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Abstract Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.

Introduction: The impact of distinct estrogen receptor (ER) and progesterone receptor (PR) expression patterns on tumor behavior and treatment outcomes within HER2-positive breast cancer is not fully explored. This study aimed to comprehensively examine the clinical differences among patients with HER2-positive breast cancer harboring distinct ER and PR expression patterns in the neoadjuvant setting. Methods: This retrospective analysis included 871 HER2-positive breast patients treated with neoadjuvant therapy at our hospital between 2011 and 2022. Comparisons were performed across the three hormone receptor (HR)-specific subtypes, namely the ER-negative/PR-negative/HER2-positive (ER-/PR-/HER2+), the single HR-positive (HR+)/HER2+, and the triple-positive breast cancer (TPBC) subtypes. Results: Of 871 patients, 21.0% had ER-/PR-/HER2+ tumors, 33.6% had single HR+/HER2+ disease, and 45.4% had TPBC. Individuals with single HR+/HER2+ tumors and TPBC cases demonstrated significantly lower pathological complete response (pCR) rates compared to those with ER-/PR-/HER2+ tumors (36.9% vs. 24.3% vs. 49.2%, p

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients’ prognosis and immunotherapy response based on apoptosis-related signature.Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model’s validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer.Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines.Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.

Abstract The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C‐X‐C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single‐cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune‐inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune‐related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune‐related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G‐related genes was developed using multi‐level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune‐active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy.

The development of immune checkpoint inhibitors (ICIs) has significantly advanced cancer treatment. However, their efficacy is not consistent across all patients, underscoring the need for personalized approaches. In this study, we examined the relationship between activated CD4+ memory T cell expression and ICI responsiveness. A notable correlation was observed between increased activated CD4+ memory T cell expression and better patient survival in various cohorts. Additionally, the chemokine CXCL13 was identified as a potential prognostic biomarker, with higher expression levels associated with improved outcomes. Further analysis highlighted CXCL13's role in influencing the Tumor Microenvironment, emphasizing its relevance in tumor immunity. Using these findings, we developed a deep learning model by the Multi-Layer Aggregation Graph Neural Network method. This model exhibited promise in predicting ICI treatment efficacy, suggesting its potential application in clinical practice.

Abstract Background Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. Methods In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. Results The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02–0.06, P

Abstract Aims To construct a quality evaluation index system for clinical drug trials nursing management and obtain the weight of all indicators. Design A mixed‐method research design with a quantitative component was used, primarily qualitative. Methods Through a literature review and semi‐structured interview, an expert consultation questionnaire on the quality of nursing evaluation indicators for clinical drug trials was developed in April 2021. Eighteen experts in clinical drug trial nursing, medical, and pharmacy conducted 2 rounds of consultation according to the Delphi method to determine the indicators for evaluating the quality of clinical drug trial nursing. The weights of each indicator were determined using analytic hierarchical analysis. Results The established quality evaluation system of clinical drug trial nursing mainly includes 3 first‐level indicators, 12 second‐level indicators, and 59 third‐level indicators. The positive coefficients of the two rounds of expert consultation were 90%–100%, and the authority coefficients were 0.831 and 0.885, respectively; the coordination coefficients were 0.189 and 0.214, respectively. The consulting results and weight settings are reliable. The evaluation index system we constructed is in line with the characteristics of the clinical drug trial nursing profession, with clear index levels and strong clinical operability, which can provide a reference for the assessment, monitoring and improvement of nursing quality in clinical drug trials. It will also clarify how nurses contribute to subjects' safety.

Introduction: The relationships between body mass index (BMI) and survival rates are complex, and have not been thoroughly investigated in breast cancer patients who received adjuvant chemotherapy. Methods: We collected data on 2394 patients from two randomized, phase III clinical trials that investigated adjuvant chemotherapy in breast cancer identified in Project Data Sphere. The objective was to examine the effect of baseline BMI, BMI after adjuvant chemotherapy, and BMI change from baseline to post-adjuvant chemotherapy on disease-free survival (DFS) and overall survival (OS). Restricted cubic splines were used to examine potential non-linear associations between continuous BMI value and survival. Stratified analyses involved chemotherapy regimens. Results: Severe obesity (BMI≥40.0 kg/m2) at baseline was independently associated with worse DFS (hazard ration [HR] = 1.48, 95% confidence interval [CI] 1.02–2.16, P = 0.04) and OS (HR = 1.79, 95%CI 1.17–2.74, P = 0.007) compared with underweight/normal weight (BMI≤24.9 kg/m2). A BMI loss >10% was also an independent prognostic factor for adverse OS (HR = 2.14, 95%CI 1.17–3.93, P = 0.014). Stratified analyses revealed that severe obesity adversely affected DFS (HR = 2.38, 95%CI 1.26–4.34, P = 0.007) and OS (HR = 2.90, 95%CI 1.46–5.76, P = 0.002) in the docetaxel-based group, but not in the non-docetaxel-based group. Restricted cubic splines revealed a “J-shaped” association of baseline BMI with risk of recurrence or all-cause death, and this relationship was more pronounced in the docetaxel-based group. Conclusions: In early breast cancer patients treated with adjuvant chemotherapy, baseline severe obesity was significantly linked to worse DFS and OS, and a BMI loss over 10% from baseline to post-adjuvant chemotherapy also negatively affected OS. Moreover, the prognostic role of BMI might differ between docetaxel-based and non-docetaxel-based groups.

Long non-coding RNAs (lncRNAs) play an important role in cancer metastasis. Exploring metastasis-associated lncRNAs and developing effective strategy for targeted regulation of lncRNA function in vivo are of utmost importance for the treatment of metastatic cancer, which however remains a big challenge. Herein, we identified a new functional lncRNA (denoted lncBCMA), which could stabilize the expression of eukaryotic translation elongation factor 1A1 (eEF1A1) via antagonizing its ubiquitination to promote triple-negative breast cancer (TNBC) growth and metastasis. Based on this regulatory mechanism, an endosomal pH-responsive nanoparticle (NP) platform was engineered for systemic lncBCMA siRNA (siBCMA) delivery. This NPs-mediated siBCMA delivery could effectively silence lncBCMA expression and promote eEF1A1 ubiquitination, thereby leading to a significant inhibition of TNBC tumor growth and metastasis. These findings show that lncBCMA could be used as a potential biomarker to predict the prognosis of TNBC patients and NPs-mediated lncBCMA silencing could be an effective strategy for metastatic TNBC treatment.

