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BackgroundGlomus tumors are typically benign soft tissue tumors that occur at the extremities; malignant and viscerally occurring cases are extremely rare.Case presentationWe report a 49-year old male patient with a malignant esophageal glomus tumor that was complicated by lung and liver metastases. Genetic test results guided the patient’s individualized treatment. Consequently, treatment with Anlotinib combined with Tislelizumab achieved significant clinical benefits.ConclusionOur case report demonstrates that immunotherapy combined with anti-angiogenic therapy in patients with malignant esophageal glomus tumors can achieve significant efficacy and suggests the potential value of next-generation sequencing (NGS) detection in guiding personalized treatments in patients with malignant esophageal glomus tumors.

ObjectiveTo investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated.MethodsA total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the “rms” package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model.ResultsThe mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model.ConclusionPD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

ObjectivesThe diagnosis and treatment of brain tumors have greatly benefited from extensive research in traditional radiomics, leading to improved efficiency for clinicians. With the rapid development of cutting-edge technologies, especially deep learning, further improvements in accuracy and automation are expected. In this study, we explored a hybrid deep learning scheme that integrates several advanced techniques to achieve reliable diagnosis of primary brain tumors with enhanced classification performance and interpretability.MethodsThis study retrospectively included 230 patients with primary brain tumors, including 97 meningiomas, 66 gliomas and 67 pituitary tumors, from the First Affiliated Hospital of Yangtze University. The effectiveness of the proposed scheme was validated by the included data and a commonly used data. Based on super-resolution reconstruction and dynamic learning rate annealing strategies, we compared the classification results of several deep learning models. The multi-classification performance was further improved by combining feature transfer and machine learning. Classification performance metrics included accuracy (ACC), area under the curve (AUC), sensitivity (SEN), and specificity (SPE).ResultsIn the deep learning tests conducted on two datasets, the DenseNet121 model achieved the highest classification performance, with five-test accuracies of 0.989 ± 0.006 and 0.967 ± 0.013, and AUCs of 0.999 ± 0.001 and 0.994 ± 0.005, respectively. In the hybrid deep learning tests, LightGBM, a promising classifier, achieved accuracies of 0.989 and 0.984, which were improved from the original deep learning scheme of 0.987 and 0.965. Sensitivities for both datasets were 0.985, specificities were 0.988 and 0.984, respectively, and relatively desirable receiver operating characteristic (ROC) curves were obtained. In addition, model visualization studies further verified the reliability and interpretability of the results.ConclusionsThese results illustrated that deep learning models combining several advanced technologies can reliably improve the performance, automation, and interpretability of primary brain tumor diagnosis, which is crucial for further brain tumor diagnostic research and individualized treatment.

Administering medication is a crucial strategy in improving the prognosis for advanced endometrial cancer. However, the rise of drug resistance often leads to the resurgence of cancer or less-than-ideal treatment outcomes. Prior studies have shown that autophagy plays a dual role in the development and progression of endometrial cancer, closely associated with drug resistance. As a result, concentrating on autophagy and its combination with medical treatments might be a novel approach to improve the prognosis for endometrial cancer. This study explores the impact of autophagy on drug resistance in endometrial cancer, investigates its core mechanisms, and scrutinizes relevant treatments aimed at autophagy, aiming to illuminate the issue of treatment resistance in advanced endometrial cancer.

ObjectiveAccumulated evidence has suggested a relatively high recurrence rate in early-stage cervical cancer (CC) patients with risk factors. This study aimed to assess the efficacy and safety of consolidation chemotherapy following adjuvant therapy (concurrent chemoradiotherapy (CCRT) or radiotherapy (RT) alone) in stage IB-IIA CC patients with risk factors.MethodsA total of 237 stage IB-IIA CC patients who received radical surgery between January 2014 and December 2021 were included in the retrospective study. According to the types of adjuvant therapies, the patients were classified into the control group (CCRT or RT alone) and the study group (consolidation chemotherapy following CCRT or RT alone). The propensity score matching (PSM) was used to balance baseline characteristics between the two groups. The primary end points of the study were disease-free survival (DFS) and overall survival (OS).ResultsFor the entire cohort, no significant difference was observed in the DFS or OS between the study and control group, which was also confirmed in the PSM cohort (n=124). The multivariate analysis identified the high-risk factor type was an independent adverse prognostic factor for the patients. In patients with high risk factors, consolidation chemotherapy following adjuvant therapy was significantly associated with better clinical outcomes and identified as an independent prognostic favorable factor. Moreover, this association remained statistically significant in high-risk patients with ≥2 metastatic lymph nodes. In patients with intermediate risk factors, consolidation chemotherapy following adjuvant therapy was unrelated to DFS or OS. The safe assessment demonstrated consolidation chemotherapy following adjuvant therapy was significantly correlated with higher rates of ≥ grade 3 hematologic toxicities in both the global and subgroup analysis stratified by risk factor type.ConclusionConsolidation chemotherapy after adjuvant therapy provided survival benefits in stage IB-IIA CC patients with high risk factors, particularly those with ≥2 metastatic lymph nodes. However, related hematologic toxicities should be alerted in patient management. The actual efficacy and safety of consolidation chemotherapy still need to be investigated in more well-designed clinical trials.

