Overall Survival In Breast Cancer Patients Research Articles

KIF2C is a prognostic biomarker associated with immune cell infiltration in breast cancer

BackgroundThe kinesin-13 family member 2C (KIF2C) is a versatile protein participating in many biological processes. KIF2C is frequently up-regulated in multiple types of cancer and is associated with cancer development. However, the role of KIF2C in immune cell infiltration of tumor microenvironment and immunotherapy in breast cancer remains unclear.MethodsThe expression of KIF2C was analyzed using Tumor Immune Estimation Resource (TIMER) database and further verified by immunohistochemical staining in human breast cancer tissues. The correlation between KIF2C expression and clinical parameters, the impact of KIF2C on clinical prognosis and independent prognostic factors were analyzed by using TCGA database, the Kaplan-Meier plotter, and Univariate and multivariate Cox analyses, respectively. The nomograms were constructed according to independent prognostic factors and validated with C-index, calibration curves, ROC curves, and decision curve analysis. A gene set enrichment analysis (GSEA) was performed to explore the underlying molecular mechanisms of KIF2C. The degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA (ssGSEA). The Tumor mutational burden and Tumor Immune Dysfunction and Rejection (TIDE) were used to analyze immunotherapeutic efficiency. Finally, the KIF2C-related competing endogenous RNA (ceRNA) network was constructed to predict the putative regulatory mechanisms of KIF2C.ResultsKIF2C was remarkably up-regulated in 18 different types of cancers, including breast cancer. Kaplan-Meier survival analysis showed that high KIF2C expression was associated with poor overall survival (OS). KIF2C expression was associated with clinical parameters such as age, TMN stage, T status, and molecular subtypes. We identified age, stage, estrogen receptor (ER) and KIF2C expression as OS-related independent prognosis factors for breast cancer. An OS-related nomogram was developed based on these independent prognosis factors and displayed good predicting ability for OS of breast cancer patients. Finally, our results revealed that KIF2C was significantly related to immune cell infiltration, tumor mutational burden, and immunotherapy in patients with breast cancer.ConclusionKIF2C was overexpressed in breast cancer and was positively correlated with immune cell infiltration and immunotherapy response. Therefore, KIF2C can serve as a potential biomarker for prognosis and immunotherapy in breast cancer.

Demystifying the Role of Prognostic Biomarkers in Breast Cancer through Integrated Transcriptome and Pathway Enrichment Analyses

Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of death in women. Researchers have discovered an increasing number of molecular targets for BC prognosis and therapy. However, it is still urgent to identify new biomarkers. Therefore, we evaluated biomarkers that may contribute to the diagnosis and treatment of BC. We searched TCGA datasets and identified differentially expressed genes (DEGs) by comparing tumor (100 samples) and non-tumor (100 samples) tissues using the Deseq2 package. Pathway and functional enrichment analysis of the DEGs was performed using the DAVID (Database for Annotation, Visualization, and Integrated Discovery) database. The protein-protein interaction (PPI) network was identified using the STRING database and visualized through Cytoscape software. Hub gene analysis of the PPI network was completed using cytohubba plugins. The associations between the identified genes and overall survival (OS) were analyzed using a Kaplan-Meier plot. Finally, we have identified hub genes at the transcriptome level. A total of 824 DEGs were identified, which were mostly enriched in cell proliferation, signal transduction, and cell division. The PPI network comprised 822 nodes and 12,145 edges. Elevated expression of the five hub genes AURKA, BUB1B, CCNA2, CCNB2, and PBK are related to poor OS in breast cancer patients. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation causing aneuploidy. The enriched functions and pathways included the cell cycle, oocyte meiosis, and the p53 signaling pathway. The identified five hub genes in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.

COL12A1 as a prognostic biomarker links immunotherapy response in breast cancer.

