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Abstract Background Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm, enabling rapid and accurate automatic planning. However, most current methods solely focus on conventional intensity-modulated radiation therapy and assume a consistent beam configuration across all patients. This study seeks to develop a more versatile model incorporating variable beam configurations of CK and considering the patient’s anatomy. Methods This study proposed that the AB (anatomy and beam) model be compared with the control Mask (only anatomy) model. These models are based on a 3D U-Net network to investigate the impact of CK beam encoding information on dose prediction. The study collected 86 lung cancer patients who received CK′s built-in MC algorithm plans using different beam configurations for training/validation (66 cases) and testing (20 cases). We compared the gamma passing rate, dose difference maps, and relevant dose-volume metrics to evaluate the model’s performance. In addition, the Dice similarity coefficients (DSCs) were calculated to assess the spatial correspondence of isodose volumes. Results The AB model demonstrated superior performance compared to the Mask model, particularly in the trajectory dose of the beam. The DSCs of the AB model were 20–40% higher than that of the Mask model in some dose regions. We achieved approximately 99% for the PTV and generally more than 95% for the organs at risk (OARs) referred to the clinical planning dose in the gamma passing rates (3 mm/3%). Relative to the Mask model, the AB model exhibited more than 90% improvement in small voxels (p

Abstract Background N4-Acetylcytidine (ac4C), a highly conserved post-transcriptional mechanism, plays a pivotal role in RNA modification and tumor progression. However, the molecular mechanism by which ac4C modification mediates tumor immunosuppression remains elusive in triple-negative breast cancer (TNBC). Methods NAT10 expression was analyzed in TNBC samples in the level of mRNA and protein, and compared with the corresponding normal tissues. ac4C modification levels also measured in the TNBC samples. The effects of NAT10 on immune microenvironment and tumor metabolism were investigated. NAT10-mediated ac4C and its downstream regulatory mechanisms were determined in vitro and in vivo. The combination therapy of targeting NAT10 in TNBC was further explored. Results The results revealed that the loss of NAT10 inhibited TNBC development and promoted T cell activation. Mechanistically, NAT10 upregulated JunB expression by increasing ac4C modification levels on its mRNA. Moreover, JunB further up-regulated LDHA expression and facilitated glycolysis. By deeply digging, remodelin, a NAT10 inhibitor, elevated the surface expression of CTLA-4 on T cells. The combination of remodelin and CTLA-4 mAb can further activate T cells and inhibite tumor progression. Conclusion Taken together, our study demonstrated that the NAT10-ac4C-JunB-LDHA pathway increases glycolysis levels and creates an immunosuppressive tumor microenvironment (TME). Consequently, targeting this pathway may assist in the identification of novel therapeutic strategies to improve the efficacy of cancer immunotherapy.

Abstract Background The novel anti-HER2 antibody drug conjugates (ADCs) can effectively improve the long-term survival of patients with HER2-low expression breast cancer. However, pathological responses to neoadjuvant therapy (NAT) within HER2-low expression breast cancer, the relationship between pathological response and prognosis and the transformation of HER2 status are all now poorly understood. Methods The patients with HER2-0 and HER2-low expression breast cancer receiving NAT at Harbin Medical University Cancer Hospital between Jan. 2014 and Nov. 2018 were retrospectively explored. HER2 low expression refers to the IHC 1 + or 2 + and FISH negative. The Kappa test was utilized for analyzing the consistency rate of HER2 expression. To evaluate disease-free survival (DFS) and overall survival (OS), this research employed both the Kaplan-Meier analysis and the Cox regression. Results In this study, 178 patients with HER2-0 and 344 patients with HER2-low expression breast cancer were included. In comparison with the HER2-0 group, it is shown that patients in the HER2-low group have more possibility to be younger compared to those 50 years old (P

Abstract Purpose To evaluate the dosimetric characteristics of ZAP-X stereotactic radiosurgery (SRS) for single brain metastasis by comparing with two mature SRS platforms. Methods Thirteen patients with single brain metastasis treated with CyberKnife (CK) G4 were selected retrospectively. The prescription dose for the planning target volume (PTV) was 18–24 Gy for 1–3 fractions. The PTV volume ranged from 0.44 to 11.52 cc.Treatment plans of thirteen patients were replanned using the ZAP-X plan system and the Gamma Knife (GK) ICON plan system with the same prescription dose and organs at risk (OARs) constraints. The prescription dose of PTV was normalized to 70% for both ZAP-X and CK, while it was 50% for GK. The dosimetric parameters of three groups included the plan characteristics (CI, GI, GSI, beams, MUs, treatment time), PTV (D2, D95, D98, Dmin, Dmean, Coverage), brain tissue (volume of 100%-10% prescription dose irradiation V100%-V10%, Dmean) and other OARs (Dmax, Dmean),all of these were compared and evaluated. All data were read and analyzed with MIM Maestro. One-way ANOVA or a multisample Friedman rank sum test was performed, where p

Abstract The inference of cell–cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes.

PurposeTo propose and validate a comprehensive novel metric balancing the registration accuracy and imaging dose for image-guided-radiotherapy based on real patient data.Materials and methodsWith written informed consent and ethical approval, 56 patients were scanned using 6MV CBCT, 140 kV CBCT, and 100 kV CBCT on Halcyon system for three consecutive treatment fractions. Online registration was performed by various on-duty therapists under routine clinical pressure and time limitation. Offline registration was carried out by an experienced physicist without pressure. The consistency between the online and offline results was used as a surrogate of the missing ground-truth of registration accuracy, which was usually developed by introducing ‘known’ setup errors and rescan the phantoms, yet is ethnically not applicable to real patients. The registration differences (ΔD) between various imaging methods and observers were analyzed. The weighted CT dose index (CTDIw) for kV and MV CBCT was acquired using the PTW CTDI head phantom. The weighted-Dose-Accuracy-Product (DAPw) index was defined as DAPw =ΔD(mm) w1* CTDIw(mGy) w2, where w1 and w2 are the weighting factors of accuracy and dose respectively (w1+w2 = 1).ResultsThe mean and interquartile range (IQR) of ΔD decreased monotonically for MV CBCT, 100 kV CBCT, and 140 kV CBCT, supporting the registration consistency as a surrogate metric of image quality. Significant differences of ΔD were observed between the online and offline registration across three imaging methods (P

Purpose: During the radiation treatment planning process, one of the time-consuming procedures is the final high-resolution dose calculation, which obstacles the wide application of the emerging online adaptive radiotherapy techniques (OLART). There is an urgent desire for highly accurate and efficient dose calculation methods. This study aims to develop a dose super resolution-based deep learning model for fast and accurate dose prediction in clinical practice. Method: A Multi-stage Dose Super-Resolution Network (MDSR Net) architecture with sparse masks module and multi-stage progressive dose distribution restoration method were developed to predict high-resolution dose distribution using low-resolution data. A total of 340 VMAT plans from different disease sites were used, among which 240 randomly selected nasopharyngeal, lung, and cervix cases were used for model training, and the remaining 60 cases from the same sites for model benchmark testing, and additional 40 cases from the unseen site (breast and rectum) was used for model generalizability evaluation. The clinical calculated dose with a grid size of 2 mm was used as baseline dose distribution. The input included the dose distribution with 4 mm grid size and CT images. The model performance was compared with HD U-Net and cubic interpolation methods using Dose-volume histograms (DVH) metrics and global gamma analysis with 1%/1 mm and 10% low dose threshold. The correlation between the prediction error and the dose, dose gradient, and CT values was also evaluated. Results: The prediction errors of MDSR were 0.06–0.84% of Dmean indices, and the gamma passing rate was 83.1–91.0% on the benchmark testing dataset, and 0.02–1.03% and 71.3–90.3% for the generalization dataset respectively. The model performance was significantly higher than the HD U-Net and interpolation methods (p