PurposeTo assess the prognostic significance of β2-microglobulin decline index (β2M DI) in multiple myeloma (MM).Methods150 MM patients diagnosed with MM were enrolled in this study. Cox proportional hazards model was used to analyze the uni- and multivariate prognosis in training cohort (n=105). A new combined prognostic model containing β2M DI was built up based on the data in training cohort. The validation group was used to verify the model.Resultsβ2M DI showed significant correlation with prognosis in both uni- and multivariate analyses and had a good correlation with complete response (CR) rate and deep remission rate. The ROC and calibration curves in validation cohort (n=45) indicated a good predictive performance of the new model. Based on the median risk score of the training group, we classified patients into high- and low- risk groups. In both training and validation groups, patients in the low-risk group had longer overall survival (OS) time than that in the high-risk group (p

IntroductionGastric schwannoma is a rare benign tumor accounting for only 1–2% of alimentary tract mesenchymal tumors. Owing to their low incidence rate, most cases are misdiagnosed as gastrointestinal stromal tumors (GISTs), especially tumors with a diameter of less than 5 cm. Therefore, this study aimed to develop and validate a diagnostic nomogram based on computed tomography (CT) imaging features for the preoperative prediction of gastric schwannomas and GISTs (diameters = 2–5 cm).MethodsGastric schwannomas in 47 patients and GISTs in 230 patients were confirmed by surgical pathology. Thirty-four patients with gastric schwannomas and 167 with GISTs admitted between June 2009 and August 2022 at Hospital 1 were retrospectively analyzed as the test and training sets, respectively. Seventy-six patients (13 with gastric schwannomas and 63 with GISTs) were included in the external validation set (June 2017 to September 2022 at Hospital 2). The independent factors for differentiating gastric schwannomas from GISTs were obtained by multivariate logistic regression analysis, and a corresponding nomogram model was established. The accuracy of the nomogram was evaluated using receiver operating characteristic and calibration curves.ResultsLogistic regression analysis showed that the growth pattern (odds ratio [OR] 3.626; 95% confidence interval [CI] 1.105–11.900), absence of necrosis (OR 4.752; 95% CI 1.464–15.424), presence of tumor-associated lymph nodes (OR 23.978; 95% CI 6.499–88.466), the difference between CT values during the portal and arterial phases (OR 1.117; 95% CI 1.042–1.198), and the difference between CT values during the delayed and portal phases (OR 1.159; 95% CI 1.080–1.245) were independent factors in differentiating gastric schwannoma from GIST. The resulting individualized prediction nomogram showed good discrimination in the training (area under the curve [AUC], 0.937; 95% CI, 0.900–0.973) and validation (AUC, 0.921; 95% CI, 0.830–1.000) datasets. The calibration curve showed that the probability of gastric schwannomas predicted using the nomogram agreed well with the actual value.ConclusionThe proposed nomogram model based on CT imaging features can be used to differentiate gastric schwannoma from GIST before surgery.

The 3D culture of intestinal organoids entails embedding isolated intestinal crypts and bone marrow mesenchymal stem cells within a growth factor-enriched matrix gel. This process leads to the formation of hollow microspheres with structures resembling intestinal epithelial cells, which are referred to as intestinal organoids. These structures encompass various functional epithelial cell types found in the small intestine and closely mimic the organizational patterns of the small intestine, earning them the name “mini-intestines”. Intestinal tumors are prevalent within the digestive system and represent a significant menace to human health. Through the application of 3D culture technology, miniature colorectal organs can be cultivated to retain the genetic characteristics of the primary tumor. This innovation offers novel prospects for individualized treatments among patients with intestinal tumors. Presently established libraries of patient-derived organoids serve as potent tools for conducting comprehensive investigations into tissue functionality, developmental processes, tumorigenesis, and the pathobiology of cancer. This review explores the origins of intestinal organoids, their culturing environments, and their advancements in the realm of precision medicine. It also addresses the current challenges and outlines future prospects for development.