Immunotherapy has shown promising efficacy for breast cancer (BC) patients. Yet the predictive biomarkers for immunotherapy response remain lacking. Based on two GEO datasets, 53 differentially expressed genes associated with durvalumab treatment response were identified. Using least absolute shrinkage and selection operator (LASSO) and univariate Cox regression, four genes (COL12A1, TNN, SCUBE2, and FDCSP) revealed prognostic value in the TCGA BC cohort. COL12A1 outperformed the others, without overlap in its survival curve. Survival analysis by Kaplan-Meier plotter demonstrated that COL12A1 was negatively associated with BC patients' prognosis. A COL12A1-based nomogram was further developed to predict the overall survival in BC patients. The calibration plot revealed an optimal agreement between nomogram prediction and actual observation. Moreover, COL12A1 expression was significantly up-regulated in BC tissues and COL12A1 knockdown impaired the proliferation of MDA-MB-231 and BT549 cells. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment analysis pathway indicated that the function of COL12A1 was related to immunity-related pathways. Immunological analyses illustrated that COL12A1 was correlated with M2 macrophage infiltration and M2 macrophage markers (transforming growth factor beta 1 (TGFB1), interleukin-10, colony stimulating factor 1 receptor (CSF1R) and CD163) in BC. Immunohistochemistry staining further revealed a highly positive relationship of COL12A1 with TGF-β1. The co-incubated models of BC cells and M2 macrophges showed COL12A1 knockdown suppressed M2 macrophage infiltration. Additionally, silencing COL12A1 suppressed TGF-B1 protein expression, and treating with TGFB1 could reverse the inhibitory effects on M2 macrophage infiltration by COL12A1 knockdown. Using immunotherapy datasets, we also found elevated expression of COL12A1 predicted poor response to anti-PD-1/PD-L1 therapy. These results reinforce the current understanding of COL12A1's roles in tumorigenesis and immunotherapy response in BC.

The prognostic value and response to immunotherapy of immunogenic cell death-associated genes in breast cancer.

Breast cancer (BRCA) remains the most prevalent cancer worldwide and the tumor microenvironment (TME) has been discovered to exert a wide influence on the overall survival and therapeutic response. Numerous lines of evidence reported that the effects of immunotherapy of BRCA were manipulated by TME. Immunogenic cell death (ICD) is a form of regulated cell death (RCD) that is capable of fueling adaptive immune responses and aberrant expression of ICD-related genes (ICDRGs) can govern the TME system by emitting danger signals or damage-associated molecular patterns (DAMPs). In the current study, we obtained 34 key ICDRGs in BRCA. Subsequently, using the transcriptome data of BRCA from the TCGA database, we constructed a risk signature based on 6 vital ICDRGs, which had a good performance in predicting the overall survival of BRCA patients. We also examined the efficacy of our risk signature in the validation dataset (GSE20711) in the GEO database and it performed excellently. According to the risk model, patients with BRCA were divided into high-risk and low-risk groups. Also, the unique immune characteristics and TME between the two subgroups and 10 promising small molecule drugs targeting BRCA patients with different ICDRGs risk have been investigated. The low-risk group had good immunity indicated by T cell infiltration and high immune checkpoint expression. Moreover, the BRCA samples could be divided into three immune subtypes according to immune response severity (ISA, ISB, and ISC). ISA and ISB predominated in the low-risk group and patients in the low-risk group exhibited a more vigorous immune response. In conclusion, we developed an ICDRGs-based risk signature that can predict the prognosis of BRCA patients and offer a novel therapeutic strategy for immunotherapy, which would be of great significance in the BRCA clinical setting.

Novel immune-related prognostic model and nomogram for breast cancer based on ssGSEA.

