Radiomics-based signature of breast cancer on preoperative contrast-enhanced MRI to predict axillary metastasis.

Aim: This study aimed to predict axillary metastasis using radiology features in dynamic contrast-enhanced MRI. Methods: This study included 243 breast lesions confirmed as malignant based on axillary status. Most outcome-predictive features were selected using four machine-learning algorithms. Receiver operating characteristic analysis was used to reflect diagnostic performance. Results: Least absolute shrinkage and selection operator was used to dimensionally reduce 1137 radiomics features to three features. Three optimal radiomics features were used to model construction. The logistic regression model achieved an accuracy of 97% and 85% in the training and test groups. Clinical utility was evaluated using decision curve analysis. Conclusion: The novel combination of radiomics analysis and machine-learning algorithm could predict axillary metastasis and prevent invasive manipulation.

Rewiring of the Endocrine Network in Triple-Negative Breast Cancer.

The immunohistochemical definition of estrogen/progesterone receptors dictates endocrine feasibility in the treatment course of breast cancer. Characterized by the deficiency of estrogen receptor α, ERα-negative breast cancers are dissociated from any endocrine regimens in the routine clinical setting, triple-negative breast cancer in particular. However, the stereotype was challenged by triple-negative breast cancers' retained sensitivity and vulnerability to endocrine agents. The interplay of hormone action and the carcinogenic signaling program previously underscored was gradually recognized along with the increasing investigation. In parallel, the overlooked endocrine-responsiveness in ERα-negative breast cancers attracted attention and supplied fresh insight into the therapeutic strategy in an ERα-independent manner. This review elaborates on the genomic and non-genomic steroid hormone actions and endocrine-related signals in triple-negative breast cancers attached to the hormone insensitivity label. We also shed light on the non-canonical mechanism detected in common hormone agents to showcase their pleiotropic effects.

SEC61G regulates breast cancer cell proliferation and metastasis by affecting the Epithelial-Mesenchymal Transition.

Breast cancer is a common malignant tumor for women and its incidence has increased constantly in recent decades. The underlying molecular means of breast tumorigenesis endure uncertain. With the sequencing expertise, we found that the SEC61G gene is overexpressed in tumor tissues. However, the biological function of SEC61G in breast malignancy has yet to be determined. We investigated the SEC61G expression level, genetic alteration, IHC, immune infiltration, diagnostic value, survival analysis, and functional enrichment analysis by bioinformatics analysis. Then, vitro experiments were done. We investigated that SEC61G was greater in breast cancer tissues related to adjacent non-tumor tissues through qRT-PCR. We performed proliferation, colony formation, migration, invasion assays, and EMT-related phenotype to determine the specific biological functions of SEC61G in breast cancer cell lines (MDA-MB-231, BT-549) transfected with small interfering RNA. SEC61G expression and exon expression were higher in the tumor while the level of SEC61G methylation was higher in normal tissues. The expression level of SEC61G was connected with immune infiltration and survival and was an effective diagnostic and prognostic indicator. The functional enrichment analysis of SEC61G prompted that SEC61G might play a tumor-promoting role via the EMT pathway. In vitro experiments indicated that knocking down SEC61G considerably impaired the colony formation, cck-8, migration, and invasion, and induced apoptosis of the breast cancer cell lines. The vitro experiments also indicated that ectopic expression of SEC61G could influence EMT. This study revealed that SEC61G plays vital tumorigenic functions and acts as a novel oncogene in breast cancer.

Development and validation of a machine learning algorithm-based risk prediction model of pressure injury in the intensive care unit.