Introduction Breast cancer is one of the most prevalent types of cancer and a leading cause of cancer-related death among females worldwide. Anoikis, a specific type of apoptosis that is triggered by the loss of anchoring between cells and the native extracellular matrix, plays a vital role in cancer invasion and metastasis. However, studies that focus on the prognostic values of anoikis-related genes (ARGs) in breast cancer are scarce. Methods Gene expression data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Five anoikis-related signatures (ARS) were selected from ARGs through univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. Subsequently, an ARGs risk score model was established, and breast cancer patients were divided into high and low risk groups. The correlation between risk groups and overall survival (OS), tumor mutation burden (TMB), tumor microenvironment (TME), stemness, and drug sensitivity were analyzed. Moreover, RT-qPCR was performed to verify the gene expression levels of the five ARS in breast cancer tissues. Furthermore, a nomogram model was constructed based on ARGs risk score and clinicopathological factors. Results A novel ARGs risk score model was constructed based on five ARS (CEMIP, LAMB3, CD24, PTK6, and PLK1), and breast cancer patients were divided into high and low risk groups. Correlation analysis showed that the high and low risk groups had different OS, TMB, TME, stemness, and drug sensitivity. Both the ARGs risk score model and the nomogram showed promising prognosis predictive value in breast cancer. Conclusion ARS could be used as promising biomarkers for breast cancer prognosis predication and treatment options selection.

An accumulation of previous work has established organoids as good preclinical models of human tumors, facilitating translation from basic research to clinical practice. They are changing the paradigm of preclinical cancer research because they can recapitulate the heterogeneity and pathophysiology of human cancers and more closely approximate the complex tissue environment and structure found in clinical tumors than in vitro cell lines and animal models. However, the potential applications of cancer organoids remain to be comprehensively summarized. In the review, we firstly describe what is currently known about cancer organoid culture and then discuss in depth the basic mechanisms, including tumorigenesis and tumor metastasis, and describe recent advances in patient-derived tumor organoids (PDOs) for drug screening and immunological studies. Finally, the present challenges faced by organoid technology in clinical practice and its prospects are discussed. This review highlights that organoids may offer a novel therapeutic strategy for cancer research.

As the genome is organized into a three-dimensional structure in intracellular space, epigenomic information also has a complex spatial arrangement. However, most epigenetic studies describe locations of methylation marks, chromatin accessibility regions, and histone modifications in the horizontal dimension. Proper spatial epigenomic information has rarely been obtained. In this study, we designed spatial chromatin accessibility sequencing (SCA-seq) to resolve the genome conformation by capturing the epigenetic information in single-molecular resolution while simultaneously resolving the genome conformation. Using SCA-seq, we are able to examine the spatial interaction of chromatin accessibility (e.g. enhancer–promoter contacts), CpG island methylation, and spatial insulating functions of the CCCTC-binding factor. We demonstrate that SCA-seq paves the way to explore the mechanism of epigenetic interactions and extends our knowledge in 3D packaging of DNA in the nucleus.

Abstract Background Early detection is critical for improving the survival of breast cancer (BC) patients. Exhaled breath testing as a non-invasive technique might help to improve BC detection. However, the breath test accuracy for BC diagnosis is unclear. Methods This multi-center cohort study consecutively recruited 5047 women from four areas of China who underwent BC screening. Breath samples were collected through standardized breath collection procedures. Volatile organic compound (VOC) markers were identified from a high-throughput breathomics analysis by the high-pressure photon ionization–time-of-flight mass spectrometry (HPPI-TOFMS). Diagnostic models were constructed using the random forest algorithm in the discovery cohort and tested in three external validation cohorts. Results A total of 465 (9.21%) participants were identified with BC. Ten optimal VOC markers were identified to distinguish the breath samples of BC patients from those of non-cancer women. A diagnostic model (BreathBC) consisting of 10 optimal VOC markers showed an area under the curve (AUC) of 0.87 in external validation cohorts. BreathBC-Plus, which combined 10 VOC markers with risk factors, achieved better performance (AUC = 0.94 in the external validation cohorts), superior to that of mammography and ultrasound. Overall, the BreathBC-Plus detection rates were 96.97% for ductal carcinoma in situ, 85.06%, 90.00%, 88.24%, and 100% for stages I, II, III, and IV BC, respectively, with a specificity of 87.70% in the external validation cohorts. Conclusions This is the largest study on breath tests to date. Considering the easy-to-perform procedure and high accuracy, these findings exemplify the potential applicability of breath tests in BC screening.

Objective: Neoadjuvant therapy (NAT) has been widely implemented as an essential treatment to improve therapeutic efficacy in patients with locally-advanced cancer to reduce tumor burden and prolong survival, particularly for human epidermal growth receptor 2-positive and triple-negative breast cancer. The role of peripheral immune components in predicting therapeutic responses has received limited attention. Herein we determined the relationship between dynamic changes in peripheral immune indices and therapeutic responses during NAT administration. Methods: Peripheral immune index data were collected from 134 patients before and after NAT. Logistic regression and machine learning algorithms were applied to the feature selection and model construction processes, respectively. Results: Peripheral immune status with a greater number of CD3+ T cells before and after NAT, and a greater number of CD8+ T cells, fewer CD4+ T cells, and fewer NK cells after NAT was significantly related to a pathological complete response (P < 0.05). The post-NAT NK cell-to-pre-NAT NK cell ratio was negatively correlated with the response to NAT (HR = 0.13, P = 0.008). Based on the results of logistic regression, 14 reliable features (P < 0.05) were selected to construct the machine learning model. The random forest model exhibited the best power to predict efficacy of NAT among 10 machine learning model approaches (AUC = 0.733). Conclusions: Statistically significant relationships between several specific immune indices and the efficacy of NAT were revealed. A random forest model based on dynamic changes in peripheral immune indices showed robust performance in predicting NAT efficacy.

Abstract Although programmed death-ligand 1 (PD-L1) inhibitors have achieved some therapeutic success in breast cancer, their efficacy is limited by low therapeutic response rates, which is closely related to the immune escape of breast cancer cells. Tissue differentiation inducing non-protein coding RNA (TINCR), a long non-coding RNA, as an oncogenic gene associated with the progression of various malignant tumors, including breast cancer; however, the role of TINCR in tumor immunity, especially in breast cancer, remains unclear. We confirmed that TINCR upregulated PD-L1 expression in vivo and in vitro, and promoted the progression of breast cancer. Next, we revealed that TINCR knockdown can significantly improve the therapeutic effect of PD-L1 inhibitors in breast cancer in vivo. Mechanistically, TINCR recruits DNMT1 to promote the methylation of miR-199a-5p loci and inhibit its transcription. Furthermore, in the cytoplasm, TINCR potentially acts as a molecular sponge of miR-199a-5p and upregulates the stability of USP20 mRNA through a competing endogenous RNA (ceRNA) regulatory mechanism, thus promoting PD-L1 expression by decreasing its ubiquitination level. IFN-γ stimulation activates STAT1 by phosphorylation, which migrates into the nucleus to promote TINCR transcription. This is the first study to describe the regulatory role of TINCR in breast cancer tumor immunity, broadening the current paradigm of the functional diversity of TINCR in tumor biology. In addition, our study provides new research directions and potential therapeutic targets for PD-L1 inhibitors in breast cancer.

Abstract Immunotherapy, especially immune checkpoint inhibitors (ICIs), has revolutionized the treatment of many types of cancer, particularly advanced-stage cancers. Nevertheless, although a subset of patients experiences dramatic and long-term disease regression in response to ICIs, most patients do not benefit from these treatments. Some may even experience cancer progression. Immune escape by tumor cells may be a key reason for this low response rate. N 6-methyladenosine (m6A) is the most common type of RNA methylation and has been recognized as a critical regulator of tumors and the immune system. Therefore, m6A modification and related regulators are promising targets for improving the efficacy of tumor immunotherapy. However, the association between m6A modification and tumor immune escape (TIE) has not been comprehensively summarized. Therefore, this review summarizes the existing knowledge regarding m6A modifications involved in TIE and their potential mechanisms of action. Moreover, we provide an overview of currently available agents targeting m6A regulators that have been tested for their elevated effects on TIE. This review establishes the association between m6A modifications and TIE and provides new insights and strategies for maximizing the efficacy of immunotherapy by specifically targeting m6A modifications involved in TIE.