This study aimed to construct an immune-related prognostic model and a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) of breast cancer patients. We applied single-sample gene set enrichment analysis to classify 1,053 breast cancer samples from The Cancer Genome Atlas (TCGA) database into high and low immune cell infiltration clusters. In cluster construction and validation, the R packages "GSVA," "hclust," "ESTIMATE," and "CIBERSORT" and GSEA software were utilized. ImmPort, univariate Cox regression analysis, and Venn analysis were then used to identify 42 prognostic immune-related genes. Eventually, the genes TAPBPL, RAC2, IL27RA, ULBP2, PSMB8, SOCS3, NFKBIE, IGLV6-57, CXCL1, IGHD, AIMP1, and CXCL13 were chosen for model construction utilizing least absolute shrinkage and selection operator regression analysis. The Kaplan-Meier curves of both the training and validation sets indicated that the overall survival of patients in the low-risk group was superior to that of patients in the high-risk group (p < .05). The areas under curves (AUCs) of the model at 1, 3, and 5years were, respectively, .697, .710, and .675 for the training set and .930, .688, and .712 for the validation set. Regarding clinicopathologic characteristics, breast cancer-related genes, and tumor mutational burden, effective differentiation was achieved between high-risk and low-risk groups. A nomogram integrating the risk model and clinicopathologic factors was constructed using the "rms" R software package. The nomogram's 1-, 3-, and 5-year AUCs were .828, .783, and .751, respectively. Overall, our study developed an immune-related model and a nomogram that could reliably predict OS for breast cancer patients, and offered insights into tumor immune and pathological mechanisms.

Small breast epithelial mucin as a useful prognostic marker for breast cancer patients.

This study aimed to evaluate the clinical utility of small breast epithelial mucin (SBEM) as a prognostic biomarker in an independent patient cohort. The paraffin-embedded tissues and clinicopathological data of 105 patients with breast cancer were collected, and the expression of SBEM in breast cancer samples was detected by immunohistochemical staining. The correlations between clinicopathological variables and the expression of SBEM were analyzed, and its significance as a prognostic indicator for breast cancer patients was determined. Immunohistochemical staining revealed that SBEM was expressed mostly in the cytomembrane and cytoplasm, with markedly increased SBEM expression (≥4 points on staining intensity) observed in 34 of 105 breast cancer tissues (32.4%). Elevated expression of SBEM was found to be significantly associated with larger tumor size (P = 0.002), more frequent lymph node metastasis (P = 0.029), advanced tumor node metastasis stage (P = 0.005), reduced expression of the progesterone receptor (PR) (P = 0.002), and a higher Ki-67 index (P = 0.006). Survival analysis indicated that patients with elevated SBEM expression had worse overall survival (OS) (5-year OS rate: 50.5 vs 93.9% for high and low SBEM expression, respectively, P < 0.001) and disease-free survival (DFS) (5-year DFS rate: 52.8 vs 81.7% for high and low SBEM expression, respectively, P = 0.001) rates than those with low expression of SBEM. Univariate and multivariate Cox analyses demonstrated that elevated expression of SBEM (hazard ratio [HR] = 1.994, 95% confidence interval [CI]: 1.008-3.945, P = 0.047), tumor size (HR = 2.318, 95% CI: 1.071-5.017, P = 0.033), and PR status (HR = 0.195, 95% CI: 0.055-0.694, P = 0.012) were independent predictors of OS in breast cancer patients. Elevated expression of SBEM was associated with both aggressive tumor characteristics and poor survival, indicating its potential as a useful prognostic biomarker for breast cancer patients.

Comprehensive Analysis of Necroptosis-Related Genes as Prognostic Factors and Immunological Biomarkers in Breast Cancer.

Breast cancer (BC) is a lethal malignancy with a poor prognosis. Necroptosis is critical in the progression of cancer. However, the expression of genes involved in necroptosis in BC and their association with prognosis remain unclear. We investigated the predictive potential of necroptosis-related genes in BC samples from the TCGA dataset. We used LASSO regression to build a risk model consisting of twelve necroptosis-related genes in BC. Using the necroptosis-related risk model, we were able to successfully classify BC patients into high- and low-risk groups with significant prognostic differences (p = 4.872 × 10 -7). Additionally, we developed a matched nomogram predicting 5, 7, and 10-year overall survival in BC patients based on this necroptosis-related risk model. Our next step was to perform multiple GSEA analyses to explore the biological pathways through which these necroptosis-related risk genes influence cancer progression. For these twelve risk model genes, we analyzed CNV, SNV, OS, methylation, immune cell infiltration, and drug sensitivity in pan-cancer. In addition, immunohistochemical data from the THPA database were used to validate the protein expression of these risk model genes in BC. Taken together, we believe that necroptosis-related genes are considered potential therapeutic targets in BC and should be further investigated.

