectomy. It is imperative to conduct prospective studies to investigate the safety and feasibility of omitting PMRT. TRIAL REGISTRATION: This research was approved by the Ethics Committee of The First Affiliated Hospital of Chongqing Medical University (ID: No. 2021-442).

Association of serum reproductive hormones changes after neoadjuvant chemotherapy with hormone receptors expression alterations and survival outcomes in breast cancer.

Purpose: This study aimed to determine the effect of neoadjuvant chemotherapy (NAC) on circulating levels of reproductive hormones and evaluate the correlation of hormone changes after NAC with hormone receptors expression alterations and relapse-free survival (RFS) outcomes in breast cancer. Methods: Information from 181 breast cancer patients who received NAC was retrospectively analyzed. For hormones parameters, the median and interquartile range (IQR) were provided at baseline and the end of NAC then was compared by Wilcoxon signed-rank test. Categorical variables were represented as numbers and percentages and were compared via two-sided chi-square and Fisher's tests. The RFS outcomes were compared between patients according to hormone changes using the log-rank test. Univariate and multivariate survival analyses with hazard ratios (HR) and 95% confidence intervals (95% CI) were carried out using Cox regression. Results: Sex steroids including estradiol, progesterone, testosterone, and dehydroepiandrosterone sulfate (DHEAS) levels decreased significantly after NAC among both premenopausal and postmenopausal patients (all P < 0.05). Decreased estradiol levels were associated with reduced progesterone receptor (PR) expression (P = 0.030). In multivariate survival analysis, the non-decreased progesterone level was strongly associated with worse RFS (non-decreased vs. decreased, HR = 7.178, 95% CI 2.340-22.019, P = 0.001). Patients with decreased progesterone levels exhibited better 3-year RFS compared with those with non-decreased (87.6% vs. 58.3%, log-rank, P = 0.001). Conclusion: Multiple reproductive hormone levels were influenced by NAC. The change in estradiol level had a positive connection with PR expression alteration. Furthermore, an inverse association between the change in progesterone level and RFS outcomes was found. These findings may provide a theoretical basis for pre-operative endocrine therapy combined with NAC in breast cancer patients.

Cuprotosis-Related Genes: Predicting Prognosis and Immunotherapy Sensitivity in Pancreatic Cancer Patients.

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.

Construction and Validation of Angiogenesis-Related Prognostic Risk Signature to Facilitate Survival Prediction and Biomarker Excavation of Breast Cancer Patients.

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.

Breast Metastasis From Rectal Signet-Ring Cell Carcinoma: A Case Report and Review of Literature.

Background: Metastatic 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. Methods: We 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. Results: A 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. Conclusion: Previous 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.

Pre-treatment circulating reproductive hormones levels predict pathological and survival outcomes in breast cancer submitted to neoadjuvant chemotherapy.

PURPOSE: This study aimed to evaluate the correlation of pre-treatment circulating reproductive hormones levels with pathological and survival outcomes in breast cancer patients received neoadjuvant chemotherapy (NAC). METHODS: Information from 196 premenopausal and 137 postmenopausal breast cancer patients who received NAC were retrospectively analyzed. Treatment response to NAC, with odds ratios (OR) and 95% confidence intervals (95% CI) was estimated using logistic regression adjusted for key confounders. Survival outcomes with hazard ratios (HR) and 95% CI were estimated using Cox regression adjusted for key confounders. The Kaplan-Meier method was applied in the survival analysis. RESULTS: Premenopausal patients with lower testosterone levels (OR = 0.996, 95% CI 0.992-0.999, P = 0.026), and postmenopausal patients with higher follicle-stimulating hormone (FSH) levels (OR = 1.045, 95% CI 1.014-1.077, P = 0.005) were likely to achieve pathological complete response (pCR). In multivariate survival analysis, the lowest tertile (T) progesterone was associated with worse overall survival (OS) in premenopausal patients (T2 vs T1, HR = 0.113, 95% CI 0.013-0.953, P = 0.045; T3 vs T1, HR = 0.109, 95% CI 0.013-0.916, P = 0.041). Premenopausal patients with the lowest tertile progesterone exhibited worse 3-year OS compared with those with higher tertiles (72.9% vs 97.4%, log-rank, P = 0.007). CONCLUSION: Pre-treatment testosterone and FSH are significant independent predictors for pCR to NAC in premenopausal and postmenopausal patients, respectively. Low progesterone levels are correlated with worse OS in premenopausal patients. These findings may provide a theoretical basis for pre-operative endocrine therapy combined with NAC in breast cancer.

Plasma fibrinogen acts as a predictive factor for pathological complete response to neoadjuvant chemotherapy in breast cancer: a retrospective study of 1004 Chinese breast cancer patients.

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.

KLHL5 Is a Prognostic-Related Biomarker and Correlated With Immune Infiltrates in Gastric Cancer.

Background: KLHL5 (Kelch Like Family Member 5) is differentially expressed in gastric cancer, but its correlation with prognosis and functioning mechanism in gastric cancer remain unclear. Methods: The Oncomine database and TIMER were employed to appraise the KLHL5 expression in a variety of cancers. The correlation between KLHL5 expression and patient prognosis was extracted from the Kaplan-Meier plotter, GEPIA, and PrognoScan database. Then the relationship between KLHL5 expression and inflammatory infiltrate profiles was inquired by TIMER. Finally, GEPIA and TIMER were explored for the correlative significance between KLHL5 expression and immune cell-related marker sets. Results: KLHL5 was found to be differentially expressed and correlated with clinical outcomes in several types of cancers in the TCGA database. Especially, KLHL5 mRNA expression was upregulated and correlated with poorer overall survival and progression-free survival in gastric cancer. Moreover, elevated KLHL5 expression was significantly related with patient node stage, infiltration level, and expression of multiple immune marker sets. Conclusions: These results implicate that KLHL5 expression is closely linked with patient clinical outcomes and the microenvironmental infiltration level in different neoplasms. This indicates that KLHL5 is a modulator in infiltrate recruitment, shaping the landscape of immune cell infiltration. Thus, it represents an eligible prognostic predictor for gastric malignancy.