Incretin-based drugs decrease the incidence of prostate cancer in type 2 diabetics: A pooling-up analysis.

Incretin-based drugs, a class of Antidiabetic medications (ADMs) used in the treatment of type 2 diabetes, may affect the incidence of prostate cancer (PCa). But real-world evidence for this possible effect is lacking. Therefore, the aim of this study is to assess the effect of incretin-based drugs on the incidence of PCa, including glucagon-like peptide-1 (GLP-1) receptor agonists and dipeptidyl peptidase-4 (DPP-4) inhibitors. We searched PubMed, Embase, and Cochrane Library databases for eligible studies through September 2023. Two independent reviewers performed screening and data extraction. We used the Cochrane Handbook for Systematic Reviews and the Newcastle-Ottawa Scale (NOS) to assess the quality of included randomized controlled trials (RCTs) and cohort studies. We did a meta-analysis of available trial data to calculate overall risk ratios (RRs) for PCa. A total of 1238 articles were identified in our search. After screening for eligibility, 7 high-quality studies met the criteria for meta-analysis, including 2 RCTs and 5 cohort studies, with a total of 1165,738 patients. Compared with the control group, we found that incretin-based drugs reduced the relative risk of PCa by 35% (95% confidence interval (CI), 0.17-0.49; P = .0006). In subgroup analysis, the RR values for GLP-1 receptor agonists and DPP-4 inhibitors were 62% (95% CI, 0.45-0.85; P = .003) and 72% (95% CI, 0.46-1.12; P = .14), respectively. Incretin-based drugs are associated with lower incidence of prostate cancer and may have a preventive effect on prostate cancer in patients with type 2 diabetes.

Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms.

Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.

Additional prognostic value of polymorphisms within the 3'-untranslated region of programmed cell death pathway genes in early-stage breast cancer.

Introduction: The programmed cell death (PCD) pathway plays an important role in restricting cancer cell survival and proliferation. However, limited studies have investigated the association between genetic variants in the 3'-untranslated region of the PCD pathway genes and breast cancer outcomes. Methods: In this study, we genotyped 28 potentially functional single nucleotide polymorphisms (SNPs) in 23 PCD pathway genes in 1,177 patients with early-stage breast cancer (EBC) from a Han Chinese population. The median follow-up period was 174 months. Results: Among all the candidate SNPs, four independent SNPs (rs4900321 and rs7150025 in ATG2B, rs6753785 in BCL2L11, and rs2213181 in c-Kit) were associated with invasive disease-free survival (iDFS), distant disease-free survival (DDFS), breast cancer-specific survival (BCSS) and overall survival (OS), respectively. Further combined genotypes of these four SNPs revealed that the survival decreased as the number of unfavorable genotypes increased (Ptrend = 1.0 × 10-6, 8.5 × 10-8, 3.6 × 10-4, and 1.3 × 10-4 for iDFS, DDFS, BCSS, and OS, respectively). Receiver operating characteristic curve analysis demonstrated that incorporating unfavorable genotypes and clinicopathological variables improved the ability to predict EBC survival (P = 0.006, 0.004, 0.029, and 0.019 for iDFS, DDFS, BCSS, and OS, respectively). Additionally, rs6753785 and rs2213181 were associated with BCL2L11 and c-Kit mRNA expression, respectively. Conclusions: Our results suggest that these four SNPs may act as novel biomarkers for EBC survival, possibly by modulating the expression of the corresponding genes.

Clinical outcomes of coronavirus disease in patients with breast cancer treated with granulocyte colony-stimulating factor following chemotherapy: Triangulation of evidence using population-based cohort and Mendelian randomization analyses.