Abstract Background Robotic linac is ideally suited to deliver hypo-fractionated radiotherapy due to its compact head and flexible positioning. The non-coplanar treatment space improves the delivery versatility but the complexity also leads to prolonged optimization and treatment time. Methods In this study, we attempted to use the deep learning (pytorch) framework for the plan optimization of circular cone based robotic radiotherapy. The optimization problem was topologized into a simple feedforward neural network, thus the treatment plan optimization was transformed into network training. With this transformation, the pytorch toolkit with high-efficiency automatic differentiation (AD) for gradient calculation was used as the optimization solver. To improve the treatment efficiency, plans with fewer nodes and beams were sought. The least absolute shrinkage and selection operator (lasso) and the group lasso were employed to address the “sparsity” issue. Results The AD-S (AD sparse) approach was validated on 6 brain and 6 liver cancer cases and the results were compared with the commercial MultiPlan (MLP) system. It was found that the AD-S plans achieved rapid dose fall-off and satisfactory sparing of organs at risk (OARs). Treatment efficiency was improved by the reduction in the number of nodes (28%) and beams (18%), and monitor unit (MU, 24%), respectively. The computational time was shortened to 47.3 s on average. Conclusions In summary, this first attempt of applying deep learning framework to the robotic radiotherapy plan optimization is promising and has the potential to be used clinically.

Summary: Neoadjuvant therapy (NAT) is currently recommended to patients with human epidermal growth factor receptor 2-positive breast cancer (HER2+ BC) that typically exhibit a poor prognosis. The tumor immune microenvironment profoundly affects the efficacy of NAT. However, the correlation between tumor-infiltrating lymphocytes or their specific subpopulations and the response to NAT in HER2+ BC remains largely unknown. In our study, the immune infiltration status of 295 patients was classified as “immune-rich” or “immune-poor” phenotypes. The “immune-rich” phenotype was significantly positively related to pathological complete response (pCR). Ten genes were correlated with both pCR and the immune phenotype based on the results of spline and logistic regression. We constructed a generalized non-linear model combining linear and non-linear gene effects and successfully validated its predictive power using an internal and external validation set (AUC = 0.819, 0.797; respectively) and a clinical set (accuracy = 0.75).

Abstract Breast cancer is the most prevalent malignant tumor and a leading cause of mortality among females worldwide. The tumorigenesis and progression of breast cancer involve complex pathophysiological processes, which may be mediated by post-translational modifications (PTMs) of proteins, stimulated by various genes and signaling pathways. Studies into PTMs have long been dominated by the investigation of protein phosphorylation and histone epigenetic modifications. However, with great advances in proteomic techniques, several other PTMs, such as acetylation, glycosylation, sumoylation, methylation, ubiquitination, citrullination, and palmitoylation have been confirmed in breast cancer. Nevertheless, the mechanisms, effects, and inhibitors of these unconventional PTMs (particularly, the non-histone modifications other than phosphorylation) received comparatively little attention. Therefore, in this review, we illustrate the functions of these PTMs and highlight their impact on the oncogenesis and progression of breast cancer. Identification of novel potential therapeutic drugs targeting PTMs and development of biological markers for the detection of breast cancer would be significantly valuable for the efficient selection of therapeutic regimens and prediction of disease prognosis in patients with breast cancer.

Purpose: To investigate and compare 2 cone-beam computed tomography (CBCT) correction methods for CBCT-based dose calculation. Materials and Methods: Routine CBCT image sets of 12 head and neck cancer patients who received volumetric modulated arc therapy (VMAT) treatment were retrospectively analyzed. The CBCT images obtained using an on-board imager (OBI) at the first treatment fraction were firstly deformable registered and padded with the kVCT images to provide enough anatomical information about the tissues for dose calculation. Then, 2 CBCT correction methods were developed and applied to correct CBCT Hounsfield unit (HU) values. One method (HD method) is based on protocol-specific CBCT HU to physical density (HD) curve, and the other method (HM method) is based on histogram matching (HM) of HU value. The corrected CBCT images (CBCT HD and CBCT HM for HD and HM methods) were imported into the original planning system for dose calculation based on the HD curve of kVCT (the planning CT). The dose computation result was analyzed and discussed to compare these 2 CBCT-correction methods. Results: Dosimetric parameters, such as the D mean , D max and D 5% of the target volume in CBCT plan doses, were higher than those in the kVCT plan doses; however, the deviations were less than 2%. The D 2% , in parallel organs such as the parotid glands, the deviations from the CBCT HM plan dose were less than those of the CBCT HD plan dose. The differences were statistically significant ( P

Abstract Background In the existing application of beam-matched multiroom proton therapy system, the model based on the commissioning data from the leading treatment room was used as the shared model. The purpose of this study is to investigate the ability of independently-tuned room-specific beam models of beam-matched gantries to reproduce the agreement between gantries’ performance when considering the errors introduced by the modeling process. Methods Raw measurements of two gantries’ dosimetric characteristics were quantitatively compared to ensure their agreement after initially beam-matched. Two gantries’ beam model parameters, as well as the model-based computed dosimetric characteristics, were analyzed to study the introduced errors and gantries’ post-modeling consistency. We forced two gantries to share the same beam model. The model-sharing patient-specific quality assurance (QA) tasks were retrospectively performed with 36 cancer patients to study the clinical impact of beam model discrepancies. Results Intra-gantry comparisons demonstrate that the modeling process introduced the errors to a certain extent indeed, which made the model-based reproduced results deviate from the raw measurements. Among them, the deviation introduced to the IDD curves was generally larger than that to the beam spots during modeling. Cross-gantry comparisons show that, from the beam model perspective, the introduced deviations deteriorated the high agreement of the dosimetric characteristics originally shown between two beam-matched gantries, but the cross-gantry discrepancy was still within the clinically acceptable tolerance. In model-sharing patient-specific QA, for the particular gantry, the beam model usage for intensity-modulated proton therapy (IMPT) QA plan generation had no significant effect on the actual delivering performance. All reached a high level of 95.0% passing rate with a 3 mm/3% criterion. Conclusions It was preliminary recognized that among beam-matched gantries, the independently-tuned room-specific beam model from any gantry is reasonable to be chosen as the shared beam model without affecting the treatment efficacy.

Abstract Background The use of magnetic nanozymes (NZs) with the ability to synchronize gas therapy through photodynamic and chemotherapy in the treatment of breast cancer has received much attention. Results Hence, in this study, we designed a bovine lactoferrin-coated iron sulfide NZs containing doxorubicin (abbreviated as: FeS-Dox@bLf NZs) by wet-chemical synthesis method. Then, the physicochemical characteristics of synthesized NZs were explored by several methods. Also, the level of Fe2+, H2S and Dox releases from FeS-Dox@Lf NZs. Also, the cytotoxic effects of FeS-Dox@Lf NZs were investigated by cellular assays. After intravenous injections of NZs and laser irradiation, significant effects of FeS-Dox@Lf NZs on mice weight and tumor status were observed. Afterwards, not only the distribution of Dox in the body was examined by fluorescent, but also the time of Fe clearance and the amount of Dox and Fe retention in vital tissues were determined. The findings confirm that FeS-Dox@Lf NZs, in addition to targeted drug distribution in tumor tissue, resulted in superior therapeutic performance compared to free Dox due to reduced Dox side effects in vital tissues, and increased level of free radicals in 4T1 cells. Conclusion Overall, FeS-Dox@Lf NZs with the ability to synchronize chemotherapy and gas therapy raised hopes for more effective treatment of breast cancer. Graphic abstract