LINC00536 Promotes Breast Cancer Progression by Regulating ROCK1 via Sponging of miR-214-5p.

Accumulating evidence has shown that long noncoding RNAs (lncRNAs) play a significant role in regulating gene expression and participating in the progression of various malignancies. In our study, by analyzing data from The Cancer Genome Atlas (TCGA), LINC00536 was found to be highly expressed in breast cancer (BC) tissues, but its function and clinical significance in BC are still unknown. Therefore, we aimed to explore the role and molecular mechanism of LINC00536 in BC. We collected human BC tissue specimens and validated that LINC00536 was overexpressed in BC tissues. Increased LINC00536 expression was associated with advanced TNM stage, larger tumor diameter, lymph node metastasis and poor prognosis in patients with BC. Univariate and multivariate Cox regression analyses showed that high LINC00536 expression was an independent prognostic risk factor for overall survival in BC patients. Furthermore, quantitative reverse transcription PCR (qRT-PCR) showed that LINC00536 was upregulated in BC cell lines. Then, we confirmed that LINC00536 silencing-inhibited BC cell proliferation, migration, and invasion and led to cell cycle arrest in vitro. Animal experiments showed that knockdown of LINC00536 expression suppressed tumorigenesis in vivo. Mechanistically, LINC00536 serves as a ceRNA for miR-214-5p, increasing the expression of ROCK1, which acts as a tumor promoter in BC. Rescue assays revealed that miR-214-5p inhibition or ROCK1 overexpression could neutralize the suppressive effects of LINC00536 knockdown on cell proliferation, migration and invasion. Our data indicated that LINC00536 accelerates BC progression by regulating the miR-214-5p/ROCK1 pathway, which might provide a new perspective to investigate the development process of BC.

A Novel Inflammatory Response-Related Gene Signature Predicts Immune Status and Prognosis of Breast Cancer.

Breast cancer is the most common type of cancer and the leading cause of cancer-related death in women worldwide. In this study, we aimed to construct an inflammatory response-related gene model for predicting the immune status and prognosis of breast cancer patients. We obtained the inflammatory response-related genes from the Molecular Signatures Database. Furthermore, we used univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression to construct an inflammatory response-related gene signature (IRGS) model based on dataset obtained from The Cancer Genome Atlas (TCGA). Patients were consequently categorized into high-risk and low-risk groups. Kaplan-Meier analysis was used to compare the overall survival (OS) of high-risk and low-risk groups. Following that, we validated the model using a dataset (GSE96058) acquired from Gene Expression Omnibus (GEO) database. Univariate and multivariate Cox analyses were used to determine the independent prognostic value of the IRGS in the TCGA and GSE96058 cohorts. A nomogram was constructed to predict the OS in the TCGA cohort. Further, we used Gene Set Enrichment Analysis (GSEA), CIBERSORT, and single-sample Gene Set Enrichment Analysis (ssGSEA) to evaluate the associations of IRGS with immune-associated pathways and immune infiltration. Finally, the relationship between the expression of the signature genes and drug sensitivity was conducted using Pearson correlation analysis. We established an IRGS to stratify breast cancer patients into the low-risk and high-risk groups. In both the training and validation sets, patients in the high-risk group had significantly shorter OS than those in the low-risk group. The risk score was significantly correlated with the clinical characteristics and could be used as a tool to predict the prognosis of breast cancer. Moreover, we found that the IRGS risk score was an independent predictor of OS in breast cancer patients, and a nomogram model based on IRGS risk score and other clinical factors could effectively predict the prognosis of breast cancer patients. Furthermore, the IRGS risk score was correlated with immune characteristics and was inversely associated with the abundance of immune cell infiltration. Patients with a low IRGS risk score had higher expression levels of immune checkpoint genes, suggesting that IRGS can be used as a potential indicator for immunotherapy. Finally, we found that the expression levels of prognostic genes were significantly correlated with tumor cell sensitivity to chemotherapeutic drugs. Overall, these findings suggest that the IRGS can be used to predict the prognosis and immune status of breast cancer patients and provide new therapeutic targets for the treatment of these patients.