Summary: Background: Leritrelvir is a novel α-ketoamide based peptidomimetic inhibitor of SARS-CoV-2 main protease. A preclinical study has demonstrated leritrelvir poses similar antiviral activities towards different SARS-CoV-2 variants compared with nirmatrelvir. A phase 2 clinical trial has shown a comparable antiviral efficacy and safety between leritrelvir with and without ritonavir co-administration. This trial aims to test efficacy and safety of leritrelvir monotherapy in adults with mild-to-moderate COVID-19. Methods: This was a randomised, double-blind, placebo-controlled, multicentre phase 3 trial at 29 clinical sites in China. Enrolled patients were from 18 to 75 years old, diagnosed with mild or moderate COVID-19 and not requiring hospitalization. Patients had a positive SARS-CoV-2 nucleic acid test (NAT) and at least one of the COVID-19 symptoms within 48 h before randomization, and the interval between the first positive SARS-CoV-2 NAT and randomization was ≤120 h (5 days). Patients were randomly assigned in a 1:1 ratio to receive a 5-day course of either oral leritrelvir 400 mg TID or placebo. The primary efficacy endpoint was the time from the first dose to sustained clinical recovery of all 11 symptoms (stuffy or runny nose, sore throat, shortness of breath or dyspnea, cough, muscle or body aches, headache, chills, fever ≥37 °C, nausea, vomiting, and diarrhea). The safety endpoint was the incidence of adverse events (AE). Primary and safety analyses were performed in the intention-to-treat (ITT) population. This study is registered with ClinicalTrials.gov, NCT05620160. Findings: Between Nov 12 and Dec 30, 2022 when the zero COVID policy was abolished nationwide, a total of 1359 patients underwent randomization, 680 were assigned to leritrelvir group and 679 to placebo group. The median time to sustained clinical recovery in leritrelvir group was significantly shorter (251.02 h [IQR 188.95–428.68 h]) than that of Placebo (271.33 h [IQR 219.00–529.63 h], P = 0.0022, hazard ratio [HR] 1.20, 95% confidence interval [CI], 1.07–1.35). Further analysis of subgroups for the median time to sustained clinical recovery revealed that (1) subgroup with positive viral nucleic acid tested ≤72 h had a 33.9 h difference in leritrelvir group than that of placebo; (2) the subgroup with baseline viral load >8 log 10 Copies/mL in leritrelvir group had 51.3 h difference than that of placebo. Leritrelvir reduced viral load by 0.82 log10 on day 4 compared to placebo. No participants in either group progressed to severe COVID-19 by day 29. Adverse events were reported in two groups: leritrelvir 315 (46.46%) compared with placebo 292 (43.52%). Treatment-relevant AEs were similar 218 (32.15%) in the leritrelvir group and 186 (27.72%) in placebo. Two cases of COVID-19 pneumonia were reported in placebo group, and one case in leritrelvir group, none of them were considered by the investigators to be leritrelvir related. The most frequently reported AEs (occurring in ≥5% of participants in at least one group) were laboratory finding: hypertriglyceridemia (leritrelvir 79 [11.7%] vs. placebo 70 [10.4%]) and hyperlipidemia (60 [8.8%] vs. 52 [7.7%]); all of them were nonserious. Interpretation: Leritrelvir monotherapy has good efficacy for mild-to-moderate COVID-19 and without serious safety concerns. Funding: This study was funded by the National Multidisciplinary Innovation Team Project of Traditional Chinese Medicine, Guangdong Science and Technology Foundation, Guangzhou Science and Technology Planning Project and R&D Program of Guangzhou Laboratory.

Background: This study aimed to comprehensively explore the clinical significance of PIK3CA mutation in human epidermal growth factor receptor 2 (HER2)-positive breast cancer treated with anti-HER2 therapy. Methods: We systematically searched PubMed, Embase, and the Cochrane databases for eligible studies assessing the association between PIK3CA mutation and outcomes in patients with HER2-positive breast cancer receiving anti-HER2 therapy. The main outcomes included: (1) pathological complete response (pCR) or disease-free survival (DFS) for the neoadjuvant setting; (2) DFS or invasive DFS for the adjuvant setting; (3) objective response rate (ORR), progression-free survival (PFS), time-to-progression (TTP), or overall survival (OS) for the metastatic setting. The mutational landscape of HER2-positive breast cancer according to PIK3CA mutation status was examined based on TCGA breast cancer dataset. Results: Totally, 43 eligible studies, covering 11,099 patients with available data on PIK3CA mutation status, were identified. In the neoadjuvant setting, PIK3CA mutation was significantly associated with a lower pCR rate (OR=0.23, 95% CI 0.19–0.27, p

AimThe aim of this study was to identify potential safety concerns associated with Sacituzumab Govitecan (SG), an antibody-drug conjugate targeting trophoblastic cell-surface antigen-2, by analyzing real-world safety data from the largest publicly available worldwide pharmacovigilance database.MethodsAll data obtained from the FDA Adverse Event Reporting System (FAERS) database from the second quarter of 2020 to the fourth quarter of 2022 underwent disproportionality analysis and Bayesian analysis to detect and assess the adverse event signals of SG, considering statistical significance when the lower limit of the 95% CI >1, based on at least 3 reports.ResultsTotal of 1072 cases were included. The main safety signals were blood and lymphatic system disorders [ROR(95CI)=7.23 (6.43-8.14)], gastrointestinal disorders [ROR(95CI)=2.01 (1.81-2.22)], and relative infection adverse events, such as neutropenic sepsis [ROR(95CI)=46.02 (27.15-77.99)] and neutropenic colitis [ROR(95CI)=188.02 (120.09-294.37)]. We also noted unexpected serious safety signals, including large intestine perforation [ROR(95CI)=10.77 (3.47-33.45)] and hepatic failure [ROR(95CI)=3.87 (1.45-10.31)], as well as a high signal for pneumonitis [ROR(95CI)=9.93 (5.75-17.12)]. Additionally, age sub-group analysis revealed that geriatric patients (>65 years old) were at an increased risk of neutropenic colitis [ROR(95CI)=282.05 (116.36-683.66)], neutropenic sepsis [ROR(95CI)=101.11 (41.83-244.43)], acute kidney injury [ROR(95CI)=3.29 (1.36-7.94)], and atrial fibrillation [ROR(95CI)=6.91 (2.86-16.69)].ConclusionThis study provides crucial real-world safety data on SG, complementing existing clinical trial information. Practitioners should identify contributing factors, employ monitoring and intervention strategies, and focus on adverse events like neutropenic sepsis, large intestine perforation, and hepatic failure. Further prospective studies are needed to address these safety concerns for a comprehensive understanding and effective management of associated risks.