Abstract Biological therapy is considered an alternative treatment capable of eliciting the same effects on tumors as surgery, radiotherapy, and chemotherapy. As a major player in biological therapy, oncolytic viruses (OVs) have attracted great attention and achieved good results. Specifically, the successful application of OVs in head and neck cancer, as well as melanoma, promoted its research in triple negative breast cancer (TNBC). TNBC is a high-risk molecular type of breast cancer, characterized by strong invasion, easy recurrence, and metastasis. Due to the absence of estrogen and progesterone receptors, as well as the absence of overexpression or gene amplification of human epidermal growth factor receptor 2 (HER2), endocrine therapy and anti HER-2 targeted therapy have proven ineffective. Although chemotherapy has shown substantial efficacy in some TNBC patients, the occurrence of drug resistance and poor prognosis have prompted the exploration of new and effective treatment methods. The emerging concept of OVs provides a new platform to treat TNBC. Indeed, several studies have confirmed the therapeutic effects of OVs in TNBC. Numerous studies have also investigated the efficacy of OVs in other malignances, including solid tumor clinical trials, thus further demonstrating the promising application of oncolytic virotherapy for TNBC. The primary focus of the current review is the examination of OV mechanisms underlying their antitumor properties, while also summarizing the ongoing progress in OV research regarding TNBC treatment, as well as the various combinatorial strategies comprising OVs and other therapies. We also briefly introduce specific relevant clinical trials and discuss some of the progress in the research of novel OVs for the treatment of other malignancies, thereby affirming the significant therapeutic potential of OVs for the treatment of TNBC, as well as other cancers.

Abstract Background Long noncoding RNA (lncRNA) small nucleolar RNA host gene 1 (SNHG1) plays a positive role in the progression of human malignant tumors. However, the molecular mechanism of SNHG1 remains elusive in breast cancer. Results LncRNA SNHG1 was upregulated and had a positive relationship with poor prognosis according to bioinformatics analysis in pan-cancer including breast cancer. Silencing SNHG1 inhibited tumorigenesis in breast cancer both in vitro and in vivo. Mechanistically, SNHG1 functioned as a competing endogenous RNA (ceRNA) to promote TERT expression by sponging miR-18b-5p in breast cancer. miR-18b-5p acted as a tumor repressor in breast cancer. Moreover, the combination of SNHG1 knockdown and TERT inhibitor administration showed a synergistic inhibitory effect on breast cancer growth in vivo. Finally, E2F1 as a transcription factor, binding to SNHG1 promoter and enhanced SNHG1 transcription in breast cancer. Conclusions Our results provide a comprehensive understanding of the oncogenic mechanism of lncRNA SNHG1 in breast cancer. Importantly, we identified a novel E2F1–SNHG1–miR-18b-5p–TERT axis, which may be a potential therapeutic target for breast cancer. Our results also provided a potential treatment for breast cancer when knockdown SNHG1 and TERT inhibitor administration simultaneously.

Abstract Mammography is used to detect breast cancer (BC), but its sensitivity is limited, especially for dense breasts. Circulating cell-free DNA (cfDNA) methylation tests is expected to compensate for the deficiency of mammography. We derived a specific panel of markers based on computational analysis of the DNA methylation profiles from The Cancer Genome Atlas (TCGA). Through training (n = 160) and validation set (n = 69), we developed a diagnostic prediction model with 26 markers, which yielded a sensitivity of 89.37% and a specificity of 100% for differentiating malignant disease from normal lesions [AUROC = 0.9816 (95% CI: 96.09-100%), and AUPRC = 0.9704 (95% CI: 94.54–99.46%)]. A simplified 4-marker model including cg23035715, cg16304215, cg20072171, and cg21501525 had a similar diagnostic power [AUROC = 0.9796 (95% CI: 95.56–100%), and AUPRC = 0.9220 (95% CI: 91.02–94.37%)]. We found that a single cfDNA methylation marker, cg23035715, has a high diagnostic power [AUROC = 0.9395 (95% CI: 89.72–99.27%), and AUPRC = 0.9111 (95% CI: 88.45–93.76%)], with a sensitivity of 84.90% and a specificity of 93.88%. In an independent testing dataset (n = 104), the obtained diagnostic prediction model discriminated BC patients from normal controls with high accuracy [AUROC = 0.9449 (95% CI: 90.07–98.91%), and AUPRC = 0.8640 (95% CI: 82.82–89.98%)]. We compared the diagnostic power of cfDNA methylation and mammography. Our model yielded a sensitivity of 94.79% (95% CI: 78.72–97.87%) and a specificity of 98.70% (95% CI: 86.36–100%) for differentiating malignant disease from normal lesions [AUROC = 0.9815 (95% CI: 96.75–99.55%), and AUPRC = 0.9800 (95% CI: 96.6–99.4%)], with better diagnostic power and had better diagnostic power than that of using mammography [AUROC = 0.9315 (95% CI: 89.95–96.34%), and AUPRC = 0.9490 (95% CI: 91.7–98.1%)]. In addition, hypermethylation profiling provided insights into lymph node metastasis stratifications (p

According to the latest global cancer statistics, cancer has become a major threat to human health, but cancer treatment has encountered many bottlenecks. As an emerging topic in epigenetics, N6-methyladenosine (m6A) is the most common internal modification on eukaryotic mRNA, which has attracted increasing attention in recent years. Accumulating studies have shown that aberrant m6A modifications have profound effects on the characteristics of tumors, which undoubtedly led to a significant breakthrough in cancer treatment. Although m6A function as an oncogene or tumor suppressor is not fully revealed, determining its precise function in the development and evolution of malignant tumors is crucial in improving clinical decisions involving targeted therapies. In this review, we briefly introduce the composition of the m6A methylation machinery and mainly summarize the biological mechanism of m6A in cancer cell death, angiogenesis, epithelial-mesenchymal transition (EMT), and therapeutic resistance. Subsequently, we present the exogenous regulatory factors of m6A and highlight the role of m6A on immune cells and cancer immunotherapy. The potential therapeutic strategies of m6A in human cancer are also discussed, considering research gaps and future applications.

Human ribonuclease 1 (hRNase 1) regulates innate immunity, hemostasis and RNA clearance. Here, the authors report an alternative function of hRNase 1 as a secretory ligand of Eph receptor EphA4 to enhance breast cancer stemness and promote breast tumour initiation.

BackgroundT1–2 breast cancer patients with only one sentinel lymph node (SLN) metastasis have an extremely low non-SLN (NSLN) metastatic rate and are favorable for axillary lymph node dissection (ALND) exemption. This study aimed to construct a nomogram-based preoperative prediction model of NSLN metastasis for such patients, thereby assisting in preoperatively selecting proper surgical procedures.MethodsA total of 729 T1–2 breast cancer patients with only one SLN metastasis undergoing sentinel lymph node biopsy and ALND were retrospectively selected from Harbin Medical University Cancer Hospital between January 2013 and December 2020, followed by random assignment into training (n=467) and validation cohorts (n=262). A nomogram-based prediction model for NSLN metastasis risk was constructed by incorporating the independent predictors of NSLN metastasis identified from multivariate logistic regression analysis in the training cohort. The performance of the nomogram was evaluated by the calibration curve and the receiver operating characteristic (ROC) curve. Finally, decision curve analysis (DCA) was used to determine the clinical utility of the nomogram.ResultsOverall, 160 (21.9%) patients had NSLN metastases. Multivariate analysis in the training cohort revealed that the number of negative SLNs (OR: 0.98), location of primary tumor (OR: 2.34), tumor size (OR: 3.15), and lymph-vascular invasion (OR: 1.61) were independent predictors of NSLN metastasis. The incorporation of four independent predictors into a nomogram-based preoperative estimation of NSLN metastasis demonstrated a satisfactory discriminative capacity, with a C-index and area under the ROC curve of 0.740 and 0.689 in the training and validation cohorts, respectively. The calibration curve showed good agreement between actual and predicted NSLN metastasis risks. Finally, DCA revealed the clinical utility of the nomogram.ConclusionThe nomogram showed a satisfactory discriminative capacity of NSLN metastasis risk in T1–2 breast cancer patients with only one SLN metastasis, and it could be used to preoperatively estimate NSLN metastasis risk, thereby facilitating in precise clinical decision-making on the selective exemption of ALND in such patients.