Integrated bioinformatic analysis to understand the association between phthalate exposure and breast cancer progression

Phthalates have been extensively used as plasticizers while manufacturing plastic-based consumer products. Estradiol mimicking properties and association studies suggest phthalates may contribute to breast cancer (BC). We performed an in-silico analysis and functional studies to understand the association between phthalate exposure and BC progression. Search for phthalate-responsive genes using the comparative toxicogenomics database identified 20 genes as commonly altered in response to multiple phthalates exposure. Of the 20 genes, 12 were significantly differentially expressed between normal and BC samples. In BC samples, 9 out of 20 genes showed a negative correlation between promoter methylation and its expression. AHR, BAX, BCL2, CAT, ESR2, IL6, and PTGS2 expression differed significantly between metastatic and non-metastatic BC samples. Gene set enrichment analysis identified metabolism, ATP-binding cassette transporters, insulin signaling, and type II diabetes as highly enriched pathways. The diagnostic assessment based on 20 genes expression suggested a sensitivity and a specificity >0.91. The aberrantly expressed phthalate interactive gene influenced the overall survival of BC patients. Drug-gene interaction analysis identified 14 genes and 523 candidate drugs, including 19 BC treatment-approved drugs. Di(2-ethylhexyl) phthlate (DEHP) exposure increased the growth, proliferation, and migration of MCF-7 and MDA-MB-231 cells in-vitro. DEHP exposure induced morphological changes, actin cytoskeletal remodeling, increased ROS content, reduced basal level lipid peroxidation, and induced epithelial to mesenchymal transition (EMT). The present approach can help to explore the potentially damaging effects of environmental agents on cancer risk and understand the underlined pathways and molecular mechanisms.

Development and Validation of Ferroptosis-Related lncRNAs as Prognosis and Diagnosis Biomarkers for Breast Cancer.

Breast cancer (BC) is one of the most common malignancies affecting women. Ferroptosis is a novel cancer treatment option. The present study is aimed to identify suitable ferroptosis-related lncRNAs to predict and diagnose BC. Differential expression and Cox regression analyses were used to screen suitable prognostic biomarkers and construct a suitable risk model. We identified four ferroptosis-related differentially expressed lncRNAs (FR-DELs) (LINC01152, AC004585.1, MAPT-IT1, and AC026401.3), which were independently correlated with the overall survival of BC patients. The area under the curve value of the prognostic model using those four biomarkers was over 0.60 in all three groups. The sensitivity and specificity of the diagnostic model using those four biomarkers were 86.89% and 86.73%, respectively. Our present study indicated that these four FR-DELs (LINC01152, AC004585.1, MAPT-IT1, and AC026401.3) could be prognostic biomarkers for BC, although clinical validation studies are required.

Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients.

Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942-0.979, P < 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.