Abstract Therapy resistance remains a huge challenge for current breast cancer treatments. Exploring molecular mechanisms of therapy resistance might provide therapeutic targets for patients with advanced breast cancer and improve their prognosis. RNA-binding proteins (RBPs) play an important role in regulating therapy resistance. Here we summarize the functions of RBPs, highlight their tremendously important roles in regulating therapy sensitivity and resistance and we also reveal current therapeutic approaches reversing abnormal functions of RBPs in breast cancer.

Abstract The prognosis of human epidermal growth factor receptor 2 (HER2) positive metastatic breast cancer (MBC) remained unsatisfactory currently, more anti-HER2 agents are needed. Here we report a phase I study that evaluated the safety, activity, and biomarkers of LZM005, a HER2 antibody, used as a monotherapy or in combination with trastuzumab plus docetaxel in patients with HER2-positive MBC. From October 2017 to December 2019, 34 patients received LZM005 (14 monotherapy, 20 combination therapy). No DLT was observed. The common adverse events (AEs) in phase Ia included diarrhea (21.4%), infusion reaction (21.4%), and hypertriglyceridemia (21.4%), while those in phase Ib were leukopenia (85.0%), neutropenia (75.0%), anemia (60.0%), diarrhea (60.0%), and rash/pruritus (50.0%). All AEs were manageable. In phase Ia, partial response (PR) was achieved in one case (1/14, overall response rate [ORR]: 7.1%); the disease control rate was 42.90% (6/14). In phase Ib, 11 patients (55.0%) achieved PR, and eight (40.0%) had stable disease. The ORR was 100% (6/6) in trastuzumab-naive and 35.7% (5/14) in trastuzumab-pretreated patients. Biomarker analysis showed that chromatin remodeling genes KMT2B and BRWD1 were associated with better progression-free survival. LZM005 is well tolerated and shows potent activity in patients with HER2-positive MBC.

Background: As the structure of the human diet changes, the prevalence of hyperuricemia is increasing each year. Hyperuricemia and its comorbidities, such as gout, severely affect quality of life. Moreover, hyperuricemia causes renal impairment and is associated with chronic kidney disease. Topiroxostat, a selective xanthine oxidoreductase inhibitor, has been approved to treat hyperuricemia or gout in Japan. Topiroxostat has shown good tolerance and efficacy in the Japanese population. However, its pharmacokinetic (PK) characteristics, efficacy, and safety in the Chinese population remains unknown. Objective: This trial evaluated the PK profile, safety, efficacy, and food effects of Topiroxostat in healthy Chinese participants. Methods: The major endpoint was determination of the PK profile of Topiroxostat. Topiroxostat concentrations were detected with LC-MS/MS. PK parameters were calculated in Phoenix WinNonlin 8.1. Minor endpoints were safety and efficacy assessments. Assessment of adverse events and safety was performed by clinicians. Plasma uric acid concentration (ΔECmax and ΔAUEC) was determined as the pharmacodynamic index. This study consisted of three arms: single ascending dose (20, 40, and 80 mg, N = 10), multiple dose (80 mg BID, 7 days, N = 10), and food effects (40 mg single dose, fasting-fed cross-over design, N = 10). Results: In the single-ascending-dose arm, Topiroxostat showed rapid absorption and excretion, with Tmax

Understanding the mutation landscape of cancer may enable the development of more targeted therapies. Here, the authors sequence a panel of genes in a large Asian cohort and compare to American cohorts and find 64% of the Asian patients have actionable mutations.

Background Immune checkpoint inhibitors (ICIs)-based therapy, is regarded as one of the major breakthroughs in cancer treatment. However, it is challenging to accurately identify patients who may benefit from ICIs. Current biomarkers for predicting the efficacy of ICIs require pathological slides, and their accuracy is limited. Here we aim to develop a radiomics model that could accurately predict response of ICIs for patients with advanced breast cancer (ABC).Methods Pretreatment contrast-enhanced CT (CECT) image and clinicopathological features of 240 patients with ABC who underwent ICIs-based treatment in three academic hospitals from February 2018 to January 2022 were assigned into a training cohort and an independent validation cohort. For radiomic features extraction, CECT images of patients 1 month prior to ICIs-based therapies were first delineated with regions of interest. Data dimension reduction, feature selection and radiomics model construction were carried out with multilayer perceptron. Combined the radiomics signatures with independent clinicopathological characteristics, the model was integrated by multivariable logistic regression analysis.Results Among the 240 patients, 171 from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center were evaluated as a training cohort, while other 69 from Sun Yat-sen University Cancer Center and the First Affiliated Hospital of Sun Yat-sen University were the validation cohort. The area under the curve (AUC) of radiomics model was 0.994 (95% CI: 0.988 to 1.000) in the training and 0.920 (95% CI: 0.824 to 1.000) in the validation set, respectively, which were significantly better than the performance of clinical model (0.672 for training and 0.634 for validation set). The integrated clinical-radiomics model showed increased but not statistical different predictive ability in both the training (AUC=0.997, 95% CI: 0.993 to 1.000) and validation set (AUC=0.961, 95% CI: 0.885 to 1.000) compared with the radiomics model. Furthermore, the radiomics model could divide patients under ICIs-therapies into high-risk and low-risk group with significantly different progression-free survival both in training (HR=2.705, 95% CI: 1.888 to 3.876, p

Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients’ transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

Therapeutic options for patients with triple-negative breast cancer (TNBC) in later-line setting are limited. Here the authors report the results of a phase 2 clinical trial to evaluate efficacy and safety of the combination of camrelizumab (anti-PD1), apatinib (VEGFR2 inhibitor), and eribulin in patients with heavily pre-treated advanced TNBC.