The study aimed to establish a machine learning-based scoring nomogram for early recognition of likely pressure injuries in an intensive care unit (ICU) using large-scale clinical data. A retrospective cohort study design was employed to develop and validate a top-performing clinical feature panel accessibly in the electronic medical records (EMRs), which was in the mode of a quantifiable nomogram. Clinical factors regarding demographics, admission cause, clinical laboratory index, medical history and nursing scales were extracted as risk candidates. The performance improvement was based on the application of the machine learning technique, comprising logistic regression, decision tree and random forest algorithm with five-fold cross-validation (CV) technique. The comprehensive assessment of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC) was considered in the evaluation of predictive performance. The receiver operating characteristic curves revealed the top performance for the logistic regression model in respect to machine learning improvement, achieving the highest sensitivity and AUC among three types of classifiers. Compared against the 23-point Braden scale routinely recorded online, an incorporated nomogram of logistic regression model and Braden scale achieved the best performance with an AUC of 0.87 ± 0.07 and 0.84 ± 0.05 in training and test cohort, respectively. Our findings suggest that the machine learning technique potentiated the limited predictive validity of routinely recorded clinical data on pressure injury development during ICU hospitalisation. Easily accessible electronic records held the potentials to substitute the traditional Braden score in the prediction of pressure injury in intensive care unit. Preoperative prediction of pressure injury facilitates the exemption from the severe consequences.

ErbB inhibitors as neoadjuvant therapy for triple-positive breast cancer: a network meta-analysis.

BACKGROUND: Evidence on the effectiveness of ErbB inhibitor interventions for women with triple-positive breast cancer (TPBC) is scarce. Exposure to hormone receptors was reported to eclipse targeted intervention effectiveness. Here, we aimed to explore the optimum targeted regimen for TPBC. METHODS: We conducted a thorough search of the literature focusing on neoadjuvant targeted therapy with both hormone receptor-positive and HER2 (ErbB2)-positive patients and performed a network meta-analysis comparing the regimens using a random-effects model. The rate of pathological complete response (pCR) (ypT0/is) was the primary outcome. The odds ratio (OR) with 95% confidence interval (CI) was used to assess the association among twelve regimens. RESULTS: Thirteen studies meeting the inclusion criteria were included. Significantly more TPBC patients receiving ado-trastuzumab emtansine plus lapatinib experienced pCR events than other patients. In the high-performance ranking of the twelve regimens, ado-trastuzumab emtansine plus lapatinib (TDM-1+L) ranked top, followed by ado-trastuzumab emtansine (TDM-1), trastuzumab plus carboplatin, taxanes and pertuzumab (TCHP), trastuzumab plus docetaxel and lapatinib (THL), trastuzumab, taxanes and pertuzumab (THP), ado-trastuzumab emtansine plus pertuzumab (TDM1+P), trastuzumab plus taxanes (TH), trastuzumab plus taxanes and neratinib, taxanes plus pertuzumab (HP), taxanes and neratinib (HN), trastuzumab plus lapatinib (TL), trastuzumab plus pertuzumab (TP) in sequence. CONCLUSION: Double-targeted therapy in chemotherapy-based regimens was associated with better pCR than single-targeted therapy, and TDM-1+L stood out. For either single-targeted or double-targeted therapies, regimens free of chemotherapy were always worse than those with targeted therapy. Our data support guidelines that recommend combinations of chemotherapies plus targeted therapies in the neoadjuvant setting for early TPBC.

SYT12 is a novel oncogene that promotes thyroid carcinoma progression and metastasis.

Background: Thyroid malignancy is the most frequent endocrine malignant tumor whose incidence is still increasing. Mechanisms genomic variations play a major part in the pathogenesis of many types of malignancy. Synaptotagmin 12 (SYT12) is a member gene of the synaptotagmins family and SYT12's variants were shown to be associated with some malignancies. Nevertheless, SYT12's specific function and probable clinical value in papillary cancer were still unknown. Methods: We conducted complete genome sequence of 39 pairs PTC malignant neoplasm and matched non-neoplastic tissues. We found that SYT12 was significantly overexpressed in thyroid malignancy. Next, we investigated the expression level of SYT12 and the relation between clinical information and SYT12 expression in thyroid cancer in the Cancer Genome Atlas (TCGA). QRt-PCR of else 40 pairs local verified cohort was performed to confirm the sequencing data and TCGA cohort. Then, we used small interfering RNA (si-RNA) to knock down the expression of SYT12 in PTC cells. Finally, proliferation, cell colony formation, migration, invasion, and apoptosis assays were done to demonstrate the function of SYT12. Results: SYT12 is significantly overexpressed and higher expression of SYT12 upsurges the risk of lymph node metastatic and incidence rate of primary neoplasm multivariate focus type and classical histological type for PTC patients in TCGA cohort. In vitro experiments, the results of functional assays presented that knock-down of SYT12 inhibited the cell proliferation, cell colony formation, trans-well migration, and trans-well invasion and promoted cell apoptotic in PTC cell lines. Conclusion: SYT12 was a novel oncogene that promotes thyroid carcinoma progression and metastasis potential and a potential biomarker for diagnosis and treatment in PTC.