ObjectivesTo evaluate the effects of platinum-based neoadjuvant chemotherapy (NACT) on the STING/IFN pathway and tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC), as well as clinicopathological factors affecting patient survival.Materials and methodsA total of 68 patients aged 34-77 years with NSCLC who received neoadjuvant chemotherapy and surgical treatment from March 2012 to February 2019 were reviewed, and the clinical pathological data and paired tissue specimens before and after NACT were collected. Immunohistochemistry and immunofluorescence were used to detect the protein levels of STING, PD-L1 and IFN-β, and the infiltration density of CD3+ TILs and CD8+TILs. The correlation between the expression of STING, PD-L1, IFN-β and the infiltration density of CD3+ TILs and CD8+ TILs as well as the clinicopathological characteristics before and after NACT was analyzed. The relationship between the related indexes, clinicopathological features and prognosis was also discussed.ResultsNACT increased the expression of STING, IFN-β and PD-L1 in tumor cells, and the infiltration of CD3+ and CD8+ TILs. In addition, ypTNM stage, ypN stage, changes in CD3+ TILs and in PD-L1 were associated with DFS (disease-free survival). CD3+ TILs changes and ypN stage were associated with OS (overall survival). Notably, ypN stage and CD3+ TILs changes were independent prognostic factors for DFS and OS.ConclusionNACT stimulates STING/IFN-β pathway, promotes infiltration of CD3+ and CD8+ TILs, triggers innate and adaptive immunity, and also upregulates PD-L1, which complemented the rationale for neoadjuvant chemotherapy in combination with immunotherapy. In addition, DFS was longer in patients with ypTNM I, ypN0-1, and elevated CD3+TILs after NACT. Patients with ypN0 and elevated CD3+ TILs after NACT had better OS benefits.

ObjectiveThis study aims to predict cervical lymph node metastasis in papillary thyroid carcinoma (PTC) patients with high accuracy. To achieve this, we introduce a novel deep learning model, DualSwinThyroid, leveraging multi-modal ultrasound imaging data for prediction.Materials and methodsWe assembled a substantial dataset consisting of 3652 multi-modal ultrasound images from 299 PTC patients in this retrospective study. The newly developed DualSwinThyroid model integrates various ultrasound modalities and clinical data. Following its creation, we rigorously assessed the model’s performance against a separate testing set, comparing it with established machine learning models and previous deep learning approaches.ResultsDemonstrating remarkable precision, DualSwinThyroid achieved an AUC of 0.924 and an 96.3% accuracy on the test set. The model efficiently processed multi-modal data, pinpointing features indicative of lymph node metastasis in thyroid nodule ultrasound images. It offers a three-tier classification that aligns each level with a specific surgical strategy for PTC treatment.ConclusionDualSwinThyroid, a deep learning model designed with multi-modal ultrasound radiomics, effectively estimates the degree of cervical lymph node metastasis in PTC patients. In addition, it also provides early, precise identification and facilitation of interventions for high-risk groups, thereby enhancing the strategic selection of surgical approaches in managing PTC patients.

ObjectiveIncreasing studies have highlighted the potential utility of non-invasive prognostic biomarkers in advanced lung cancer patients receiving immune checkpoint inhibitor (ICI) based anti-cancer therapies. Here, a novel prognostic predictor named as C-PLAN integrating C-reactive protein (CRP), Performance status (PS), Lactate dehydrogenase (LDH), Albumin (ALB), and derived Neutrophil-to-lymphocyte ratio (dNLR) was identified and validated in a single-center retrospective cohort.MethodsThe clinical data of 192 ICI-treated lung cancer patients was retrospectively analyzed. The pretreatment levels of CRP, PS, LDH, ALB and dNLR were scored respectively and then their scores were added up to form C-PLAN index. The correlation of C-PLAN index with the progression-free survival (PFS) or overall survival (OS) was analyzed by a Kaplan–Meier model. The multivariate analysis was used to identify whether C-PLAN index was an independent prognostic predictor.ResultsA total of 88 and 104 patients were included in the low and high C-PLAN index group respectively. High C-PLAN index was significantly correlated with worse PFS and OS in ICI-treated lung cancer patients (both p

ObjectivesTo explore the feasibility and importance of deep learning (DL) based on 68Ga-prostate-specific membrane antigen (PSMA)-11 PET/CT in predicting pathological upgrading from biopsy to radical prostatectomy (RP) in patients with prostate cancer (PCa).MethodsIn this retrospective study, all patients underwent 68Ga-PSMA-11 PET/CT, transrectal ultrasound (TRUS)-guided systematic biopsy, and RP for PCa sequentially between January 2017 and December 2022. Two DL models (three-dimensional [3D] ResNet-18 and 3D DenseNet-121) based on 68Ga-PSMA-11 PET and support vector machine (SVM) models integrating clinical data with DL signature were constructed. The model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.ResultsOf 109 patients, 87 (44 upgrading, 43 non-upgrading) were included in the training set and 22 (11 upgrading, 11 non-upgrading) in the test set. The combined SVM model, incorporating clinical features and signature of 3D ResNet-18 model, demonstrated satisfactory prediction in the test set with an AUC value of 0.628 (95% confidence interval [CI]: 0.365, 0.891) and accuracy of 0.727 (95% CI: 0.498, 0.893).ConclusionA DL method based on 68Ga-PSMA-11 PET may have a role in predicting pathological upgrading from biopsy to RP in patients with PCa.