Abstract Early detection is crucial to improve breast cancer (BC) patients’ outcomes and survival. Mammogram and ultrasound adopting the Breast Imaging Reporting and Data System (BI-RADS) categorization are widely used for BC early detection, while suffering high false-positive rate leading to unnecessary biopsy, especially in BI-RADS category-4 patients. Plasma cell-free DNA (cfDNA) carrying on DNA methylation information has emerged as a non-invasive approach for cancer detection. Here we present a prospective multi-center study with whole-genome bisulfite sequencing data to address the clinical utility of cfDNA methylation markers from 203 female patients with breast lesions suspected for malignancy. The cfDNA is enriched with hypo-methylated genomic regions. A practical computational framework was devised to excavate optimal cfDNA-rich DNA methylation markers, which significantly improved the early diagnosis of BI-RADS category-4 patients (AUC from 0.78–0.79 to 0.93–0.94). As a proof-of-concept study, we performed the first blood-based whole-genome DNA methylation study for detecting early-stage breast cancer from benign tumors at single-base resolution, which suggests that combining the liquid biopsy with the traditional diagnostic imaging can improve the current clinical practice, by reducing the false-positive rate and avoiding unnecessary harms.

Abstract The long noncoding RNA (lncRNA) TINCR has recently been found to be associated with the progression of human malignancies, but the molecular mechanism of TINCR action remains elusive, particularly in breast cancer. The oncogenic role of TINCR was examined in vitro and in vivo in breast cancer. Next, the interaction between TINCR, DNMT1, and miR-503-5p methylation was explored. Moreover, the mechanism by which TINCR enhances EGFR expression and downstream signaling via an RNA–RNA interaction was comprehensively investigated. Furthermore, upstream transcriptional regulation of TINCR expression by STAT3 was examined by performing chromatin immunoprecipitation. Finally, feedback signaling in the STAT3–TINCR–EGFR downstream cascade was also investigated. TINCR is upregulated in human breast cancer tissues, and TINCR knockdown suppresses tumorigenesis in vitro and in vivo. Mechanistically, TINCR recruits DNMT1 to the miR-503-5p locus promoter, which increases the methylation and suppresses the transcriptional expression of miR-503-5p. Furthermore, TINCR also functions as a competing endogenous RNA to upregulate EGFR expression by sponging miR-503-5p. In addition, TINCR stimulates JAK2–STAT3 signaling downstream from EGFR, and STAT3 reciprocally enhances the transcriptional expression of TINCR. Our findings broaden the current understanding of the diverse manners in which TINCR functions in cancer biology. The newly identified STAT3–TINCR–EGFR-feedback loop could serve as a potential therapeutic target for human cancer.

An amendment to this paper has been published and can be accessed via the original article.

Background and ObjectiveSentinel lymph node biopsy (SLNB) is used to assess the status of axillary lymph node (ALN), but it causes many adverse reactions. Considering the low rate of sentinel lymph node (SLN) metastasis in T1 breast cancer, this study aims to identify the characteristics of T1 breast cancer without SLN metastasis and to select T1 breast cancer patients who avoid SLNB through constructing a nomogram.MethodsA total of 1,619 T1 breast cancer patients with SLNB in our hospital were enrolled in this study. Through univariate and multivariate logistic regression analysis, we analyzed the tumor anatomical and clinicopathological factors and constructed the Heilongjiang Medical University (HMU) nomogram. We selected the patients exempt from SLNB by using the nomogram.ResultsIn the training cohort of 1,000 cases, the SLN metastasis rate was 23.8%. Tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes were found to be independent predictors for SLN metastasis in multivariate regression analysis. Distance from nipple or surface and position of tumor have no effect on SLN metastasis. A regression model based on the results of the multivariate analysis was developed to predict the risk of SLN metastasis, indicating an AUC of 0.798. It showed excellent diagnostic performance (AUC = 0.773) in the validation cohort.ConclusionThe HMU nomogram for predicting SLN metastasis incorporates four variables, including tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes. The SLN metastasis rates of intraductal carcinoma and HER2 enriched are 2.05% and 6.67%. These patients could be included in trials investigating the SLNB exemption.

PurposeThe purpose of this study was to evaluate and explore the difference between an atlas-based and deep learning (DL)-based auto-segmentation scheme for organs at risk (OARs) of nasopharyngeal carcinoma cases to provide valuable help for clinical practice.Methods120 nasopharyngeal carcinoma cases were established in the MIM Maestro (atlas) database and trained by a DL-based model (AccuContour®), and another 20 nasopharyngeal carcinoma cases were randomly selected outside the atlas database. The experienced physicians contoured 14 OARs from 20 patients based on the published consensus guidelines, and these were defined as the reference volumes (Vref). Meanwhile, these OARs were auto-contoured using an atlas-based model, a pre-built DL-based model, and an on-site trained DL-based model. These volumes were named Vatlas, VDL-pre-built, and VDL-trained, respectively. The similarities between Vatlas, VDL-pre-built, VDL-trained, and Vref were assessed using the Dice similarity coefficient (DSC), Jaccard coefficient (JAC), maximum Hausdorff distance (HDmax), and deviation of centroid (DC) methods. A one-way ANOVA test was carried out to show the differences (between each two of them).ResultsThe results of the three methods were almost similar for the brainstem and eyes. For inner ears and temporomandibular joints, the results of the pre-built DL-based model are the worst, as well as the results of atlas-based auto-segmentation for the lens. For the segmentation of optic nerves, the trained DL-based model shows the best performance (p < 0.05). For the contouring of the oral cavity, the DSC value of VDL-pre-built is the smallest, and VDL-trained is the most significant (p < 0.05). For the parotid glands, the DSC of Vatlas is the minimum (about 0.80 or so), and VDL-pre-built and VDL-trained are slightly larger (about 0.82 or so). In addition to the oral cavity, parotid glands, and the brainstem, the maximum Hausdorff distances of the other organs are below 0.5 cm using the trained DL-based segmentation model. The trained DL-based segmentation method behaves well in the contouring of all the organs that the maximum average deviation of the centroid is no more than 0.3 cm.ConclusionThe trained DL-based segmentation performs significantly better than atlas-based segmentation for nasopharyngeal carcinoma, especially for the OARs with small volumes. Although some delineation results still need further modification, auto-segmentation methods improve the work efficiency and provide a level of help for clinical work.

PurposeConsistent training and testing datasets can lead to good performance for deep learning (DL) models. However, a large high-quality training dataset for unusual clinical scenarios is usually not easy to collect. The work aims to find optimal training data collection strategies for DL-based dose prediction models.Materials and MethodsA total of 325 clinically approved cervical IMRT plans were utilized. We designed comparison experiments to investigate the impact of (1) beam angles, (2) the number of beams, and (3) patient position for DL dose prediction models. In addition, a novel geometry-based beam mask generation method was proposed to provide beam setting information in the model training process. What is more, we proposed a new training strategy named “full-database pre-trained strategy”.ResultsThe model trained with a homogeneous dataset with the same beam settings achieved the best performance [mean prediction errors of planning target volume (PTV), bladder, and rectum: 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%] compared with that trained with large mixed beam setting plans (mean errors of PTV, bladder, and rectum: 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4%). A homogeneous dataset is more accessible to train an accurate dose prediction model (mean errors of PTV, bladder and rectum: 2.2 ± 0.15%, 5 ± 2.1%, and 3.23 ± 1.53%) than a non-homogeneous one (mean errors of PTV, bladder and rectum: 2.55 ± 0.12%, 6.33 ± 2.46%, and 4.76 ± 2.91%) without other processing approaches. The added beam mask can constantly improve the model performance, especially for datasets with different beam settings (mean errors of PTV, bladder, and rectum improved from 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4% to 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%).ConclusionsA consistent dataset is recommended to form a patient-specific IMRT dose prediction model. When a consistent dataset is not accessible to collect, a large dataset with different beam angles and a training model with beam information can also get a relatively good model. The full-database pre-trained strategies can rapidly form an accuracy model from a pre-trained model. The proposed beam mask can effectively improve the model performance. Our study may be helpful for further dose prediction studies in terms of training strategies or database establishment.