The Prognostic value of Serum Ferritin in Breast cancer patients

Background: Breast cancer is the most common tumor among women worldwide, and elevated pretreatment serum ferritin levels are a poor prognostic factor in patients with many malignancies, but the relationship between serum ferritin levels and disease prognosis in breast cancer patients remains unclear. Aim: To estimate the relationship between pretreatment serum ferritin, overall survival (OS) and progression-free survival (PFS) in breast cancer patients. Patients and Methods: We evaluated pretreatment serum ferritin in 96 newly diagnosed breast cancer patients who have been followed up for 27months. Statistical analysis was performed using MedCalc (version 19.1.6). A p value &lt;0.05 was considered statistically significant. Results: Among patients with serum ferritin levels less than 146ng/ml, the estimated mean PFS survival was 26.561±0.328 months compared to 15.2121.396 months for those with serum ferritin levels 146ng/ml or higher (p=0.001). The estimated mean OS survival for patients with serum ferritin levels &lt; 146ng/ml was 27 months, compared to 23.5331.224 months for patients with serum ferritin levels ≥ 146ng/ml (P=0.002), their 27 months OS survival rates were 68.75% and 27.08%, respectively (p=0.008). Conclusion: Serum ferritin can be useful prognostic factor for PFS and OS in breast cancer patients, and elevated levels of it at 146ng/ml or more are a poor prognostic factor for 27month survival rates.

A Prognostic Model Based on Nutritional Risk Index in Operative Breast Cancer.

Background: The nutritional risk index (NRI) is an independent prognostic factor for overall survival in various cancers, but its prognostic value in breast cancer remains unclear. This study aimed to explore the relationship between the NRI and overall survival (OS) in breast cancer and to develop a predictive nomogram. Methods: We retrospectively enrolled 1347 breast cancer patients who underwent mastectomy or lumpectomy between January 2011 and November 2012. Using a cutoff value of 110.59, patients were divided into a high-NRI group and a low-NRI group. OS was compared between the two groups. Clinicopathological factors independently associated with survival were used to construct a predictive nomogram. Results: Of the 1347 patients, 534 patients were classified as high NRI and 813 as low NRI. OS was significantly shorter in low-NRI patients. The 3- and 5-year OS rates were 87.3% and 73.4%, respectively, in the high-NRI group whereas they were 83.0% and 67.2%, respectively, in the low-NRI group. Cox regression analysis found that histopathological type, tumor size, lymph node status, progesterone receptor (PR) status, Ki-67, and NRI were independently associated with OS. Conclusions: NRI is an independent prognostic factor of OS in breast cancer patients. The proposed nomogram model may be a useful tool for individualized survival prediction.

Comprehensive Analysis Identifies COL1A1, COL3A1, and POSTN as Key Genes Associated with Brain Metastasis in Patients with Breast Cancer.

Objective Brain metastasis (BM) is associated with a high mortality in patients with breast cancer (BC). Nevertheless, the molecular mechanisms of BM in BM remain uncertain. The study aims to identify the key genes in BC in relation to BM and to assess their prognostic value. Methods Two microarray datasets GSE125989 and GSE100534 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between primary BC and BM samples. The function enrichment analysis and protein-protein interaction (PPI) network were performed. We mapped hub genes into the Kaplan–Meier database for their correlations with BC survival. Results Venn diagram analysis showed an overlapped upregulated DEG and 18 overlapped downregulated ones between primary BC and BM samples. We constructed the PPI network, and top 5 hub genes were sorted out according to the node degree, including type I collagen α1 chain (COL1A1), lumican (LUM), type III collagen α1 chain (COL3A1), type V collagen α2 chain (COL5A2), and periosteal protein (POSTN). The Kaplan–Meier database analysis found that COL1A1, COL3A1, and POSTN were significantly correlated with overall survival of BC patients. Conclusion The study suggests that COL1A1, COL3A1, and POSTN may be key genes associated with BM in patients with BC.

Impact of body mass index on pathological complete response and survival of breast cancer patients receiving neoadjuvant chemotherapy.