Recombinant human granulocyte colony-stimulating factor (G-CSF) administration in patients with cancer and coronavirus disease (COVID-19) remains controversial. Concerns exist that it may worsen COVID-19 outcomes by triggering an inflammatory cytokine storm, despite its common use for managing chemotherapy-induced neutropenia (CIN) or febrile neutropenia post-chemotherapy. Here, we determined whether prophylactic or therapeutic G-CSF administration following chemotherapy exacerbates COVID-19 progression to severe/critical conditions in breast cancer patients with COVID-19. Between December 2022 and February 2023, all 503 enrolled breast cancer patients had concurrent COVID-19 and received G-CSF post-chemotherapy, with most being vaccinated pre-chemotherapy. We prospectively observed COVID-19-related adverse outcomes, conducted association analyses, and subsequently performed Mendelian randomization (MR) analyses to validate the causal effect of genetically predicted G-CSF or its associated granulocyte traits on COVID-19 adverse outcomes. Only 0.99% (5/503) of breast cancer patients experienced COVID-19-related hospitalization following prophylactic or therapeutic G-CSF administration after chemotherapy. No mortality or progression to severe/critical COVID-19 occurred after G-CSF administration. Notably, no significant associations were observed between the application, dosage, or response to G-CSF and COVID-19-related hospitalization (all p >.05). Similarly, the MR analyses showed no evidence of causality of genetically predicted G-CSF or related granulocyte traits on COVID-19-related hospitalization or COVID-19 severity (all p >.05). There is insufficient evidence to substantiate the notion that the prophylactic or therapeutic administration of G-CSF after chemotherapy for managing CIN in patients with breast cancer and COVID-19 would worsen COVID-19 outcomes, leading to severe or critical conditions, or even death, especially considering the context of COVID-19 vaccination.

PhosMap: An ensemble bioinformatic platform to empower interactive analysis of quantitative phosphoproteomics.

BACKGROUND: Liquid chromatography-mass spectrometry (LC-MS)-based quantitative phosphoproteomics has been widely used to detect thousands of protein phosphorylation modifications simultaneously from the biological specimens. However, the complicated procedures for analyzing phosphoproteomics data has become a bottleneck to widening its application. METHODS: Here, we develop PhosMap, a versatile and scalable tool to accomplish phosphoproteomics data analysis. A standardized phosphorylation data format was created for data analyses, from data preprocessing to downstream bioinformatic analyses such as dimension reduction, differential phosphorylation analysis, kinase activity, survival analysis, and so on. For better usability, we distribute PhosMap as a Docker image for easy local deployment upon any of Windows, Linux, and Mac system. RESULTS: The source code is deposited at https://github.com/BADD-XMU/PhosMap. A free PhosMap webserver (https://huggingface.co/spaces/Bio-Add/PhosMap), with easy-to-follow fashion of dashboards, is curated for interactive data analysis. CONCLUSIONS: PhosMap fills the technical gap of large-scale phosphorylation research by empowering researchers to process their own phosphoproteomics data expediently and efficiently, and facilitates better data interpretation.

An Energy-Efficient FD-fNIRS Readout Circuit Employing a Mixer-First Analog Frontend and a Σ-Δ Phase-to-Digital Converter.

This paper presents a low-power frequency-domain functional near-infrared spectroscopy (FD-fNIRS) readout circuit for the absolute value measurement of tissue optical characteristics. The paper proposes a mixer-first analog front-end (AFE) structure and a 1-bit Σ-Δ phase-to-digital converter (PDC) to reduce the required circuit bandwidth and the laser modulation frequency, thereby saving power while maintaining high resolution. The proposed chip achieves sub-0.01° phase resolution and consumes 6.8 mW of power. Nine optical solid phantoms are produced to evaluate the chip. Compared to a self-built high-precision measurement platform that combines a network analyzer with an avalanche photodiode (APD) module, the maximum measuring errors of the absorption coefficient and reduced scattering coefficient are 10.6% and 12.3%, respectively.

Healthy lifestyles, systemic inflammation and breast cancer risk: a mediation analysis.