Primary hepatic adenosquamous carcinoma is considered a rare subtype of intrahepatic cholangiocarcinoma, with fewer than 100 domestic and international cases reported. This malignancy exhibits a high degree of malignancy, strong invasiveness, and an unfavorable prognosis due to its propensity for early lymph node and intrahepatic metastasis. The etiology of this disease remains uncertain, and preoperative diagnosis is exceedingly challenging owing to the nonspecific clinical features and lack of specificity in imaging studies. Radical surgical resection is the most effective treatment for non-metastatic tumors, while targeted adjuvant therapy administered postoperatively can enhance therapeutic efficacy and delay tumor recurrence. This article documents the diagnostic and therapeutic course of a case of primary hepatic adenosquamous carcinoma treated at our medical institution, along with a comprehensive synthesis of the clinical characteristics and advances in the diagnosis and treatment of this disease, aiming to augment understanding and serve as a reference for future clinical endeavors.

Anaplastic lymphoma kinase gene (ALK) rearrangement is present in only approximately 5% of non-small cell lung cancers (NSCLCs) and is scarce in LCNEC patients. The conventional first-line treatment options are chemotherapy combined with immunotherapy or chemotherapy followed by palliative radiotherapy. In this report, we present two cases of metastatic LCNEC with EML4-ALK fusion that were treated with ALK-TKI inhibitors and demonstrated a rapid therapeutic response. Both patients were nonsmoking women who declined cytotoxic chemotherapy, underwent Next-Generation Sequencing (NGS), and confirmed EML4-ALK fusion. They were treated with alectinib as first-line therapy, and the tumors showed significant shrinkage after two months, achieving a PR (defined as a more than 30% decrease in the sum of maximal dimensions). The PFS was 22 months and 32 months, respectively, until the last follow-up. A systematic review of all previously reported cases of LCNEC with ALK mutations identified only 21 cases. These cases were characterized by being female (71.4%), nonsmoking (85.7%), diagnosed at a relatively young age (median age 51.1), and stage IV (89.5%), with an overall response rate (ORR) of 90.5%. PFS and OS were significantly longer than those treated with conventional chemotherapy/immunotherapy. Based on the clinical characteristics and the effective therapeutic outcomes with ALK inhibitors in LCNEC patients with ALK fusion, we recommend routine ALK IHC (economical, affordable, and convenient, but with higher false positives) as a screening method in advanced LCNEC patients, particularly nonsmoking females or those who are not candidates for or unwilling to undergo cytotoxic chemotherapy. Further molecular profiling is necessary to confirm these potential beneficiaries. We suggest TKI inhibitors as the first-line treatment for metastatic LCNEC with ALK fusion. Additional studies on larger cohorts are required to assess the prevalence of ALK gene fusions and their sensitivity to various ALK inhibitors.

PurposeTo establish and validate a radiomics nomogram for predicting recurrence of esophageal squamous cell carcinoma (ESCC) after esophagectomy with curative intent.Materials and methodsThe medical records of 155 patients who underwent surgical treatment for pathologically confirmed ESCC were collected. Patients were randomly divided into a training group (n=109) and a validation group (n=46) in a 7:3 ratio. ​Tumor regions are accurately segmented in computed tomography images of enrolled patients. Radiomic features were then extracted from the segmented tumors. We selected the features by Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. A radiomics signature was then built by logistic regression analysis. To improve predictive performance, a radiomics nomogram that incorporated the radiomics signature and independent clinical predictors was built. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA).ResultsWe selected the five most relevant radiomics features to construct the radiomics signature. The radiomics model had general discrimination ability with an area under the ROC curve (AUC) of 0.79 in the training set that was verified by an AUC of 0.76 in the validation set. The radiomics nomogram consisted of the radiomics signature, and N stage showed excellent predictive performance in the training and validation sets with AUCs of 0.85 and 0.83, respectively. Furthermore, calibration curves and the DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram.ConclusionWe successfully established and validated a prediction model that combined radiomics features and N stage, which can be used to predict four-year recurrence risk in patients with ESCC who undergo surgery.