Breast cancer (BRCA) is one of the leading causes of death among women worldwide, and drug resistance often leads to a poor prognosis. Necroptosis is a type of programmed cell death (PCD) and exhibits regulatory effects on tumor progression, but few studies have focused on the relationships between necroptosis-associated lncRNAs and BRCA. In this study, we established a signature basis of 7 necroptosis-related lncRNAs associated with prognosis and divided BRCA patients into high- and low-risk groups. Kaplan-Meier curves all showed an adverse prognosis for patients in the high-risk group. Cox assays confirmed that risk score was an independent prognostic factor for BRCA patients. The receiver operating characteristic (ROC) curve proved the predictive accuracy of the signature and the area under the curve (AUC) values of the risk score reached 0.722. The nomogram relatively accurately predicted the prognosis of the patients. GSEA analysis suggested that the related signaling pathways and biological processes enriched in the high- and low-risk groups may influence the tumor microenvironment (TME) of BRCA. ssGSEA showed the difference in immune cell infiltration, immune pathway activation, and immune checkpoint expression between the two risk groups, with the low-risk group more suitable for immunotherapy. According to the significant difference in IC50 between risk groups, patients can be guided for an individualized treatment plan. Overall, the authors established a prognostic signature consisting of 7 necroptosis-associated lncRNAs that can independently predict the clinical outcome of BRCA patients. The difference in the tumor immune microenvironment between the low- and high-risk populations may be the reason for the resistance to immunotherapy in some patients.

The dysregulation of transfer RNA (tRNA) expression contributes to the diversity of proteomics, heterogeneity of cell populations, and instability of the genome, which may be related to human cancer susceptibility. However, the relationship between tRNA dysregulation and cancer susceptibility remains elusive because the landscape of cancer-associated tRNAs has not been portrayed yet. Furthermore, the molecular mechanisms of tRNAs involved in tumorigenesis and cancer progression have not been systematically understood. In this review, we detail current knowledge of cancer-related tRNAs and comprehensively summarize the basic characteristics and functions of these tRNAs, with a special focus on their role and involvement in human cancer. This review bridges the gap between tRNAs and cancer and broadens our understanding of their relationship, thus providing new insights and strategies to improve the potential clinical applications of tRNAs for cancer diagnosis and therapy.

PurposeThis study focused on predicting 3D dose distribution at high precision and generated the prediction methods for nasopharyngeal carcinoma patients (NPC) treated with Tomotherapy based on the patient-specific gap between organs at risk (OARs) and planning target volumes (PTVs).MethodsA convolutional neural network (CNN) is trained using the CT and contour masks as the input and dose distributions as output. The CNN is based on the “3D Dense-U-Net”, which combines the U-Net and the Dense-Net. To evaluate the model, we retrospectively used 124 NPC patients treated with Tomotherapy, in which 96 and 28 patients were randomly split and used for model training and test, respectively. We performed comparison studies using different training matrix shapes and dimensions for the CNN models, i.e., 128 ×128 ×48 (for Model I), 128 ×128 ×16 (for Model II), and 2D Dense U-Net (for Model III). The performance of these models was quantitatively evaluated using clinically relevant metrics and statistical analysis.ResultsWe found a more considerable height of the training patch size yields a better model outcome. The study calculated the corresponding errors by comparing the predicted dose with the ground truth. The mean deviations from the mean and maximum doses of PTVs and OARs were 2.42 and 2.93%. Error for the maximum dose of right optic nerves in Model I was 4.87 ± 6.88%, compared with 7.9 ± 6.8% in Model II (p=0.08) and 13.85 ± 10.97% in Model III (p

State of charge (SOC) estimations are an important part of lithium-ion battery management systems. Aiming at existing SOC estimation algorithms based on neural networks, the voltage increment is proposed in this paper as a new input feature for estimation of the SOC of lithium-ion batteries. In this method, the port voltage, current and voltage increment are taken as inputs and the current SOC is used as output to train a neural network. Different from the adaptive filtering algorithm, which requires complex equivalent circuit parameter identification, this algorithm uses the voltage increment instead of the open circuit voltage (OCV); hence, the complexity of the SOC estimation algorithm is reduced, and the problem of inaccurate estimation caused by neural network algorithms without considering the internal structure of the battery is avoided. The experimental results show that compared with the traditional neural network algorithm, the neural network SOC estimation algorithm based on the voltage increment could improve the accuracy of SOC estimation.

Abstract Background An increasing number of studies have shown that long noncoding RNAs (lncRNAs) play essential roles in tumor initiation and progression. LncRNAs act as tumor promoters or suppressors by targeting specific genes via epigenetic modifications and competing endogenous RNA (ceRNA) mechanisms. In this study, we explored the function and detailed mechanisms of long intergenic nonprotein coding RNA 673 (LINC00673) in breast cancer progression. Methods Quantitative real-time PCR (qRT-PCR) was used to examine the expression of LINC00673 in breast cancer tissues and in adjacent normal tissues. Gain-of-function and loss-of function experiments were conducted to investigate the biological functions of LINC00673 in vitro and in vivo. We also explored the potential role of LINC00673 as a therapeutic target using antisense oligonucleotide (ASO) in vivo. RNA sequencing (RNA-seq), dual-luciferase reporter assays, chromatin immunoprecipitation (ChIP) assay, and rescue experiments were performed to uncover the detailed mechanism of LINC00673 in promoting breast cancer progression. Results In the present study, LINC00673 displayed a trend of remarkably increased expression in breast cancer tissues and was associated with poor prognosis in breast cancer patients. Importantly, LINC00673 depletion inhibited breast cancer cell proliferation by inhibiting the cell cycle and increasing apoptosis. Furthermore, ASO therapy targeting LINC00673 substantially suppressed breast cancer cell proliferation in vivo. Mechanistically, LINC00673 was found to act as a ceRNA by sponging miR-515-5p to regulate MARK4 expression, thus inhibiting the Hippo signaling pathway. Finally, ChIP assay showed that the transcription factor Yin Yang 1 (YY1) could bind to the LINC00673 promoter and increase its transcription in cis. Conclusions YY1-activated LINC00673 may exert an oncogenic function by acting as a sponge for miR-515-5p to upregulate the MARK4 and then inhibit Hippo signaling pathway, and may serve as a potential therapeutic target.

Abstract Background The biology function of antisense intronic long noncoding RNA (Ai-lncRNA) is still unknown. Meanwhile, cancer patients with paclitaxel resistance have limited therapeutic options in the clinic. However, the potential involvement of Ai-lncRNA in paclitaxel sensitivity remains unclear in human cancer. Methods Whole transcriptome sequencing of 33 breast specimens was performed to identify Ai-lncRNA EGOT. Next, the role of EGOT in regulation of paclitaxel sensitivity was investigated. Moreover, the mechanism of EGOT enhancing autophagy sensitizes paclitaxel cytotoxicity via upregulation of ITPR1 expression by RNA-RNA and RNA-protein interactions was investigated in detail. Furthermore, upstream transcriptional regulation of EGOT expression was also investigated by co-immunoprecipitation and chromatin immunoprecipitation. Finally, clinical breast specimens in our cohort, TCGA and ICGC were applied to validate the role of EGOT in enhancing of paclitaxel sensitivity. Results EGOT enhances autophagosome accumulation via the up-regulation of ITPR1 expression, thereby sensitizing cells to paclitaxel toxicity. Mechanistically, on one hand, EGOT upregulates ITPR1 levels via formation of a pre-ITPR1/EGOT dsRNA that induces pre-ITPR1 accumulation to increase ITPR1 protein expression in cis. On the other hand, EGOT recruits hnRNPH1 to enhance the alternative splicing of pre-ITPR1 in trans via two binding motifs in EGOT segment 2 (324–645 nucleotides) in exon 1. Moreover, EGOT is transcriptionally regulated by stress conditions. Finally, EGOT expression enhances paclitaxel sensitivity via assessment of cancer specimens. Conclusions These findings broaden comprehensive understanding of the biology function of Ai-lncRNAs. Proper regulation of EGOT may be a novel synergistic strategy for enhancing paclitaxel sensitivity in cancer therapy.