BACKGROUND: Despite the known association between healthy lifestyles and reduced risk of breast cancer, it remains unclear whether systemic inflammation, as a consequence of unhealthy lifestyles, may mediate the association. METHODS: A cohort study of 259,435 female participants in the UK Biobank was conducted to estimate hazard ratio (HR) for breast cancer according to 9 inflammation markers using Cox regression models. We further estimated the percentage of total association between healthy lifestyle index (HLI) and breast cancer that is mediated by these inflammation markers. RESULTS: During 2,738,705 person-years of follow-up, 8,889 cases of breast cancer were diagnosed among 259,435 women in the UK Biobank cohort. Higher level of C-reactive protein (CRP), systemic immune-inflammation index (SII), CRP-to-albumin Ratio (CAR), CRP-to-lymphocyte Ratio (CLR), monocyte-to-HDL-c ratio (MHR), and neutrophil-to-HDL-c ratio (NHR) were associated with increased breast cancer risk, while a higher lymphocyte-to-monocyte ratio (LMR) was associated with a lower risk. The inverse association between HLI and breast cancer was weakly mediated by CRP (8.5%), SII (1.71%), CAR (8.66%), CLR (6.91%), MHR (6.27%), and NHR (7.33%). When considering individual lifestyle factors, CRP and CAR each mediated 16.58% and 17.20%, respectively, of the associations between diet score and breast cancer risk, while the proportion mediated for physical activity and breast cancer were 12.13% and 11.48%, respectively. Furthermore, MHR was found to mediate 13.84% and 12.01% of the associations between BMI, waist circumference, and breast cancer. CONCLUSION: The association of HLI and breast cancer is weakly mediated by the level of inflammation, particularly by CRP and CAR. Systemic inflammatory status may be an intermediate in the biological pathway of breast cancer development.

SIMarker: Cellular similarity detection and its application to diagnosis and prognosis of liver cancer.

BACKGROUND: The emergence of single-cell technology offers a unique opportunity to explore cellular similarity and heterogeneity between precancerous diseases and solid tumors. However, there is lacking a systematic study for identifying and characterizing similarities at single-cell resolution. METHODS: We developed SIMarker, a computational framework to detect cellular similarities between precancerous diseases and solid tumors based on gene expression at single-cell resolution. Taking hepatocellular carcinoma (HCC) as a case study, we quantified the cellular and molecular connections between HCC and cirrhosis. Core analysis modules of SIMarker is publicly available at https://github.com/xmuhuanglab/SIMarker ("SIM" means "similarity" and "Marker" means "biomarkers). RESULTS: We found PGA5+ hepatocytes in HCC showed cirrhosis-like characteristics, including similar transcriptional programs and gene regulatory networks. Consequently, the genes constituting the gene expression program of these cirrhosis-like subpopulations were designated as cirrhosis-like signatures (CLS). Strikingly, our utilization of CLS enabled the development of diagnosis and prognosis biomarkers based on within-sample relative expression orderings of gene pairs. These biomarkers achieved high precision and concordance compared with previous studies. CONCLUSIONS: Our work provides a systematic method to investigate the clinical translational significance of cellular similarities between HCC and cirrhosis, which opens avenues for identifying similar paradigms in other categories of cancers and diseases.

Comparative efficacy and safety of extended adjuvant endocrine therapy for hormone receptor-positive early breast cancer: a Bayesian network meta-analysis.

PURPOSE: Optimal extended adjuvant endocrine therapy (ET) duration and strategy for hormone receptor-positive (HR +) early breast cancer remain unclear. In this network meta-analysis (NMA), the efficacy and safety of all available extended adjuvant ETs were compared and ranked. METHODS: PubMed, Embase, and Cochrane Library and abstracts presented at ASCO, SABCS, and ESMO were searched on March 5, 2022. Fourteen randomized controlled trials (RCTs) comprising eight extended adjuvant ETs for HR + breast cancer and 38,070 patients were analyzed. Main outcomes were disease-free survival (DFS), overall survival (OS), grade ≥ 3 adverse events (AEs), and contralateral breast cancer (CBC). Direct and indirect comparisons were integrated via Bayesian NMA. Hierarchical cluster analysis was performed to jointly rank efficacy and safety outcomes. RESULTS: Compared with that of 5 year ET, extended 10 year aromatase inhibitor (AI) treatment provided the greatest DFS benefit (HR = 0.45, 95%CrI 0.23-0.83), whereas no strategy differed significantly in terms of the other main outcomes. Extended 10 year AI treatment was the preferred strategy for DFS improvement and CBC prevention (surface under the cumulative ranking curve: 93.51% and 91.29% probability, respectively). All strategies had comparable safeties (grade ≥ 3 AEs). Compared with that of 5 year ET, 10 year extended AI significantly increased arthralgia (OR = 1.65, 95%CrI 1.02-2.93) and osteoporosis (OR = 3.33, 95%CrI 1.19-9.68). CONCLUSION: Extended 10 year AI therapy may be optimal for HR + early breast cancer given its relatively high efficacy and safety.