ImportancePatients with EGFR mutations who have advanced-stage non-small cell lung cancer (NSCLC) already receive tyrosine kinase inhibitors (TKIs) as the standard first-line therapy. Notably, Yunnan is a regional high incidence area of lung cancer in the highlands with a high rate of rare EGFR mutations. Overall, lung cancer patients in Xuanwei may present a distinct subgroup globally. Recent studies suggested that the NSCLC cohort in Xuanwei harbored a significantly higher uncommon mutation rate. However, little was known about the clinicopathological features and treatment efficacy of EGFR-TKI in Yunnan NSCLC patients.ObjectiveThis study aimed to investigate the clinical impact of histologic type on the survival outcomes of patients with stage IIIB and IV NSCLC receiving EGFR-TKI treatment of Yunnan in southwestern China.MethodsIn this retrospective study, we enrolled advanced NSCLC patients (IIIB-IV) with EGFR mutations who were first diagnosed and treated at Yunnan Cancer hospital from January 2016 to December 2019. Sociodemographics, lifestyle, survival, and clinicopathological characteristics of the patients were collected. The Kaplan-Meier method was used to assess the OS and PFS of patients. An analysis of prognostic factors was conducted using Cox regression.ResultsA total of 468 eligible patients were included. The median progression-free survival (PFS) and overall survival(OS) were 11.30(95% CI, 10.12-12.48) months and 30.30(95% CI, 26.24-34.36) months. Based on survival analysis among all the patients,females(HR=0.815;95% CI:0.671-0.989; P=0.017), Xuanwei origin (HR=0.776; 95% CI: 0.609-0.989; P=0.040), sample types(HR=0.780; 95% CI: 0.642-0.947; P=0.012) had a longer PFS. Multivariable analysis showed that only the sample type was an independent factor on median PFS with EGFR-TKI therapy. Patients less than 60 years old (HR=1.433; 95% CI:1.134-1.812, P=0.003)had better OS, but objectives with BMI≥24kg/m2(HR=0.653; 95% CI: 0.500-0.864; P=0.002), females(HR=0.776; 95% CI:0.613-0.982; P=0.035)and patients with tissue sample type (HR=0.760; 95% CI:0.600-.0961; P=0.022) had better OS. Notably, subgroup analysis of our study also found that PFS was significantly better in patients with G719X, L861Q, S768I, G719X+L861Q, and G719X+S768I in Xuanwei than classical mutation ones, including 19-Del and L858R (median 22.7 vs. 12.0 months, HR=0.523, P=0.010), while PFS was inferior in patients with rare mutations of EGFR in non-Xuanwei than the classical mutation ones (median 5.10 vs. 11.10 months, HR=1.760, P=0.015).ConclusionNSCLC patients in Yunnan displayed a unique EGFR mutation profile, especially a higher prevalence of EGFR uncommon and compound mutations subtype. This study indicates prognostic factors of NSCLC treated with EGFR-TKI in Yunan and Xuanwei. This study will provide new clinical evidence for EGFR-TKI-targeted therapy in patients with rare EGFR mutations in China and worldwide. More researchs were needed for NSCLC EGFR-TKI therapy and medical insurance policy-making in Yunnan, Xuanwei area and uncommon especially.

IntroductionA large-sample study focusing on VIN lesions of a more precise thickness is needed to help guide clinical treatment. This study aimed to investigate the depth of vulvar intraepithelial neoplasia (VIN) and involved skin appendages to provide evidence for laser surgery.MethodsThe study retrospectively enrolled and analyzed the clinical characteristics of VIN patients in the obstetrics and gynecology department of a university hospital between January 1, 2019 and December 30, 2021. The study further explored the thickness of epithelium and skin appendages of 285 women with low-grade VIN (VIN1) and 285 women with high-grade VIN (VIN2/3).ResultsThe study included 1,139 (80%) VIN1 and 335 (20%) VIN2/3 cases. The VIN1 and VIN2/3 groups showed a significant difference in human papillomavirus infection (P

ObjectiveMultiple myeloma (MM) is a highly characteristic tumor that is influenced by numerous factors that determine its prognosis. Studies indicate that the presence of circulating plasma cells (cPCs) is a detrimental factor that significantly impacts the prognosis of patients with MM.MethodsThis study retrospectively analyzed the prognostic value of cPCs quantified by 10-color flow cytometry in 145 newly diagnosed MM (NDMM) cases in the First Affiliated Hospital of Soochow University from November 2018 to February 2021. The study was approved by the Ethics Committee of the hospital (2021 No. 93).ResultsOf the 145 patients, 99 (68.2%) were detected cPCs. Through receiver operating characteristics (ROC) analysis, an optimal threshold of 0.165% was identified as a predictor for overall survival (OS). The median progression-free survival (PFS) was 33 months in patients with cPCs ≥0.165%, whereas those with cPCs

ObjectiveTo assess the prognostic value of a model based on pre-treatment T2WI-based radiomic features and postoperative pathological staging in patients with locally advanced rectal cancer who have undergone neoadjuvant chemoradiotherapy.MethodsRadiomic features were derived from T2WI, and a radiomic signature (RS) was established and validated for the prediction of distant metastases (DM). Subsequently, we designed and validated a nomogram model that combined the radiomic signature and postoperative pathological staging for enhanced DM prediction. Performance measures such as the concordance index (C-index) and area under the curve (AUC) were computed to assess the predictive accuracy of the models.ResultsA total of 260 patients participated in this study, of whom 197 (75.8%) were male, and the mean age was 57.2 years with a standard deviation of 11.2 years. 15 radiomic features were selected to define the radiomic signature. Patients with a high-risk radiomic signature demonstrated significantly shorter distant metastasis-free survival (DMFS) in both the development and validation cohorts. A nomogram, incorporating the radiomic signature, pathological T stage, and N stage, achieved an area under the curve (AUC) value of 0.72 (95% CI, 0.60-0.83) in the development cohort and 0.83 (95% CI, 0.73-0.92) in the validation cohort.ConclusionA radiomic signature derived from T2WI-based radiomic features can effectively distinguish patients with varying risks of DM. Furthermore, a nomogram integrating the radiomic signature and postoperative pathological stage proves to be a robust predictor of DMFS.