BackgroundBreast cancer has become the malignancy with the highest mortality rate in female patients worldwide. The limited efficacy of immunotherapy as a breast cancer treatment has fueled the development of research on the tumor immune microenvironment.MethodsIn this study, data on breast cancer patients were collected from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts. Differential gene expression analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed to select overall survival (OS)-related, tumor tissue highly expressed, and immune- and inflammation-related genes. A tumor immune-inflammation signature (TIIS) consisting of 18 genes was finally screened out in the LASSO Cox regression model. Model performance was assessed by time-dependent receiver operating characteristic (ROC) curves. In addition, the CIBERSORT algorithm and abundant expression of immune checkpoints were utilized to clarify the correlation between the risk signature and immune landscape in breast cancer. Furthermore, the association of IL27 with the immune signature was analyzed in pan-cancer and the effect of IL27 on the migration of breast cancer cells was investigated since the regression coefficient of IL27 was the highest.ResultsA TIIS based on 18 genes was constructed via LASSO Cox regression analysis. In the TCGA-BRCA training cohort, 10-year AUC reached 0.89, and prediction performance of this signature was also validated in the METABRIC set. The high-risk group was significantly correlated with less infiltration of tumor-killing immune cells and the lower expression level of the immune checkpoint. Furthermore, we recommended some small-molecule drugs as novel targeted drugs for new breast cancer types. Finally, the relationship between IL27, a significant prognostic immune and inflammation cytokine, and immune status was analyzed in pan-cancer. Expression of IL27 was significantly correlated with immune regulatory gene expression and immune cell infiltration in pan-cancer. Furthermore, IL27 treatment improved breast cancer cell migration.ConclusionThe TIIS represents a promising prognostic tool for estimating OS in patients with breast cancer and is correlated with immune status.

Background: One-third of patients with hormone receptor (HR)-positive breast cancers fail to respond to hormone therapy, and some patients even progress within two years of adjuvant endocrine therapy (ET) toward primary endocrine resistance. However, there is no effective way to predict endocrine resistance. Objective: To build a model that incorporates the radiomic signature of pretreatment magnetic resonance imaging (MRI) with clinical information to predict endocrine resistance. Methods: Clinical data of non-metastatic breast cancer patients diagnosed between May 1, 2015 and December 31, 2018 and preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were retrospectively collected from three hospitals in China. The significant clinicopathological characteristics and radiomic signatures were included in multivariable logistic regression to establish a combined model to predict endocrine resistance in the training set, and validate the internal and external validation set. Results: A total of 744 female non-metastatic breast cancer patients from three hospitals in China were included. In the training cohort, the AUC of the Radiomic-Clinical combined model to predict endocrine resistance was 0.975, which was higher than clinical model (0.849), IHC4 model (0.682) and similar as radiomic model (0.941). Also, the AUC of the combined model in the internal (0.921) and external validation cohort (0.955) were higher than clinical model and IHC4 model. The sensitivity of combined model was higher than radiomic alone, and got the best thresholding of the AUC. Conclusion: This study developed and validated a pretreatment multiparametric MRI-based radiomic-clinical combined model and showed good performance in predicting endocrine resistance.

ObjectiveThe aim of the study is to develop and validate a deep learning model to predict the platinum sensitivity of patients with epithelial ovarian cancer (EOC) based on contrast-enhanced magnetic resonance imaging (MRI).MethodsIn this retrospective study, 93 patients with EOC who received platinum-based chemotherapy (≥4 cycles) and debulking surgery at the Sun Yat-sen Memorial Hospital from January 2011 to January 2020 were enrolled and randomly assigned to the training and validation cohorts (2:1). Two different models were built based on either the primary tumor or whole volume of the abdomen as the volume of interest (VOI) within the same cohorts, and then a pre-trained convolutional neural network Med3D (Resnet 10 version) was transferred to automatically extract 1,024 features from two MRI sequences (CE-T1WI and T2WI) of each patient to predict platinum sensitivity. The performance of the two models was compared.ResultsA total of 93 women (mean age, 50.5 years ± 10.5 [standard deviation]) were evaluated (62 in the training cohort and 31 in the validation cohort). The AUCs of the whole abdomen model were 0.97 and 0.98 for the training and validation cohorts, respectively, which was better than the primary tumor model (AUCs of 0.88 and 0.81 in the training and validation cohorts, respectively). In k-fold cross-validation and stratified analysis, the whole abdomen model maintained a stable performance, and the decision function value generated by the model was a prognostic indicator that successfully discriminates high- and low-risk recurrence patients.ConclusionThe non-manually segmented whole-abdomen deep learning model based on MRI exhibited satisfactory predictive performance for platinum sensitivity and may assist gynecologists in making optimal treatment decisions.

Background: Since lobaplatin (LBP) has been approved to treat metastatic breast cancer in China, this study aimed to evaluate the safety and efficacy of LBP-based chemotherapy in clinical practice. Methods: This trial was a prospective, open-label, multicenter phase IV clinical trial that enrolled patients with unresectable locally advanced or recurrent/metastatic breast cancer from 34 sites between July 2013 and March 2017. Patients were treated with LBP monotherapy or in combination for four to six cycles. The primary endpoint was safety. Secondary endpoints included progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR). Results: A total of 1179 patients were analyzed; 59 (5.0%) were treated with LBP alone, 134 (11.4%) with LBP plus paclitaxel, 263 (22.3%) with LBP plus docetaxel, 237 (20.1%) with LBP plus gemcitabine, 403 (34.2%) with LBP plus vinorelbine, and 83 (7.0%) with other LBP-based regimens. The overall incidence of adverse events (AEs) was 95.2%, and 57.9% of patients had grade >3 AEs. The most common grade >3 AEs were neutropenia (43.9%), leukopenia (39.4%), anemia (17.8%), and thrombopenia (17.7%). LBP monotherapy showed the lowest incidence of grade >3 AEs (39.0%), followed by LBP plus docetaxel (52.9%), LBP plus paclitaxel (59.0%), LBP plus vinorelbine (62.5%), and LBP plus gemcitabine (62.9%). The ORR and DCR were 36.8 and 77.0%, respectively. The median PFS was 5.5 months (95% confidence interval: 5.2–5.9). Conclusion: LBP-based chemotherapy shows favorable efficacy in patients with advanced breast cancer, with manageable safety profile. Trial registration: This trial was registered with ChiCTR.org.cn, ChiCTR-ONC-13003471.