Union With Recursive Feature Elimination: A Feature Selection Framework to Improve the Classification Performance of Multicategory Causes of Death in Colorectal Cancer.

Despite the use of machine learning tools, it is challenging to properly model cause-specific deaths in colorectal cancer (CRC) patients and choose appropriate treatments. Here, we propose an interesting feature selection framework, namely union with recursive feature elimination (U-RFE), to select the union feature sets that are crucial in CRC progression-specific mortality using The Cancer Genome Atlas (TCGA) dataset. Based on the union feature sets, we compared the performance of 5 classification algorithms, including logistic regression (LR), support vector machines (SVM), random forest (RF), eXtreme gradient boosting (XGBoost), and Stacking, to identify the best model for classifying 4-category deaths. In the first stage of U-RFE, LR, SVM, and RF were used as base estimators to obtain subsets containing the same number of features but not exactly the same specific features. Union analysis of the subsets was then performed to determine the final union feature set, effectively combining the advantages of different algorithms. We found that the U-RFE framework could improve various models' performance. Stacking outperformed LR, SVM, RF, and XGBoost in most scenarios. When the target feature number of the RFE was set to 50 and the union feature set contained 298 deterministic features, the Stacking model achieved F1_weighted, Recall_weighted, Precision_weighted, Accuracy, and Matthews correlation coefficient of 0.851, 0.864, 0.854, 0.864, and 0.717, respectively. The performance of the minority categories was also significantly improved. Therefore, this recursive feature elimination-based approach of feature selection improves performances of classifying CRC deaths using clinical and omics data or those using other data with high feature redundancy and imbalance.

Prognostic Value of Immunohistochemistry-based Subtyping Before and After Neoadjuvant Chemotherapy in Patients with Triple-negative Breast Cancer.

To assess the predictive and prognostic value of a subtyping method based on immunohistochemistry in patients with triple-negative breast cancer (TNBC) treated with neoadjuvant chemotherapy (NAC). This study included patients with TNBC treated with anthracycline- and taxane-based NAC and curative surgery. Immunohistochemical (IHC) subtyping was performed using core needle biopsy specimens before NAC (pre-NAC) and residual tumors after NAC (post-NAC). Logistic regression was performed to identify predictive biomarkers of pathological complete response (pCR). Invasive disease-free survival (iDFS), distant disease-free survival (DDFS), and overall survival (OS) were assessed using the log-rank test and Cox proportional hazards regression. A total of 230 patients were followed up for a median of 59 months. Clinical lymph node status and the pre-NAC subtype were independent predictors of pCR (P=0.006 and 0.005, respectively). The pre-NAC subtype was an independent prognostic factor for long-term survival (iDFS: P < 0.001, DDFS: P=0.010, and OS: P=0.044). Among patients with residual disease (RD) after NAC, approximately 45% of tumors changed their IHC subtype. Furthermore, the post-NAC subtype, but not the pre-NAC subtype, was strongly associated with the survival of patients with RD (iDFS: P < 0.001, DDFS: P=0.005, and OS: P=0.006). The IHC subtype predicted response to NAC and long-term survival in patients with early TNBC. In patients with RD, almost 45% of the tumors changed subtype after NAC. The IHC subtype should be considered when planning additional therapies pre- and post-NAC.