Background2’,5’-oligoadenylate synthetase 1 (OAS1), has been reported as a tumor driver gene in breast carcinoma and pancreatic carcinoma. However, the role of OAS1 in most tumors has not been reported.MethodsThe original data of 35 tumor types were down load from the TCGA (The Cancer Genome Atlas) database and Human Protein Atlas (HPA) database. TIMER2, Kmplot, UALCAN, and TISIDB tools were used to investigate the expression and function of OAS1, and the role of OAS1 in prognosis, diagnostic value, and immune characteristics of pan-cancer. LUAD and PRAD cell lines, A549, H1975, PC-3 and C4-2 were utilized to perform cell function tests.ResultsOAS1 expression was up-regulated in 12 tumor types and down-regulated in 2 tumor types. High OAS1 expression was correlated with poor prognosis in 6 tumor types, while high OAS1 expression was correlated with good prognosis in 2 tumor types. OAS1 was correlated with molecular subtypes in 8 tumor types and immune subtypes in 12 tumor types. OAS1 was positively associated with the expression of numerous immune checkpoint genes and tumor mutational burden (TMB). OAS1 had potential diagnostic value in 15 tumor types. Silence of OAS1 significantly inhibited the cell proliferation ability, and promoted G2/M cell cycle arrest of LUAD and PRAD cells. Meanwhile, silence of OAS1 enhanced cisplatin-induced apoptosis of LUAD and PRAD cells, but weakened cell migration.ConclusionThis pan-cancer study suggests that OAS1can be used as a molecular biomarker for prognosis in pan-cancer and may play an important role in tumor immune response.

Adjuvant radiotherapy after breast-conserving surgery has become an integral part of the treatment of breast cancer. In recent years, the development of radiotherapy technology has made great progress in this field, including the comparison of the curative effects of various radiotherapy techniques and the performance of the segmentation times. The choice of radiotherapy technology needs to be co-determined by clinical evidence practice and evaluated for each individual patient to achieve precision radiotherapy. This article discusses the treatment effects of different radiotherapy, techniques, the risk of second cancers and short-range radiation therapy techniques after breast-conserving surgery such as hypo fractionated whole breast irradiation and accelerated partial breast irradiation. The choice of radiotherapy regimen needs to be based on the individual condition of the patient, and the general principle is to focus on the target area and reduce the irradiation of the normal tissues and organs. Short-range radiotherapy and hypofractionated are superior to conventional radiotherapy and are expected to become the mainstream treatment after breast-conserving surgery.

BackgroundRefractory/relapsed acute myeloid leukemia (R/R AML) has unsatisfactory outcomes even after allogeneic hematopoietic stem cell transplantation. Long-term survival is mainly influenced by complete remission (CR) rates after induction therapies.ObjectivesTo investigate CR/CR with incomplete hematologic recovery (CRi) rates and adverse events with a new induction therapy (bortezomib, homoharringtonine, and cytarabine [BHA]) for patients with R/R AML.MethodsWe enrolled 21 patients with R/R AML (median age, 42 [range, 30–62] years), who received BHA for remission induction (bortezomib, 1.3 mg/m2/day on days 1 and 4; homoharringtonine, 4 mg/m2/day for 5 days, and cytarabine, 1.5 g/m2/day for 5 days). CR and adverse events were assessed.ResultsAfter one course of BHA, the CR/CRi and partial remission rates were 38.1% and 14.3%, respectively, with an overall response rate (ORR) of 52.4% in 21 patients. 9 of 21 patients harbored FLT3-ITD or FLT3-TKD mutations, and achieved either CR/CRi or ORR of 66.7% (P=0.03) by comparison with that in R/R AML without FLT3 mutation. After induction therapy, consolidation chemotherapy or allogeneic hematopoietic stem cell transplantation led to a one-year overall survival of 27.8% in all patients. One-year relapse-free survival was 50% in 8 patients who had achieved CR/CRi after one course of BHA. During induction, non-hematologic adverse events (grade 3/4) commonly were infection (90.5%), hypokalemia (14.4%), hypocalcemia (14.3%), and mucositis (9.5%). In patients achieving CR, the median time to neutrophil count >0.5×109/L and time to platelet count >20×109/L were 15 (13–17) days and 13 (13–18) days, respectively.ConclusionBHA chemotherapy regimen was safe and tolerable to serve as an induction therapy for R/R AML, particularly with FLT3 mutation. The higher CR/CRi rate will give a clue to determine a potentialeffectiveness of BHA for AML patients carrying FLT3 mutation in a further investigation.Clinical trial registrationhttps://www.chictr.org.cn/, identifier ChiCTR2000029841.