The experimental drug SHR6390 has anti-tumor activity as a cyclin dependent kinase 4/6 inhibitor and is metabolized primarily by the cytochrome P450 3A4 enzyme. Therefore, The purpose of this trial was to evaluate the safety and pharmacokinetics of SHR6390, a potent cytochrome P450 3A4 inhibitor, in healthy Chinese subjects. In this trial study, 18 subjects received a single oral dose of SHR6390 50 mg on day 1, multiple doses of 200 mg itraconazole on days 12–24 for 13 days, and a single oral dose of SHR6390 50 mg on day 15. After coadministration with itraconazole, the maximum plasma concentration (Cmax) of SHR6390 increased by 70.7% (from 14.3 ng/ml to 24.5 ng/ml), and the area under the time curve from 0 to T (AUC0-T) increased by 110.8% from 468 h∙ng/mL to 988 h∙ng/mL. The area under the concentration-time curve extrapolated to ∞(AUC0-∞) increases from 509 H∙ng/mL to 1,040 h∙ng/mL, an increase of 105.1%. Oral gap (CL/F) decreased (47.9 L/h and 98.3 L/h) and apparent volume of distribution (Vz/F) decreased (4190 L and 5890 L). According to common terminology criteria, 15 32 adverse events were reported in 18 subjects (AEs) (27 SHR6390-related AEs and 15 Itraconazole-related AEs), AEs were all Class 1 adverse events. Overall, co-administration of Itraconazole increased the plasma exposure of SHR6390 in healthy subjects. Both SHR6390 alone and co-administration of Itraconazole showed acceptable safety profiles, which warrants further investigation. The experimental drug SHR-6390 of this clinical trial has been applied for registration, which is classified as Chemical drugs Class 1. The study drug SHR6390 registration number:ClinicalTrials.gov Identifier: NCT04423601 (https://clinicaltrials.gov/)

HIF1alpha is reported to drive tumourigenesis through activating glycolysis. Here, the authors show that HIFAL, the HIF1alpha antisense long non-coding RNA, and HIF1alpha form a positive feed-forward loop which is essential for HIF1a-mediated metabolic reprogramming and oncogenic role.

This study aimed to analyze and discuss the biomarkers of PEGylated liposomal doxorubicin (PLD) injection-induced hypersensitivity reactions (HSRs) in advanced breast cancer patients. Fourteen patients from Sun Yat-sen Memorial Hospital were included in the study between April 15th, 2020 and April 14th, 2021. Patient plasma was collected 30 min before PLD injection. HSRs were found to occur in a total of 9 patients (64.3%). No association was found between HSRs and various patient characteristics such as age, body surface area, anthracycline treatment history, IgE, and complement 3 and 4 (p > 0.05). Non-targeted metabolomics analysis of patient plasma was performed, and several metabolites showed significant association with HSRs. In particular, l-histidine (fold change = 91.5, p = 0.01) showed significantly higher levels in the immediate HSR group, while myristicin (fold change = 0.218, p = 0.003), urocanic acid (fold change = 0.193, p = 0.007), and d-aldose (fold change = 0.343, p = 0.003) showed significantly lower levels in the same group. In vivo experiments showed that exogenous histidine aggravated HSRs and increased IgE plasma levels in rats following the injection of PLD. Histidine can be decarboxylated to histamine by histidine decarboxylase. Histidine decarboxylase inhibitor 4-bromo-3-hydroxybenzoic acid improved symptoms and IgE levels in vivo. These findings suggested that l-histidine can be a potential biomarker for PLD-induced HSR. Moreover, an antihistamine drug, histidine decarboxylase inhibitor, or dietary histidine management could be used as potential preventive measures. Furthermore, metabolomics research could serve as a powerful method to explore biomarkers or uncover mechanisms of drug side effects.

Chemotherapy-induced neutropenia (CIN) is a potentially fatal and common complication in myelosuppressive chemotherapy. The timing and grade of CIN may play prognostic and predictive roles in cancer therapy. CIN is associated with older age, poor functional and nutritional status, the presence of significant comorbidities, the type of cancer, previous chemotherapy cycles, the stage of the disease, specific chemotherapy regimens, and combined therapies. There are many key points and new challenges in the management of CIN in adults including: (1) Genetic risk factors to evaluate the patient’s risk for CIN remain unclear. However, these risk factors urgently need to be identified. (2) Febrile neutropenia (FN) remains one of the most common reasons for oncological emergency. No consensus nomogram for FN risk assessment has been established. (3) Different assessment tools [e.g., Multinational Association for Supportive Care in Cancer (MASCC), the Clinical Index of Stable Febrile Neutropenia (CISNE) score model, and other tools] have been suggested to help stratify the risk of complications in patients with FN. However, current tools have limitations. The CISNE score model is useful to support decision-making, especially for patients with stable FN. (4) There are still some challenges, including the benefits of granulocyte colony stimulating factor treatment and the optimal antibiotic regimen in emergency management of FN. In view of the current reports, our group discusses the key points, new challenges, and management of CIN.

Acquired resistance to chemotherapy can lead to multi-drug resistance and poor prognosis in cancer. Here, the authors show that Rac1 increases glycolysis and non-oxidative pentose phosphate pathway activity leading to neoadjuvant chemotherapy (NAC) resistance, thus its inhibition sensitizes resistant breast cancer PDXs to NAC.

The Golgi apparatus (GA) is a cellular organelle that participates in the packaging, modification, and transport of proteins and lipids from the endoplasmic reticulum to be further fabricated before being presented to other cellular components. Recent studies have demonstrated that GA facilitates numerous cellular processes in cancer development. Therefore, this study aimed to establish a novel lung adenocarcinoma (LUAD) risk evaluation model based on GA gene signatures. In this study, we used TCGA-LUAD (n = 500) as the training cohort and GSE50081 (n = 127), GSE68465 (442), and GSE72094 (398) as the validation cohorts. Two immunotherapy datasets (GSE135222 and GSE126044) were also obtained from a previous study. Based on machine algorithms and bioinformatics methods, a GA gene-related risk score (GARS) was established. We found that the GARS independently predicted the prognosis of LUAD patients and remained effective across stages IA to IIIA. Then, we identified that the GARS was highly correlated with mutations in P53 and TTN. Further, this study identified that GARS is related to multiple immune microenvironmental characteristics. Furthermore, we investigated GSE135222 and GSE126044 and found that a lower GARS may be indicative of an improved therapeutic effect of PD-1/PD-L1 therapy. We also found that high GARS may lead to a better response to multiple anticancer drugs. Finally, we established a nomogram to better guide clinical application. To our knowledge, this is the first study to demonstrate a novel GA signature-based risk score formula to predict clinical prognosis and guide the treatment of LUAD patients.