MRPL48 is a novel prognostic and predictive biomarker of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most prevalent forms of cancer and poses a threat to the health and survival of humans. Mitochondrial ribosomal protein L48 (MRPL48) belongs to the mitochondrial ribosomal protein family, which participates in energy production. Studies have shown that MRPL48 can predict osteosarcoma incidence and prognosis, as well as promotes colorectal cancer progression. However, the role of MRPL48 in HCC remains unknown. METHODS: TCGA, GEO, HCCDB, CPTAC, SMART, UALCAN, Kaplan-Meier plotter, cBioPortal, and MethSurv were performed for bioinformatics purposes. Quantitative RT-PCR, immunoblotting, and functional studies were conducted to validate the methodology in vitro. RESULTS: MRPL48 was greatly overexpressed in HCC tissues, compared with healthy tissue, which was subsequently demonstrated in vitro as well. The survival and regression analyses showed that MRPL48 expression is of significant clinical prognostic value in HCC. The ROC curve and nomogram analysis indicated that MRPL48 is a powerful predictor of HCC. MRPL48 methylation was adversely associated with the expression of MRPL48, and patients with a low level of methylation had poorer overall survival than those with a high level of methylation. GSEA showed that the expression of the MRPL48 was correlated with Resolution of Sister Chromatid Cohesion, Mitotic Prometaphase, Retinoblastoma Gene in Cancer, RHO Gtpases Activate Formins, Mitotic Metaphase and Anaphase, and Cell Cycle Checkpoints. An analysis of immune cell infiltration showed a significant association between MRPL48 and immune cell infiltration subsets, which impacted the survival of HCC patients. Additionally, MRPL48 knockdown reduced HCC cell proliferation, migration, and invasion in vitro. CONCLUSIONS: We demonstrated that MRPL48 expression may be associated with HCC development and prognosis. These findings may open up new research directions and opportunities for the development of HCC treatments.

The interaction between systemic inflammatory markers and polygenic risk score in breast cancer risk: A cohort study in the UK Biobank.

BACKGROUND: Systemic inflammatory markers have been widely used in cancer prognosis prediction recently. However, there is limited knowledge regarding their impact on breast cancer risk and their interaction with polygenic risk scores. METHODS: A cohort study of 202,403 female participants from the UK Biobank were analyzed to estimate the hazard ratio (HR) for the incidence and mortality of breast cancer based on inflammatory markers using Cox regression models. Additionally, we stratified the analysis by polygenic risk scores (PRS) for breast cancer, and examined the interaction between these markers and PRS through likelihood ratio tests and relative excess risk due to interaction (RERI). RESULTS: Women in the highest tertile of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and C-reactive protein (CRP) showed an increased risk of breast cancer [HR (95 %CI) = 1.10 (1.02-1.18), 1.09 (1.01-1.17) and 1.15 (1.05-1.25), respectively], as compared to those in the lowest tertile. Regarding breast cancer mortality, only NLR and CRP exhibited consistent results in the univariate model [HR (95 %CI) = 1.25 (0.99-1.58) and 1.39 (1.10-1.77), respectively]. When stratified by PRS, stronger associations between inflammatory markers and breast cancer risk were observed in the high PRS group. Furthermore, there was a significant additive interaction between CRP and PRS [RERI (95 % CI) = 0.30 (0.06-0.53)]. CONCLUSION: NLR and CRP are associated with breast cancer risk and mortality, and the effect of CRP is influenced by PRS. Systematic inflammatory markers, together with PRS, might be applied in combined screening for breast cancer.

The SlARF4-SlHB8 regulatory module mediates leaf rolling in tomato.