BackgroundLimited studies have observed the prognostic value of CT images for esophageal neuroendocrine carcinoma (NEC) due to rare incidence and low treatment experience in clinical. In this study, the pretreatment enhanced CT texture features and clinical characteristics were investigated to predict the overall survival of esophageal NEC.MethodsThis retrospective study included 89 patients with esophageal NEC. The training and testing cohorts comprised 61 (70%) and 28 (30%) patients, respectively. A total of 402 radiomics features were extracted from the tumor region that segmented pretreatment venous phase CT images. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to feature dimension reduction, feature selection, and radiomics signature construction. A radiomics nomogram was constructed based on the radiomics signature and clinical risk factors using a multivariable Cox proportional regression. The performance of the nomogram for the pretreatment prediction of overall survival (OS) was evaluated for discrimination and calibration.ResultsOnly the enhancement degree was an independent factor in clinical variable influenced OS. The radiomics signatures demonstrated good predictability for prognostic status discrimination. The radiomics nomogram integrating texture signatures was slightly superior to the nomogram derived from the combined model with a C-index of 0.844 (95%CI: 0.783-0.905) and 0.847 (95% CI: 0.782-0.912) in the training set, and 0.805 (95%CI: 0.707-0.903) and 0.745 (95% CI: 0.639-0.851) in the testing set, respectively.ConclusionThe radiomics nomogram based on pretreatment CT radiomics signature had better prognostic power and predictability of the overall survival in patients with esophageal NEC than the model using combined variables.

ObjectivesTo evaluate the magnetic resonance imaging (MRI) features of bile duct adenoma.MethodsThe data of 28 patients [with 32 pathologically confirmed bile duct adenomas, including 15 with malignant change (malignant group) and 17 without malignant change (benign adenoma group)] were retrospectively reviewed. Abdominal MRI was performed for all patients; in addition, dynamic enhanced MRI was performed for 18 lesions. The MRI features, including lesion location, maximum size, morphology, signal characteristics, enhancement type, and appearance of the bile duct, were assessed by two abdominal radiologists. Apparent diffusion coefficient (ADC) values were measured and compared.ResultsOf the 32 bile duct adenomas, 22 (68.75%) involved the common bile duct (CBD). While 14/32 (43.75%) lesions presented as focal eccentric-type masses, 9/32 (28.13%) presented as plaque-like masses, 4/32 (12.50%) as bile duct casting masses, and 5/32 (15.62%) as infiltrative masses. A frond-like superficial appearance was seen in 8/32 (25%) lesions. Infiltrative masses were significantly more common in the malignant group than in the benign adenoma group (P = 0.015). While 23/32 (71.88%) lesions were isointense on T1-weighted imaging (T1WI), 24/32 (75%) were hyperintense on T2-weighted imaging (T2WI). Bile duct dilatation was present upstream of the lesion in all cases. Bile duct dilatation at the lesion was seen in 24/32 (75%) cases and downstream of the lesion in 6/32 (18.75%) cases. Of the 18 lesions that underwent dynamic enhanced MRI, 14 (77.78%) showed moderate enhancement and 13 (72.22%) showed persistent enhancement. On diffusion-weighted imaging (DWI), 27/32 (84.37%) lesions showed hyperintensity. Mean ADC value was comparable between the malignant group and the benign adenoma group (P = 0.156).ConclusionsBile duct adenoma primarily presents as intraductal growth in the CBD, usually with bile duct dilatation at the lesion site or upstream to it. Most lesions are isointense on T1WI, are hyperintense on T2WI and DWI, and show moderate enhancement. A superficial frond-like appearance of the lesion and bile duct dilatation at the lesion or downstream to it might be characteristics of bile duct adenoma. An infiltrative appearance might indicate malignant transformation.

Breast cancer, the most prevalent malignant tumor among women, poses a significant threat to patients’ physical and mental well-being. Recent advances in early screening technology have facilitated the early detection of an increasing number of breast cancers, resulting in a substantial improvement in patients’ overall survival rates. The primary techniques used for early breast cancer diagnosis include mammography, breast ultrasound, breast MRI, and pathological examination. However, the clinical interpretation and analysis of the images produced by these technologies often involve significant labor costs and rely heavily on the expertise of clinicians, leading to inherent deviations. Consequently, artificial intelligence(AI) has emerged as a valuable technology in breast cancer diagnosis. Artificial intelligence includes Machine Learning(ML) and Deep Learning(DL). By simulating human behavior to learn from and process data, ML and DL aid in lesion localization reduce misdiagnosis rates, and improve accuracy. This narrative review provides a comprehensive review of the current research status of mammography using traditional ML and DL algorithms. It particularly highlights the latest advancements in DL methods for mammogram image analysis and offers insights into future development directions.