Histone modifications (HMs) contribute to maintaining genomic stability, transcription, DNA repair, and modulating chromatin in cancer cells. Furthermore, HMs are dynamic and reversible processes that involve interactions between numerous enzymes and molecular components. Aberrant HMs are strongly associated with tumorigenesis and progression of breast cancer (BC), although the specific mechanisms are not completely understood. Moreover, there is no comprehensive overview of abnormal HMs in BC, and BC therapies that target HMs are still in their infancy. Therefore, this review summarizes the existing evidence regarding HMs that are involved in BC and the potential mechanisms that are related to aberrant HMs. Moreover, this review examines the currently available agents and approved drugs that have been tested in pre-clinical and clinical studies to evaluate their effects on HMs. Finally, this review covers the barriers to the clinical application of therapies that target HMs, and possible strategies that could help overcome these barriers and accelerate the use of these therapies to cure patients.

Background: To evaluate the toxicities and long-term outcomes of induction chemotherapy (ICT) plus simultaneous modulated accelerated radiation therapy (SMART) in non-operative hypopharyngeal and supraglottic laryngeal squamous cell carcinoma (SCCH/L).Materials and Methods: This was a prospective phase 2 study. Patients diagnosed with SCCH/L, aged from 18 to 75, staged from III to IVB in accordance with the AJCC 2010 criteria, and refusing surgery were eligible. The patients were treated with 2–3 cycles of docetaxel-cisplatin-based ICT and SMART combined with 2–3 cycles of cisplatin-based concurrent chemotherapy. The prescription dose to the primary tumor and metastatic nodes was 69 Gy in 30 fractions. Acute and late toxicities were assessed according to the established Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer (RTOG/EORTC) criteria, and long-term outcomes were analyzed.Results: Between February 2013 and June 2015, 55 newly diagnosed SCCH/L patients were enrolled. No grade 2 or worse acute xerostomia was noted. The incidences of grade 3 acute dermatitis, oral mucositis, and pharyngoesophagitis were 12.7, 3.6, and 12.7%, respectively. The median follow-up time was 48 months (range 5.5–74 months). The main late toxicity was hoarseness or sore throat, with an incidence of 32.7%. The 5-year functional larynx-preservation survival was 51.5%. The 3- and 5-year locoregional control and overall survival were 58.2, 51.5, 63.6, and 54.1%, respectively.Conclusions: The ICT plus SMART with a regimen of 69 Gy/30 F for the treatment of SCCH/L demonstrated acceptable severe toxicity, satisfactory long-term outcomes, and laryngeal function preservation.

ObjectiveThis study aimed to develop a least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) model to predict radiation-induced xerostomia in patients with nasopharyngeal carcinoma (NPC) treated with comprehensive salivary gland–sparing helical tomotherapy technique.Methods and MaterialsLASSO with the extended bootstrapping technique was used to build multivariable NTCP models to predict factors of patient-reported xerostomia relieved by 50% and 80% compared with the level at the end of radiation therapy within 1 year and 2 years, R50-1year and R80-2years, in 203 patients with NPC. The model assessment was based on 10-fold cross-validation and the area under the receiver operating characteristic curve (AUC).ResultsThe prediction model by LASSO with 10-fold cross-validation showed that radiation-induced xerostomia recovery could be predicted by prognostic factors of R50-1year (age, gender, T stage, UICC/AJCC stage, parotid Dmean, oral cavity Dmean, and treatment options) and R80-2years (age, gender, T stage, UICC/AJCC stage, oral cavity Dmean, N stage, and treatment options). These prediction models also demonstrated a good performance by the AUC.ConclusionThe prediction models of R50-1year and R80-2years by LASSO with 10-fold cross-validation were recommended to validate the NTCP model before comprehensive salivary gland–sparing radiation therapy in patients with NPC.

ObjectivesComplement 1q binding protein (C1QBP/HABP1/p32/gC1qR) has been found to be overexpressed in triple-negative breast cancer (TNBC). However, the underlying mechanisms of high C1QBP expression and its role in TNBC remain largely unclear. Hypoxia is a tumor-associated microenvironment that promotes metastasis and paclitaxel (PTX) chemoresistance in tumor cells. In this study, we aimed to assess C1QBP expression and explore its role in hypoxia-related metastasis and chemoresistance in TNBC.Materials and MethodsRNA-sequencing of TNBC cells under hypoxia was performed to identify C1QBP. The effect of hypoxia inducible factor 1 subunit alpha (HIF-1α) on C1QBP expression was investigated using chromatin immunoprecipitation (ChIP) assay. The role of C1QBP in mediating metastasis, chemoresistance to PTX, and regulation of metastasis-linked vascular cell adhesion molecule 1 (VCAM-1) expression were studied using in vitro and in vivo experiments. Clinical tissue microarrays were used to verify the correlation of C1QBP with the expression of HIF-1α, VCAM-1, and RELA proto-oncogene nuclear factor-kappa B subunit (P65).ResultsWe found that hypoxia-induced HIF-1α upregulated C1QBP. The inhibition of C1QBP notably blocked metastasis of TNBC cells and increased their sensitivity to PTX under hypoxic conditions. Depletion of C1QBP decreased VCAM-1 expression by reducing the amount of P65 in the nucleus and suppressed the activation of hypoxia-induced protein kinase C-nuclear factor-kappa B (PKC-NF-κB) signaling.immunohistochemistry (IHC) staining of the tissue microarray showed positive correlations between the C1QBP level and those of HIF-1α, P65, and VCAM-1.ConclusionTargeting C1QBP along with PTX treatment might be a potential treatment for TNBC patients.

Abstract Background The landscape and biological functions of tumor suppressor long noncoding RNAs in breast cancer are still unknown. Methods Data from whole transcriptome sequencing of 33 breast specimens in the Harbin Medical University Cancer Center cohort and The Cancer Genome Atlas was applied to identify and validate the landscape of tumor suppressor long noncoding RNAs, which was further validated by The Cancer Genome Atlas pancancer data including 33 cancer types and 12,839 patients. Next, the expression model, prognostic roles, potential biological functions and epigenetic regulation of tumor suppressor long noncoding RNAs were investigated and validated in the breast cancer and pancancer cohorts. Finally, EPB41L4A-AS2 was selected to validate our novel finding, and the tumor suppressive roles of EPB41L4A-AS2 in breast cancer were examined. Results We identified and validated the landscape of tumor suppressor long noncoding RNAs in breast cancer. The expression of the identified long noncoding RNAs was downregulated in cancer tissue samples compared with normal tissue samples, and these long noncoding RNAs correlated with a favorable prognosis in breast cancer patients and the patients in the pancancer cohort. Multiple carcinogenesis-associated biological functions were predicted to be regulated negatively by these long noncoding RNAs. Moreover, these long noncoding RNAs were transcriptionally regulated by epigenetic modification, including DNA methylation and histone methylation modification. Finally, EPB41L4A-AS2 inhibited breast cancer cell proliferation, migration and invasion and induced cell apoptosis in vitro. Mechanistically, EPB41L4A-AS2, acting at least in part as a tumor suppressor, upregulated tumor suppressor gene expression. Moreover, ZNF217 recruited EZH2 to the EPB41L4A-AS2 locus and suppressed the expression of EPB41L4A-AS2 by epigenetically increasing H3K27me3 enrichment. Conclusions This work enlarges the functional landscape of known long noncoding RNAs in human cancer and provides novel insights into the suppressive roles of these long noncoding RNAs.