Leaf is the main photosynthetic organ in plants and the primary energy source all along the plant life. Given the beneficial role of leaf rolling in improving photosynthetic efficiency and yield in specific environmental conditions, a better understanding of the factors and molecular mechanisms underlying this process is highly suited. Previously, the SlARF4 knocking out mutant exhibited upward curly leaf showed higher resistance to water deficit which driving us to uncover the function of SlARF4 in regulating the curly leaf formation. In this study, we unraveled the unexplored role of the SlARF4-SlHB8 module of transcription factors in the development of leaf rolling. Both SlARF4 loss-of-function and SlHB8 overexpressing tomato plants exhibited upward-rolled leaves, reflecting the active role of the two genes in controlling leaf rolling. Dual-luciferase reporter assays and phenotypic analysis of hybrid progenies suggested that SlHB8 acts downstream of SlARF4 in curly leaf formation. SlARF4 and SlHB8 influence the development of leaf palisade tissues via modulating the expression of genes associated with curly leaf formation. SEM analysis revealed no significant differences in leaf epidermal cells between the two leaf-rolling mutants and the wild type, indicating that curly leaves of arf4 and SlHB8-OE do not result from the asymmetric leaf epidermal cell growth. Our data provide novel insight into the molecular mechanism of abaxial-adaxial determination involving SlARF4 and SlHB8 and reveals that leaf rolling operates via different regulation mechanisms in tomato and Arabidopsis model plant.

Genetic and lifestyle factors for breast cancer risk assessment in Southeast China.

BACKGROUND: Despite the rising incidence and mortality of breast cancer among women in China, there are currently few predictive models for breast cancer in the Chinese population and with low accuracy. This study aimed to identify major genetic and life-style risk factors in a Chinese population for potential application in risk assessment models. METHODS: A case-control study in southeast China was conducted including 1321 breast cancer patients and 2045 controls during 2013-2016, in which the data were randomly divided into a training set and a test set on a 7:3 scale. The association between genetic and life-style factors and breast cancer was examined using logistic regression models. Using AUC curves, we also compared the performance of the logistic model to machine learning models, namely LASSO regression model and support vector machine (SVM), and the scores calculated from CKB, Gail and Tyrer-Cuzick models in the test set. RESULTS: Among all factors considered, the best model was achieved when polygenetic risk score, lifestyle, and reproductive factors were considered jointly in the logistic regression model (AUC = 0.73; 95% CI: 0.70-0.77). The models created in this study performed better than those using scores calculated from the CKB, Gail, and Tyrer-Cuzick models. However, the logistic model and machine learning models did not significantly differ from one another. CONCLUSION: In summary, we have found genetic and lifestyle risk predictors for breast cancer with moderate discrimination, which might provide reference for breast cancer screening in southeast China. Further population-based studies are needed to validate the model for future applications in personalized breast cancer screening programs.

Tyrosine kinase inhibitors in HER2-positive breast cancer brain metastases: A systematic review and meta-analysis.

BACKGROUND: Small tyrosine kinase inhibitors (TKIs) show activity against breast cancer brain metastases (BCBM) of the human epidermal growth factor receptor 2 (HER2)-positive subtype. This meta-analysis aimed to objectively explore the efficacy and safety of TKIs. METHODS: Electronic databases were searched for relevant clinical trials. We conducted a pairwise meta-analysis, pooled analysis, and estimated summary survival curves to compare survival outcomes following TKIs therapy for BCBM patients using Stata version 16.0 or R x64 4.0.5. RESULTS: Thirteen clinical trials involving 987 HER2-positive BCBM patients were analyzed. A trend of longer progression-free survival (PFS) was observed in the TKI-containing arm compared to the non-TKI-containing arm (hazard ratio = 0.64, 95% confidence interval [CI]: 0.35-1.15, p = 0.132), although the difference is not statistically significant. Summary survival curves reported the summary median PFS and overall survival were 7.9 months and 12.3 months. Subgroup analysis revealed that TKIs combined with capecitabine (TKI + Cap) regimens resulted in improved survival outcomes. Tucatinib may be more effective in BCBM patients. The main grade 3-5 adverse events (AEs) were diarrhea (22%, 95% CI: 14%-32%), neutropenia (11%, 95% CI: 5%-18%), hepatic toxicity (7%, 95% CI: 1%-16%), and sensory neuropathy (6%, 95% CI: 2%-12%). CONCLUSION: TKIs therapy improved the survival outcomes of HER2-positive BCBM patients, especially when combined with capecitabine and tolerable AEs. We also identified the clinical value of tucatinib, which appears to be the most favorable TKI drug for BCBM patients.