Background: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), is associated with poor prognosis. However, current stage-based clinical methods are insufficient for survival prediction and decision-making. This study aimed to establish a novel model for evaluating the risk of LUAD based on hypoxia, immunity, and epithelial-mesenchymal transition (EMT) gene signatures.Methods: In this study, we used data from TCGA-LUAD for the training cohort and GSE68465 and GSE72094 for the validation cohorts. Immunotherapy datasets GSE135222, GSE126044, and IMvigor210 were obtained from a previous study. Using bioinformatic and machine algorithms, we established a risk model based on hypoxia, immune, and EMT gene signatures, which was then used to divide patients into the high and low risk groups. We analyzed differences in enriched pathways between the two groups, following which we investigated whether the risk score was correlated with stemness scores, genes related to m6A, m5C, m1A and m7G modification, the immune microenvironment, immunotherapy response, and multiple anti-cancer drug sensitivity.Results: Overall survival differed significantly between the high-risk and low-risk groups (HR = 4.26). The AUCs for predicting 1-, 3-, and 5-year survival were 0.763, 0.766, and 0.728, respectively. In the GSE68465 dataset, the HR was 2.03, while the AUCs for predicting 1-, 3-, and 5-year survival were 0.69, 0.651, and 0.618, respectively. The corresponding values in the GSE72094 dataset were an HR of 2.36 and AUCs of 0.653, 0.662, and 0.749, respectively. The risk score model could independently predict OS in patients with LUAD, and highly correlated with stemness scores and numerous m6A, m5C, m1A and m7G modification-related genes. Furthermore, the risk model was significantly correlated with multiple immune microenvironment characteristics. In the GSE135222 dataset, the HR was 4.26 and the AUC was 0.702. Evaluation of the GSE126044 and IMvigor210 cohorts indicated that PD-1/PD-LI inhibitor treatment may be indicated in patients with low risk scores, while anti-cancer therapy with various drugs may be indicated in patients with high risk scores.Conclusion: Our novel risk model developed based on hypoxia, immune, and EMT gene signatures can aid in predicting clinical prognosis and guiding treatment in patients with LUAD.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s12943-018-0845-6.

Background: in current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer.Methods: in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n = 106) of patients treated with neoadjuvant chemotherapy in prospective phase 3 trials [NCT01503905], were included to evaluate the predictive value of the two signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). This study was registered with ClinicalTrials.gov, number NCT04003558.Findings: the ALN-tumor radiomic signature for ALN status prediction comprising ALN and tumor radiomic features showed a high prediction quality with AUC of 0·88 in the training cohort, 0·87 in the external validation cohort, and 0·87 in the prospective-retrospective validation cohort. The multiomic signature incorporating tumor and lymph node MRI radiomics, clinical and pathologic characteristics, and molecular subtypes achieved better performance for ALN status prediction with AUCs of 0·90, 0·91, and 0·93 in the training cohort, the external validation cohort, and the prospective-retrospective validation cohort, respectively. Among patients who underwent neoadjuvant chemotherapy in the prospective-retrospective validation cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites.Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics.

Abstract Background The prognosis of ductal carcinoma in situ (DCIS) is reportedly well. Extremely rare patients with DCIS develop distant breast cancer metastasis without locoregional or contralateral recurrence. This is the first report of multiple bones and sigmoid colon metastases from DCIS after mastectomy. Case presentation A 43-year-old woman was diagnosed with DCIS, and she received mastectomy, followed by endocrine therapy and target therapy. During the following-up, convulsions and pain on the legs were complaint. Therefore, Computed Tomography (CT) on bones and positron emission tomography (PET) for whole body were examined in order. Multiple bones and sigmoid colon were under the suspect of metastases, which were then verified by biopsy in the left ilium and colonoscopy respectively. Conclusions This case reveals the heterogeneous behavior and the potential poor outcome of DCIS, regular examination and surveillance are necessary even though the distant metastasis rate in DCIS is low.

PurposeTriple-negative breast cancer (TNBC) is characterized by high malignancy and a poor prognosis. Patients with TNBC who survive longer than 5 years represent a unique portion of the population. This study aimed to analyze the clinicopathological features, explore prognostic factors, and evaluate treatment options for these patients.MethodsA total of 24,943 TNBC patients were enrolled from the national Surveillance, Epidemiology, and End Results (SEER) database between January 2010 and December 2016. The patients were divided into three groups: group 1, survival time

Abstract Resistance to radiotherapy is frequently encountered in clinic, leading to poor prognosis of cancer patients. Long noncoding RNAs (lncRNAs) play important roles in the development of radioresistance due to their functions in regulating the expression of target genes at both transcriptional and posttranscriptional levels. Exploring key lncRNAs and elucidating the mechanisms contributing to radioresistance are crucial for the development of effective strategies to reverse radioresistance, which however remains challenging. Here, actin filament‐associated protein 1 antisense RNA1 (lncAFAP1‐AS1) is identified as a key factor in inducing radioresistance of triple‐negative breast cancer (TNBC) via activating the Wnt/β‐catenin signaling pathway. Considering the generation of a high concentration of reduction agent glutathione (GSH) under radiation, a reduction‐responsive nanoparticle (NP) platform is engineered for effective lncAFAP1‐AS1 siRNA (siAFAP1‐AS1) delivery. Systemic delivery of siAFAP1‐AS1 with the reduction‐responsive NPs can synergistically reverse radioresistance by silencing lncAFAP1‐AS1 expression and scavenging intracellular GSH, leading to a dramatically enhanced radiotherapy effect in both xenograft and metastatic TNBC tumor models. The findings indicate that lncAFAP1‐AS1 can be used to predict the outcome of TNBC radiotherapy and combination of systemic siAFAP1‐AS1 delivery with radiotherapy can be applied for the treatment of recurrent TNBC patients.