High body mass index (BMI) is regarded as a poor prognostic factor in breast cancer (BC). However, its association with pathological complete response (pCR) and survival after neoadjuvant chemotherapy (NAC) remains controversial. We wanted to assess the prognostic impact of BMI in this setting. Single-center, retrospective review of 314 BC patients undergoing NAC from 2010 to 2018. Patients were categorized as underweight/normal weight (UW/NW) (BMI < 25) or overweight/obese (OW/OB) (BMI ≥ 25). The relationship between BMI and other traditional clinical-pathological prognostic variables with the pCR rate was investigated using logistic regression analysis. The effect on event-free survival (EFS) and overall survival (OS) was estimated by the Cox proportional hazards regression analysis. One hundred and twenty-two patients were UW/NW while 192 were OW/OB. Multivariate analysis revealed that hormonal receptors negative, HER2 positive, and clinical tumor stage (cT) 1-2 were independent predictor factors for pCR. Multivariate analysis confirmed tumor grade G3 and lack of pCR as independent adverse prognostic factors for EFS, while factors associated with worse OS were cT3-4, hormone receptors negative, and lack of pCR. Non-significant differences in pCR, EFS, or OS were observed between the two baseline BMI categories. In our experience, BMI is not associated with pCR, EFS, or OS in BC patients receiving NAC. Achieving pCR is the most consistent factor associated with EFS and OS. Prospective and well-designed studies taking into account other important biological and anthropometric factors are needed to determine the exact role of BMI in this setting.

Identification and validation of a novel 16-gene prognostic signature for patients with breast cancer

Despite increased early diagnosis and improved treatment in breast cancer (BRCA) patients, prognosis prediction is still a challenging task due to the disease heterogeneity. This study was to identify a novel gene signature that can accurately evaluate BRCA patient survival. The gene expression and clinical data of BRCA patients were collected from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of BRCA International Consortium (METABRIC) databases. Genes associated with prognosis were determined by Kaplan–Meier survival analysis and multivariate Cox regression analysis. A prognostic 16-gene score was established with linear combination of 16 genes. The prognostic value of the signature was validated in the METABRIC and GSE202203 datasets. Gene expression analysis was performed to investigate the diagnostic values of 16 genes. The 16-gene score was associated with shortened overall survival in BRCA patients independently of clinicopathological characteristics. The signalling pathways of cell cycle, oocyte meiosis, RNA degradation, progesterone mediated oocyte maturation and DNA replication were the top five most enriched pathways in the high 16-gene score group. The 16-gene nomogram incorporating the survival‐related clinical factors showed improved prediction accuracies for 1-year, 3-year and 5‐year survival (area under curve [AUC] = 0.91, 0.79 and 0.77 respectively). MORN3, IGJ, DERL1 exhibited high accuracy in differentiating BRCA tissues from normal breast tissues (AUC > 0.80 for all cases). The 16-gene profile provides novel insights into the identification of BRCA with a high risk of death, which eventually guides treatment decision making.

Construction and validation of a prognostic risk model for breast cancer based on protein expression

Breast cancer (BRCA) is the primary cause of mortality among females globally. The combination of advanced genomic analysis with proteomics characterization to construct a protein prognostic model will help to screen effective biomarkers and find new therapeutic directions. This study obtained proteomics data from The Cancer Proteome Atlas (TCPA) dataset and clinical data from The Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier and Cox regression analyses were used to construct a prognostic risk model, which was consisted of 6 proteins (CASPASE7CLEAVEDD198, NFKBP65-pS536, PCADHERIN, P27, X4EBP1-pT70, and EIF4G). Based on risk curves, survival curves, receiver operating characteristic curves, and independent prognostic analysis, the protein prognostic model could be viewed as an independent factor to accurately predict the survival time of BRCA patients. We further validated that this prognostic model had good predictive performance in the GSE88770 dataset. The expression of 6 proteins was significantly associated with the overall survival of BRCA patients. The 6 proteins and encoding genes were differentially expressed in normal and primary tumor tissues and in different BRCA stages. In addition, we verified the expression of 3 differential proteins by immunohistochemistry and found that CDH3 and EIF4G1 were significantly higher in breast cancer tissues. Functional enrichment analysis indicated that the 6 genes were mainly related to the HIF-1 signaling pathway and the PI3K-AKT signaling pathway. This study suggested that the prognosis-related proteins might serve as new biomarkers for BRCA diagnosis, and that the risk model could be used to predict the prognosis of BRCA patients.