Impact of body mass index in therapeutic response for HER2 positive breast cancer treated with neoadjuvant targeted therapy: a multi-center study and meta-analysis.

While overweight/obesity has become a major public health issue worldwide, any association between body mass index (BMI) and therapeutic response in neoadjuvant targeted therapy treated HER2 positive breast cancer patients remain unclear. The information from a total of four-hundred and ninety-one neoadjuvant targeted therapy treated HER2 positive breast cancer patients from four institutions were retrospectively collected. Univariate and multivariate logistic analysis was developed to determine the association between BMI and therapeutic response. A meta-analysis of published literature was then conducted to confirm the effect of overweight/obesity on pCR for patients treated with neoadjuvant targeted therapy. Restricted cubic spline (RCS) adjusted for confounding factors demonstrated a decrease pCR with increasing BMI (OR = 0.937, P = 0.045). Patients were then categorized into under/normal weight (n = 299) and overweight/obesity (n = 192). Overweight/obese patients were independently associated with a poor therapeutic response. In the subgroup analysis, a significant negative impact of overweight/obesity on pCR can be observed both in single-targeted (OR = 0.556; P = 0.02) and dual-targeted (OR = 0.392; P = 0.021) populations. Six eligible studies involving 984 neoadjuvant targeted therapy treated HER2 positive breast cancer patients were included in the meta-analysis. The meta-analysis also demonstrated that overweight/obesity was significantly associated with a poor response to neoadjuvant anti-HER2 therapy (OR = 0.68; P = 0.007). Our result show that overweight and obese HER2 positive breast cancer patients are less likely to achieve pCR after neoadjuvant targeted therapy.

Comprehensive proteome, phosphoproteome and kinome characterization of luminal A breast cancer.

Background: Breast cancer is one of the most frequently occurring malignant cancers worldwide. Invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the two most common histological subtypes of breast cancer. In this study, we aimed to deeply explore molecular characteristics and the relationship between IDC and ILC subtypes in luminal A subgroup of breast cancer using comprehensive proteomics and phosphoproteomics analysis. Methods: Cancer tissues and noncancerous adjacent tissues (NATs) with the luminal A subtype (ER- and PR-positive, HER2-negative) were obtained from paired IDC and ILC patients respectively. Label-free quantitative proteomics and phosphoproteomics methods were used to detect differential proteins and the phosphorylation status between 10 paired breast cancer and NATs. Then, the difference in protein expression and its phosphorylation between IDC and ILC subtypes were explored. Meanwhile, the activation of kinases and their substrates was also revealed by Kinase-Substrate Enrichment Analysis (KSEA). Results: In the luminal A breast cancer, a total of 5,044 high-confidence proteins and 3,808 phosphoproteins were identified from 10 paired tissues. The protein phosphorylation level in ILC tissues was higher than that in IDC tissues. Histone H1.10 was significantly increased in IDC but decreased in ILC, Conversely, complement C4-B and Crk-like protein were significantly decreased in IDC but increased in ILC. Moreover, the increased protein expression of Septin-2, Septin-9, Heterogeneous nuclear ribonucleoprotein A1 and Kinectin but reduce of their phosphorylation could clearly distinguish IDC from ILC. In addition, IDC was primarily related to energy metabolism and MAPK pathway, while ILC was more closely involved in the AMPK and p53/p21 pathways. Furthermore, the kinomes in IDC were primarily significantly activated in the CMGC groups. Conclusions: Our research provides insights into the molecular characterization of IDC and ILC and contributes to discovering novel targets for further drug development and targeted treatment.