Background Previous trials showed that antiangiogenesis or anti-programmed death protein 1/programmed death ligand 1 (PD-1/PD-L1) monotherapy only showed marginal effect in triple-negative breast cancer (TNBC). Preclinical studies demonstrated that antiangiogenic therapy could sensitize breast cancer to PD-1/PD-L1 blockade via reprogramming tumor microenvironment. Combinational treatment of checkpoint blockade and antiangiogenesis for TNBC has not been reported.Methods Patients with advanced TNBC with less than three lines of systemic therapy were enrolled in an open-label, non-comparative, two-arm, phase II trial at Sun Yat-sen Memorial Hospital. Camrelizumab (intravenously every 2 weeks) with apatinib orally at either continuous dosing (d1–d14) or intermittent dosing (d1–d7) was given until disease progression or unacceptable toxicities. Primary endpoint was objective response rate (ORR).Results From January 2018 to April 2019, 40 patients were enrolled, including 10 in the apatinib intermittent dosing cohort and 30 in the apatinib continuous dosing cohort. The ORR was 43.3% (13 of 30) in the continuous dosing cohort, while no objective response was observed in the intermittent dosing cohort. The disease control rate was 63.3% (19 of 30) in the apatinib continuous dosing cohort, and 40.0% (4 of 10) in the apatinib intermittent dosing cohort, respectively. The median progression-free survival (PFS) was 3.7 (95% CI 2.0 to 6.4) months and 1.9 (95% CI 1.8 to 3.7) months in the continuous dosing and intermittent dosing cohort, respectively. In the continuous dosing cohort, the median PFS of patients with partial response (8.3 months, 95% CI 5.9 to not reached) was significantly longer than that of patients with stable disease/progressive disease/not evaluable (2.0 months, 95% CI 1.7 to 3.0). The most common adverse events (AEs) included elevated aspartate aminotransferase/alanine aminotransferase and hand-foot syndrome. Overall, 26.7% and 20.0% of patients experienced grade ≥3 AEs in the continuous dosing and intermittent dosing cohort, respectively. In the continuous dosing cohort, a high percentage of baseline tumor-infiltrating lymphocytes (>10%) was associated with higher ORR and favorable PFS (p=0.029, 0.054, respectively).Conclusions The ORR by this chemo-free regimen was dramatically higher than previously reported ORR by anti-PD-1/PD-L1 antibody or apatinib monotherapy. Camrelizumab combined with apatinib demonstrated favorable therapeutic effects and a manageable safety profile in patients with advanced TNBC.Trial registration number NCT03394287.

Abstract Background Defective autophagy is thought to contribute to the pathogenesis of many diseases, including cancer. Human plasmacytoma variant translocation 1 (PVT1) is an oncogenic long non-coding RNA that has been identified as a prognostic biomarker in pancreatic ductal adenocarcinoma, but how PVT1 operates in the regulation of autophagy in pancreatic ductal adenocarcinoma (PDA) is unclear. Methods PVT1 expression level was detected by quantitative real-time polymerase chain reaction (qRT-PCR) and hybridization in situ (ISH). Western blot or qRT-PCR was performed to assess the ULK1 protein or mRNA level. Autophagy was explored via autophagic flux detection under a confocal microscope and autophagic vacuoles investigation under a transmission electron microscopy (TEM). The biological role of PVT1 in autophagy and PDA development was determined by gain-of-function and loss-of-function assays. Results We found that PVT1 levels paralleled those of ULK1 protein in PDA cancer tissues. PVT1 promoted cyto-protective autophagy and cell growth by targeting ULK1 both in vitro and in vivo. Moreover, high PVT1 expression was associated with poor prognosis. Furthermore, we found that PVT1 acted as sponge to regulate miR-20a-5p and thus affected ULK1 expression and the development of pancreatic ductal adenocarcinoma. Conclusions The present study demonstrates that the “PVT1/miR-20a-5p/ULK1/autophagy” pathway modulates the development of pancreatic ductal adenocarcinoma and may be a novel target for developing therapeutic strategies for pancreatic ductal adenocarcinoma.

Most breast cancer patients are estrogen receptor positive and thus benefit from treatments that inhibit estrogen production; however, one third of tamoxifen-treated patients develops resistance and relapse. Here the authors show that tamoxifen resistant cells are resistant to chemotherapy because of BARD1 and BRCA1 upregulation.

Resistance to therapy is a significant issue for patients with metastatic breast cancer (MBC). Here the authors analyze total miRNA from serum samples of 386 MBC patients before treatment with a follow up of 31 months and define a four miRNA signature that predicts the therapeutic benefit of trastuzumab.

Lung cancer (LC) is a common lethal malignancy with rapid progression and metastasis, and Ring1 and YY1 binding protein (RYBP) has been shown to suppress cell growth in human cancers. This study aimed to investigate the role of RYBP in LC progression and metastasis. In this study, a total of 149 LC patients were recruited, and the clinical stage of their tumors, metastasis status, survival time, presence of epidermal growth factor receptor (EGFR) mutation, and RYBP expression levels were measured. RYBP silencing and overexpression were experimentally performed in LC cell lines and in nude mice, and the expressions of genes in EGFR-related signaling pathways and epithelial-mesenchymal transition (EMT) were detected. The results showed that RYBP was downregulated in LC compared with adjacent normal tissues, and low RYBP expression was associated with a more severe clinical stage, high mortality, high metastasis risk, and poor survival. Cell proliferation and xenograft growth were inhibited by RYBP overexpression, whereas proliferation and xenograft growth were accelerated by RYBP silencing. EGFR and phosphorylated-EGFR levels were upregulated when RYBP was silenced, whereas EGFR, p-EGFR, p-AKT, and p-ERK were downregulated when RYBP was overexpressed. Low RYBP expression was related to a high metastasis risk, and metastasized tumors showed low RYBP levels. Cell migration and invasion were promoted by silencing RYBP but were inhibited by overexpressed RYBP. In addition, the EMT marker vimentin showed diminished expression, and E-cadherin was promoted by the overexpression of RYBP. In conclusion, our data suggest that RYBP suppresses cell proliferation and LC progression by impeding the EGFR-ERK and EGFR-AKT signaling pathways and thereby inhibiting cell migration and invasion and LC metastasis through the suppression of EMT.