Pan-cancer analysis of the prognostic and immunological role of PSMB8.

Recently some evidence has demonstrated the significance of PSMB8 in various malignancies. Nevertheless, PSMB8 (proteasome subunit beta 8), more familiar in the field of immunology contributing to the process of antigen presentation, is indeterminate in the role as a survival predictor of human pan-cancer. Besides, how PSMB8 interacts with immune cell infiltration in the tumor microenvironment requires further research. We then penetrated into the analysis of the PSMB8 expression profile among 33 types of cancer in the TCGA database. The results show that overexpression of PSMB8 was associated with poor clinical outcomes in overall survival (Sartorius et al. in Oncogene 35(22):2881-2892, 2016), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) in most cancer varieties. In addition, there existed distinctly positive correlations between PSMB8 and immunity, reflected straightforwardly in the form of immune scores, tumor-infiltrating immune cells (TIICs) abundance, microsatellite instability, tumor mutation burden, and neoantigen level. Notably, specific markers of dendrite cells exhibited the tightest association with PSMB8 expression in terms of tumor-related immune infiltration patterns. Moreover, gene enrichment analysis showed that elevated PSMB8 expression was related to multiple immune-related pathways. We finally validated the PSMB8 expression in our local breast samples via quantitative PCR assays and concluded that PSMB8 appeared to perform well in predicting the survival outcome of BRCA patients. These findings elucidate the pivotal role of the antigen presentation-related gene PSMB8, which could potentially serve as a robust biomarker for prognosis determination in multiple cancers.

Genomic Instability-Related LncRNA Signature Predicts the Prognosis and Highlights LINC01614 Is a Tumor Microenvironment-Related Oncogenic lncRNA of Papillary Thyroid Carcinoma.

BACKGROUND: Genomic instability (GI) is among the top ten characteristics of malignancy. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in GI. So far, the clinical value of GI-related lncRNAs (GIlncs) in papillary thyroid cancer (PTC) has not been clarified. METHODS: Integrative analysis of lncRNA expression and somatic mutation profiles was performed to identify GIlncs. Analysis of differentially expressed lncRNAs in the group with high- and low- cumulative number of somatic mutations revealed significant GIlncs in PTC. Univariate and multivariate Cox proportional hazard regression analyses were performed to identify hub-GIlncs. RESULTS: A computational model based on four lncRNAs (FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1) was identified as a quantitative index using an in-silicon discovery cohort. GILS score was significantly associated with poor prognosis, as validated in the TCGA dataset and further tested in our local RNA-Seq cohort. Moreover, a combination of clinical characteristics and the composite GILS-clinical prognostic nomogram demonstrates satisfactory discrimination and calibration. Furthermore, the GILS score and FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 were also associated with driver mutations and multiple clinical-pathological variables, respectively. Moreover, RNA-Seq confirmed the expression patterns of FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 in PTC and normal thyroid tissues. Biological experiments demonstrated that downregulated or overexpressed LINC01614 affect PTC cell proliferation, migration, and invasion in vitro. Activation of the stromal and immune cell infiltration was also observed in the high LINC01614 group in the PTC microenvironment. CONCLUSION: In summary, we identified a signature for clinical outcome prediction in PTC comprising four lncRNAs associated with GI. A better understanding of the GI providing an alternative evaluation of the progression risk of PTC. Our study also demonstrated LINC01614 as a novel oncogenic lncRNA and verified its phenotype in PTC.

PSD3 is an oncogene that promotes proliferation, migration, invasion, and G1/S transition while inhibits apoptotic in papillary thyroid cancer.