High leukocyte mitochondrial DNA copy number contributes to poor prognosis in breast cancer patients.

BACKGROUND: Compelling evidence has indicated a significant association between leukocyte mitochondrial DNA copy number (mtDNAcn) and prognosis of several malignancies in a cancer-specific manner. However, whether leukocyte mtDNAcn can predict the clinical outcome of breast cancer (BC) patients has not been well investigated. METHODS: The mtDNA copy number of peripheral blood leukocytes from 661 BC patients was measured using a Multiplex AccuCopy™Kit based on a multiplex fluorescence competitive PCR principle. Kaplan-Meier curves and Cox proportional hazards regression model were applied to investigate the association of mtDNAcn with invasive disease-free survival (iDFS), distant disease-free survival (DDFS), breast cancer special survival (BCSS), and overall survival (OS) of patients. The possible mtDNAcn-environment interactions were also evaluated by the Cox proportional hazard regression models. RESULTS: BC patients with higher leukocyte mtDNA-CN exhibited a significantly worse iDFS than those with lower leukocyte mtDNAcn (5-year iDFS: fully-adjusted model: HR = 1.433[95%CI 1.038-1.978], P = 0.028). Interaction analyses showed that mtDNAcn was significantly associated with hormone receptor status (adjusted p for interaction: 5-year BCSS: 0.028, 5-year OS: 0.022), so further analysis was mainly in the HR subgroup. Multivariate Cox regression analysis demonstrated that mtDNAcn was an independent prognostic factor for both BCSS and OS in HR-positive patients (HR+: 5-year BCSS: adjusted HR (aHR) = 2.340[95% CI 1.163-4.708], P = 0.017 and 5-year OS: aHR = 2.446 [95% CI 1.218-4.913], P = 0.011). CONCLUSIONS: For the first time, our study demonstrated that leukocyte mtDNA copy number might influence the outcome of early-stage breast cancer patients depending on intrinsic tumor subtypes in Chinese women.

Relationship Between Weight-Adjusted Waist Index and Osteoporosis in the Senile in the United States from the National Health and Nutrition Examination Survey, 2017-2020.

BACKGROUND: Some studies suggested obesity may be beneficial in preventing bone loss through the negative relationship between body mass index (BMI) and osteoporosis in senile. However, using BMI to measure obesity is unconvincing due to confounding factors such as muscle mass were not taken into account, and few articles have yet taken a better way to evaluate the relationship between obesity and osteoporosis. METHODOLOGY: Using a cross-sectional sample of 1,979 participants aged ≥65 years from the National Health and Nutrition Examination Survey (NHANES) 2017 to 2020, we evaluated the relation of weight-adjusted waist index (WWI) with osteoporosis. WWI was calculated as waist (cm) divided by the square root of body weight (kg). Diagnosis of osteoporosis was described as follows: according to the updated reference for calculating bone mineral density T-Scores, we marked the BMD value as X, using the formula T femoral neck= (X g/cm2-0.888 g/cm2)/0.121 g/cm2, T lumbar spine= (X g/cm2- 1.065 g/cm2)/0.122 g/cm2, and defined those with a final T femoral neck <-0.25. T lumbar spine<-0.25 or patients with previously diagnosed OP in other hospitals as osteoporosis. RESULTS: All the 1,979 participants were between 65 and 80 years, there were 379 (21.1%) with osteoporosis, 608 (30.7%) with WWI exceeding 12 (cm/√kg) (range 8.85-14.14), and 955 (48.3%) women. Furthermore, the relationship between WWI and osteoporosis was nonlinear with a threshold effect point. Odds of OP significantly increased with the increase of WWI (OR 2.33, 95% CI 11.48-3.38, P = 0.0001) at the right side of the threshold point (WWI≥12) according to the threshold effect study. CONCLUSIONS: Found a significant positive relationship between WWI and osteoporosis. Body fat management in the senile may be good to prevent osteoporosis if confirmed by other prospective studies analyzing the longitudinal risk of osteoporosis with obesity.