Background/Aims: Tropomyosin-2 (TPM2) plays important roles in functions of the cytoskeleton, such as cytokinesis, vesicle transport, cell proliferation, migration and apoptosis,and these functions imply that TPM2 also plays a role in cancer development. Indeed, it has been shown that TPM2 plays a critical role in some cancers. However, the role of TPM2 in breast cancer is still poorly characterized. Thus, we explored the role of TPM2 in breast cancer. Methods: We analysed TPM2 expression and its correlation with the clinicopathological features in breast cancer. Then, we examined the influence of hypoxia on TPM2 expression and methylation status using bisulfite sequencing PCR. Furthermore, we performed TPM2-mediated migration and invasion assays in the context of hypoxia and examined changes in matrix metalloproteinase-2 (MMP2) expression. Finally, we detected the influence of TPM2 on survival and chemotherapy drug sensitivity. Results: We found that TPM2 expression is down-regulated in breast cancer cells compared to that in normal breast cells. The data from TCGA supported these results. Promoter methylation of TPM2, which could be induced by hypoxia, was responsible for its low expression. Hypoxia might regulate cell invasiveness partly by TPM2 down-regulation-mediated changes of MMP2 expression. Importantly, low TPM2 expression was correlated with lymph node metastasis (P=0.031), tumour node metastasis stage (P=0.01), histological grade (P=0.037), and shorter overall survival (P=0.028). Univariate and multivariate analyses indicated that TPM2 was an independent predictor in breast cancer patients. Paclitaxel chemotherapy did not benefit patients with low TPM2 expression (P

Abstract Background A growing amount of evidence has indicated that PSAT1 is an oncogene that plays an important role in cancer progression and metastasis. In this study, we explored the expression and function of PSAT1 in estrogen receptor (ER)-negative breast cancer. Method The expression level of PSAT1 in breast cancer tissues and cells was analyzed using real-time-PCR (RT-PCR), TCGA datasets or immunohistochemistry (IHC). The overall survival of patients with ER-negative breast cancer stratified by the PSAT1 expression levels was evaluated using Kaplan-Meier analysis. The function of PSAT1 was analyzed using a series of in vitro assays. Moreover, a nude mouse model was used to evaluate the function of PSAT1 in vivo. qRT-PCR and western blot assays were used to evaluate gene and protein expression, respectively, in the indicated cells. In addition, we demonstrated that PSAT1 was activated by ATF4 by chromatin immunoprecipitation (ChIP) assays. Results mRNA expression of PSAT1 was up-regulated in ER-negative breast cancer. A tissue microarray that included 297 specimens of ER-negative breast cancer was subjected to an immunohistochemistry assay, which demonstrated that PSAT1 was overexpressed and predicted a poor clinical outcome of patients with this disease. Our data showed that PSAT1 promoted cell proliferation and tumorigenesis in vitro and in vivo. We further found that PSAT1 induced up-regulation of cyclin D1 via the GSK3β/β-catenin pathway, which eventually led to the acceleration of cell cycle progression. Furthermore, ATF4 was also overexpressed in ER-negative breast cancers, and a positive correlation between the ATF4 and PSAT1 mRNA levels was observed in ER-negative breast cancers. We further demonstrated that knockdown of ATF4 by siRNA reduced PSAT1 expression. Finally, chromatin immunoprecipitation (ChIP) assays showed that PSAT1 was a target of ATF4. Conclusions PSAT1, which is overexpressed in ER-negative breast cancers, is activated by ATF4 and promotes cell cycle progression via regulation of the GSK3β/β-catenin/cyclin D1 pathway.

Background/Aims: The oncogenic role of linc00152 in pan-cancer is unclear. Methods: In this study, RNA-Seq of 33 breast specimens was performed, and the expression of linc00152 was validated by qPCR using 50 paired breast cancer tissues and adjacent normal tissues. This result combined with the expression of linc00152 in pan-cancer was revalidated by Gene Expression Omnibus and The Cancer Genome Atlas data. Next, the oncogenic roles of linc00152 in view of prognosis, chemoresistance, genomic and epigenetic regulation, including DNA methylation and histone modification, potential biological function enrichment, and basic molecular function in pan-cancer, were also evaluated in vitro and in vivo. Results: Linc00152 is upregulated in pan-cancer, especially in progressive cancer, and the high expression of linc00152 may lead to a worse prognosis and chemoresistance in pan-cancer patients. Amplification, DNA hypomethylation, promoter-like lncRNA characteristics and super-enhancer regulation are the drivers that lead to the upregulation of linc00152 in pan-cancer. Meanwhile, linc00152 was involved in cancer-related pathways, infection and immune response-associated pathways by enriched analysis using TCGA data. Finally, linc00152 was confirmed to promote the proliferation, migration and invasion in MDA-MB-231, SGC-7901 and 786-O. Moreover, RIP and RNA pull-down assays indicated that linc00152 can bind to EZH2 directly. Conclusion: All of the results indicated that linc00152 acted as an oncogenic propellant from various perspectives, and it may be an effective therapy target in pan-cancer.

Abstract Background Few long noncoding RNAs (lncRNAs) that act as oncogenic genes in breast cancer have been identified. Methods Oncogenic lncRNAs associated with tumourigenesis and worse survival outcomes were examined and validated in Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), respectively. Then, the potential biological functions and expression regulation of these lncRNAs were studied via bioinformatics and genome data analysis. Moreover, progressive breast cancer subtype-specific lncRNAs were investigated via high-throughput sequencing in our cohort and TCGA validation. To elucidate the mechanisms of the regulation of these lncRNAs, genomic alterations from the TCGA, Broad, Sanger and BCCRC data, as well as epigenetic modifications from GEO data, were then applied and examined to meet this objective. Finally, cell proliferation assays, flow cytometry analyses and TUNEL assays were applied to validate the oncogenic roles of these lncRNAs in vitro. Results A cluster of oncogenic lncRNAs that was upregulated in breast cancer tissue and was associated with worse survival outcomes was identified. These oncogenic lncRNAs are involved in regulating immune system activation and the TGF-beta and Jak-STAT signalling pathways. Moreover, TINCR, LINC00511, and PPP1R26-AS1 were identified as subtype-specific lncRNAs associated with HER-2, triple-negative and luminal B subtypes of breast cancer, respectively. The up-regulation of these oncogenic lncRNAs is mainly caused by gene amplification in the genome in breast cancer and other solid tumours. Finally, the knockdown of TINCR, DSCAM-AS1 or HOTAIR inhibited breast cancer cell proliferation, increased apoptosis and inhibited cell cycle progression in vitro. Conclusions These findings enhance the landscape of known oncogenic lncRNAs in breast cancer and provide insights into their roles. This understanding may potentially aid in the comprehensive management of breast cancer.

Porous cellulose aerogel materials are attracting increasing interest due to their promising potential in multiple fields. In this paper, highly porous and mechanically strong cellulose aerogels were successfully prepared from microcrystalline cellulose and dissolving pulp in ionic liquid BmimCl via a sol–gel polymerization method. The surface morphology and physical properties were characterized by scanning electron microscopy, X-ray diffraction, and compression tests, etc. The differences in microstructure, crystalline structure, and mechanical performance of these two kinds of cellulose aerogels were studied and compared. Moreover, the two cellulose aerogels were used as adsorbents to remove dye, oil, and organic solvents. The kinetics and equilibrium capacity were investigated. Results indicate that the cellulose aerogels with high degrees of polymerization have much better mechanical strength and adsorption properties than the cellulose aerogels with low degrees of polymerization.