The role of long non-coding RNA (lncRNA) in the progression of Nasopharyngeal carcinoma (NPC) has not been fully elucidated. The study was designed to explore the functional role of NKILA, a newly identified lncRNA, in the progression of NPC. We performed a lncRNA expression profile microarray using four NPC and paired para-cancerous tissues. NKILA was identified as a potential functional lncRNA by this lncRNA expression profile. We used 107 paraffin-embedded NPC tissues with different TNM stages to detect the expression of NKILA and analyzed the survival data by Log-rank test and Cox regression. The role of NKILA and its underlying mechanisms in the progression of NPC were evaluated by a series of experiments in vitro and vivo by silencing or expressing NKILA. Compared with control tissues, NKILA expression was identified to be decreased in NPC tissues. Low NKILA expression was correlated with unfavorable clinicopathological features and predicted poor survival outcome in NPC patients. After adjusting for potential confounders, low expression of NKILA was confirmed to be an independent prognostic factor correlated with poor survival outcomes. Furthermore, we found that NKILA overexpression in high-metastatic-potential NPC cells repressed motile behavior and impaired the metastatic capacity in vitro and in vivo. In contrast, RNAi-mediated NKILA depletion increased the invasive motility of cells with lower metastatic potential. Further experiments demonstrated that NKILA regulated the metastasis of NPC through the NF-κB pathway. Taken together, NKILA plays vital roles in the pathogenesis of NPC. The unique histological characteristics of NPC indicate that local inflammation plays a vital role in carcinogenesis of nasopharyngeal carcinoma.

Macrophages are one of the most abundant immune cells in the solid tumor and their increased density is associated with the specific pathological features of cancers, including invasiveness, metastasis, immunosuppression, neovascularization, and poor response to therapy. Therefore, reprogramming macrophage behavior is emerging as a promising therapeutic modality for cancer treatment. RNA interference (RNAi) technology is one of the powerful strategies for the regulation of macrophage activities by silencing specific genes. However, as polyanionic biomacromolecules, RNAi therapeutics such as small interfering RNA (siRNA) cannot readily cross cell membrane and thus specific delivery vehicles are required to facilitate the cytosolic siRNA delivery. Herein, we developed a robust nanoparticle (NP) platform for efficient siRNA delivery and gene silencing in macrophages. This NP platform is composed of biodegradable poly (ethylene glycol)-b-poly (𝜀-caprolactone) (PEG-b-PCL), poly (𝜀-caprolactone)-b-poly (2-aminoethyl ethylene phosphate) (PCL-b-PPEEA), and PCL homopolymer. We chose CC-chemokine ligand 18 (CCL-18) as a proof of concept therapeutic target and our results demonstrate that the CCL-18 silencing in macrophages can significantly inhibit the migration of breast cancer cells. The successful regulation of the macrophage behavior demonstrated herein shows great potential as an effective strategy for cancer therapy.

Colorectal cancer (CRC) is the third most common cancer worldwide and its pathogenesis has been extensively explored over the past decades. Recently, microorganisms in the gastrointestinal tract have emerged as potential etiological agents. In particular, a direct proportional association between Fusobacterium and CRC has been described. Since then, the functional impact of Fusobacterium in CRC development has been studied using various mouse models. Although some epidemiologic studies did not establish an obvious relationship between Fusobacterium and CRC, numerous pathogenic mechanisms leading to the disease have been described. For instance, Fusobacterium can activate the E-cadherin/β-catenin signaling pathway and is associated with particular epigenetic phenotype, such as microsatellite instability (MSI) and hypermethylation, via its strong adhesive and invasive abilities resulting in malignant transformation of epithelial cells. Also, Fusobacterium could alter the tumor microenvironment (TME) significantly by myeloid-derived suppressor cells (MDSCs), tumor associated macrophages (TAMs), and tumor associated neutrophils (TANs) recruitment and local immune suppression. Herein, we provide an in-depth review of the relationship between Fusobacterium and colorectal cancer. In light of the emergence of microbiome-based therapeutics, potential therapies and preventive strategies for colorectal cancer related to Fusobacterium are also discussed.

Aberrant expression of miR-196a has been frequently reported in different cancers including pancreatic cancer. However, its function in pancreatic cancer has not been fully elucidated. Here, we investigated the expression pattern and the biological role of miR-196a in pancreatic cancer cell lines, as well as its interaction with a metastasis-related gene, nuclear factor-kappa-B-inhibitor alpha (NFKBIA). We demonstrated that miR-196a was up-regulated in human pancreatic cancer cell lines compared with immortalized pancreatic ductal epithelial cells by means of microRNAs microarray and qRT-PCR. Furthermore, down-regulation of miR-196a in PANC-1 suppressed its proliferation and migration with an increase in G0/G1 transition and decreased expression of Cyclin D1 and CDK4/6. Meanwhile, an increased expression in E-cadherin and decreased expression in N-cadherin and Vimentin were also observed. We identified a novel miR-196a target, NFKBIA, and down-regulation of miR-196a enhanced the expression of NFKBIA protein. Luciferase assay confirmed that NFKBIA was a direct and specific target of miR-196a. Silencing NFKBIA in PANC-1 cells enhanced its proliferation and migration. Taken together, our findings indicate that miR-196a is highly expressed in pancreatic cancer cell lines, and may play a crucial role in pancreatic cancer proliferation and migration, possibly through its downstream target, NFKBIA. Thus, miR-196a may serve as a potential therapeutic target for pancreatic cancer.

BACKGROUND: It is currently unclear whether the expression of HOX transcript antisense RNA (HOTAIR) correlates with the progression of esophageal cancer. The aim of this study was to examine HOTAIR expression in patients with esophageal squamous cell cancer (ESCC) and explore its clinical significance. METHODS: Differences in the expression of HOTAIR were examined via in situ hybridization (ISH) and quantitative reverse transcriptase PCR (qRT-PCR). The prognostic significance was evaluated using Kaplan-Meier and Cox regression analyses. Proliferation, colony formation and migration assays were performed in ESCC cell lines to determine the function of HOTAIR in the progression of ESCC in vitro. RESULTS: A notably higher level of HOTAIR expression was found in ESCC tissues. High expression levels of HOTAIR in ESCC patients correlated positively with clinical stage, TNM classification, histological differentiation and vital status. HOTAIR expression was found to be an independent prognostic factor in ESCC patients. ESCC patients who expressed high levels of HOTAIR had substantially lower overall 5-year survival rates than HOTAIR-negative patients. In vitro assays of ESCC cell lines demonstrated that HOTAIR mediated the proliferation, colony formation and migratory capacity of ESCC cells. CONCLUSION: HOTAIR is a potential biomarker for ESCC prognosis, and the dysregulation of HOTAIR may play an important role in ESCC progression.