Background: The morbidity of thyroid cancer is gradually increasing, meanwhile, the average age of the morbidity population also becomes younger. Mechanisms genomic variations serve an important function for the pathogenesis of many cancer types. Pleckstrin and sec7 domain-containing 3 (PSD3), also known as EFA6R, was shown to be associated with some cancers such as acute myeloid leukemia, breast cancer metastasis, and astrocytoma. But it was unknown that whether PSD3 took effect and how did it work in thyroid cancer. Methods: We guessed that PSD3 might play an important role in thyroid cancer by consulting previous literature. Then, we analyzed the level of PSD3 expression in thyroid malignancy and the connection with clinical manifestation in The Cancer Genome Atlas (TCGA). And RNA extraction, reverse transcription, and real-time quantitative polymerase chain reaction (qRt-PCR) of 40 pairs of local samples were done to verify the result of TCGA. Then, PSD3 was knocked down by small interfering RNA (siRNA) for flowing functional experiments. Results: Bioinformatics and qRt-PCR analysis shown PSD3 was overexpressed in papillary thyroid cancer (PTC) and connected with the histological type (P=0.009) and risk of lymph node metastasis (P=0.016). In vitro assays, we confirmed that down-regulation PSD3 could not only suppress the cell proliferation, colony formation, cell migration, cell invasion, and G1/S cell cycle transition but also promote apoptosis in PTC cells. Conclusion: PSD3 promotes proliferation, migration, invasion, and G1/S transition while inhibits apoptotic in PTC and a possible biomarker in PTC.

Genetic landscape of breast cancer and mutation tracking with circulating tumor DNA in Chinese women.

Considerable efforts have been devoted to exploring the breast cancer mutational landscape to understand its genetic complexity. However, no studies have yet comprehensively elucidated the molecular characterization of breast tumors in Chinese women. This study aimed to determine the potential clinical utility of peripheral blood assessment for circulating tumor-derived DNA (ctDNA) and comprehensively characterize the female Chinese population's genetic mutational spectrum. We used Omi-Seq to create cancer profiles of 273 patients enrolled at The First Affiliated Hospital of Wenzhou Medical University. The gene landscape results indicate PIK3CA and TP53 as the most frequently detected genes, followed by ERBB2, in Chinese breast cancer patients. The accuracy of ERBB2 copy number variations in tissue/formalin-fixed and paraffin-embedded samples was 95% with 86% sensitivity and 99% specificity. Moreover, mutation numbers varied between different molecular cell-free DNA subtypes, with the basal-like patients harboring a higher number of variants than the luminal patients. Furthermore, ratio changes in the max ctDNA allele fraction highly correlated with clinical response measurements, including cancer relapse and metastasis. Our data demonstrate that ctDNA characterization using the Omi-Seq platform can extend the capacity of personalized clinical cancer management.

Identification and validation of L Antigen Family Member 3 as an immune-related biomarker associated with the progression of papillary thyroid cancer.

BACKGROUND: Papillary thyroid cancer (PTC) is heterogeneous cancer with many different immune cells involved in its pathogenesis. L Antigen Family Member 3 (LAGE3) is an ESO/LAGE gene family member that has not been extensively studied in PTC. METHODS: Comprehensive bioinformatics analyses of LAGE3 were based on The Cancer Genome Atlas, Gene Expression Omnibus, and Genomics of Drug Sensitivity in Cancer (GDSC) databases. We also performed RNA-sequencing on 78 paired samples from local PTC patients. RESULTS: We observed that LAGE3 was significantly up-regulated in most solid tumor types, including PTC compared with corresponding normal tissues. The high level of LAGE3 was also significantly associated with advanced malignancy. LAGE3 expression was significantly associated with cancer-related pathways, biochemical metabolism, and immune-related terms. Further, tumor microenvironment analysis indicated LAGE3 was positively correlated with different immune cells infiltrating levels and the activity of different steps of the cancer-immunity cycle. Analyses based on the GDSC database revealed that low levels of LAGE3 might be resistant to WZ3105, I-BET-762, and PHA-793887. In addition, the experimental results validated that knocking down LAGE3 could affect proliferation, migration, and invasion in the PTC cell lines. CONCLUSION: This study discloses that LAGE3 plays an oncogenic and cancer-immunological role, also providing novel PTC biological and clinical implications.