Role of metformin in prevention and prognosis of breast cancer

Dear Editor, Metformin is a hypoglycemic drug of the class biguanides used as the first-line treatment for type 2 diabetes mellitus (T2DM). The drug acts by inhibiting hepatic gluconeogenesis, decreasing intestinal absorption of glucose and circulating insulin. It has also been suggested that the drug indirectly increases insulin sensitivity by increasing peripheral glucose utilization. Data from the World Health Organization (WHO) shows that breast cancer (BC) is the most prevalent cancer in women, with an incidence of 1.7 million cases in 2012 (25 % of all female cancers). It is the fifth leading cause of death from cancer overall.1 A clinical trial conducted in 2019 elaborated that metformin altered host metabolism proving to be a substitute for a fasting-like mechanism that affects cancer cells' metabolism and growth environment of cancer cells including an increase in beta-OH-butyric acid (BHBA).2 It was further corroborated by a research that found an inverse relationship between metformin and BC. This is accomplished by boosting the activity of AMP-dependent protein kinase, which suppresses the mTOR-associated oncogenic signalling pathway. This induces S phase arrest and apoptosis in triple-negative breast cancer cells. Data from 18,527 participants brought about 2,680 participants who used metformin at least three months before the diagnosis of BC. A multivariate study showed that this was negatively associated with BC risk (0.821, 0.726-0.912, p=0.002).3 A favourable response on disease-free survival and overall survival in BC patients with T2DM. The number of CD8+ tumour-infiltrating lymphocytes (TILS) has been increased with the use of metformin engendering anti-carcinogenic effects.4 In the last 15 years, we have seen a drastic change in the demographics of BC, with an estimated increase of 70.7 to 130.6% in 2020 and 2025 relative to 2015 in the 30-34 age group in Pakistan.5 There has been a significant increase in T2DM along with increased use of metformin for its treatment which thus raises a question about the efficacy of the drug in a neoadjuvant setting for BC on Pakistani society. Further research, especially clinical trials, needs to be conducted in Pakistan including but not limited to a correlation of metformin use and incidence of BC.

A Novel YTHDF3-Based Model to Predict Prognosis and Therapeutic Response in Breast Cancer.

Background: Due to high tumor heterogeneity, breast cancer (BC) patients still suffer poor survival outcomes. YTHDF3 plays a critical role in the prognosis of BC patients. Hence, we aimed to construct a YTHDF3-based model for the prediction of the overall survival (OS) and the sensitivity of therapeutic agents in BC patients. Methods: Based on The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database, we obtained BC patients’ data (n = 999) with YTHDF3 expression profiles. The association between YTHDF3 expression and 5-year OS was determined via Cox proportional hazards regression (CPHR) analysis. By integrating the variables, we established a prognostic nomogram. The model was estimated via discrimination, calibration ability, and decision curve analysis (DCA). The performance of the model was compared with the TNM stage system through receiver operating characteristic (ROC) curves and DCA. By means of the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/), the therapeutic agents’ response was estimated. Gene set enrichment analysis (GSEA) demonstrated possible biological mechanisms related to YTHDF3. TIMER and CIBERSORTx were employed to analyze the association between YTHDF3 and tumor-infiltrating immune cells. Results: The high YTHDF3 expression was significantly correlated with poor 5-year OS in BC patients. Through multivariate CPHR, four independent prognostic variables (age, TNM stage, YTHDF3 expression, and molecular subtype) were determined. On the basis of the four factors, a YTHDF3-based nomogram was built. The area under the curve (AUC) of the ROC curve for the model surpassed that of the TNM stage system (0.72 vs. 0.63, p = 0.00028). The model predictions showed close consistency with the actual observations via the calibration plot. Therapeutic response prediction was conducted in high- and low-risk groups and compared with each other. The BC patients with higher risk scores showed more therapeutic resistance than those with a lower risk score. Conclusion: YTHDF3 was verified as a prognostic biomarker of BC, and a novel YTHDF3-based model was constructed to predict the 5-year OS of BC patients. Our model could be applied to effectively predict the therapeutic response of commonly used agents for BC patients.