BackgroundAlthough anthracyclines are the first-line chemotherapy drugs for treating non-M3 acute myeloid leukaemia (AML), their efficacy remains limited. It is important to identify factors that influence the efficacy of anthracyclines against AML. Mitochondrial apoptosis-related genes play significant roles in the pathogenesis, treatment, and prognosis of AML.MethodsWe utilized the CRISPR/Cas9 screening system to find AML anthracyclines resistance related genes and several mitochondrial apoptosis-related genes, such as BCL2L11, CASP8, TP63, TP53BP2, PLAUR, SOD2, BNIP3L, and MMP9, were screened out. Then, DNA from 279 patients with AML and 321 healthy individuals were extracted and the contributions of single nucleotide polymorphisms (SNPs) within these genes to the patient’s chemotherapy response, susceptibility to AML, and overall survival were investigated.ResultsOur findings indicated that SNP rs4251864 in the PLAUR gene was associated with an increase in complete remission after anthracycline-based induction chemotherapy. rs4880 in SOD2 was associated with the response to the second course of chemotherapy, whereas rs3789068 in BCL2L11 was associated with susceptibility to AML.ConclusionsOur results about the association of SNPs in mitochondrial apoptosis-related genes with the response to anthracycline-based chemotherapy in AML provide an important reference for predicting the treatment outcomes in patients with this disease.

IntroductionGastric cancer is a highly heterogeneous malignant tumor of the digestive system. Anti-HER2 treatment can inhibit downstream signaling pathways and improve clinical treatment and outcomes in patients with HER2 protein overexpression. Currently, two standard methods for evaluating HER2 expression status are immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). However, these low-throughput assays often produce discordant or equivocal results.MethodsIn this study, we presented a new HER2 protein detection method based on mass spectrometry selected reaction monitoring (MS-SRM) and validated the method. We conducted a retrospective study on 118 formalin-fixed paraffin-embedded (FFPE) tissues from patients with advanced gastric adenocarcinoma in northern China, and we compared the MS-SRM results with those from IHC and correlated them with FISH. ResultsWe established and validated the upper and lower detection limits (300-700 amol/μg) for abnormal HER2 protein expression in advanced gastric cancer. We also found that, among samples with mixed Lauren subtypes, those with a high level of HER2 expression had typical intestinal type features in pathology. DiscussionThis study demonstrated that the MS-SRM method can overcome the limitations and deficiencies of IHC, directly quantify the expression of HER2 protein in tumor cells and be used as a supplement to IHC. It has the potential to be used as a companion diagnosis for new drugs used to treat advanced gastric cancer. Large-scale clinical validation is required.

PurposePrevious studies have shown that DNA methylation in peripheral blood may be associated with breast cancer (BC). To explore the association between the methylation level of the Cathepsin Z (CTSZ) gene in peripheral blood and BC, we conducted a case–control study in the Chinese population.MethodsPeripheral blood samples were collected from 567 BC cases, 635 healthy controls, and 303 benign breast disease (BBD) cases. DNA extraction and bisulfite-specific PCR amplification were performed for all samples. The methylation levels of seven sites of the CTSZ gene were quantitatively determined by Mass spectrometry. The odds ratios (ORs) of CpG sites were evaluated for BC risk using per 10% reduction and quartiles analyses by logistic regression.ResultsOur analysis showed that five out of the seven CpG sites exhibited significant associations with hypomethylation of CTSZ and BC, compared to healthy controls. The highest OR was for Q2 of CTSZ_CpG_1 (OR: 1.62, P=0.006), particularly for early-stage breast cancer in the case of per 10% reduction of CTSZ_CpG_1 (OR: 1.20, P=0.003). We also found that per 10% reduction of CTSZ_CpG_5 (OR: 1.39, P=0.004) and CTSZ_CpG_7,8 (OR: 1.35, P=0.005) were associated with increased BC risk. Our study also revealed that four out of seven CpG sites were linked to increased BC risk in women under 50 years of age, compared to healthy controls. The highest OR was for per 10% reduction of CTSZ_CpG_1 (OR: 1.47, P

BackgroundLymph node metastasis frequently occurs in head and neck squamous cell carcinoma (HNSCC) patients, and [18F] fluorodeoxyglucose positron emission tomography with computed tomography (18FDG-PET/CT) examination for lymph node metastasis could result in false negativity and delay following treatment. However, the mechanism and resolution for 18FDG-PET/CT false negatives remain unclear. Our study was aim to found biomarkers for false negativity and true positivity from a metabolic perspective.MethodsNinety-two patients diagnosed with HNSCC who underwent preoperative 18FDG-PET/CT and subsequent surgery in our institution were reviewed. Immunohistochemistry (IHC) examinations of glucose metabolism (GLUT1 and GLUT5), amino acid metabolism4 (GLS and SLC1A5), and lipid metabolism (CPT1A and CD36) markers were conducted on their primary lesion and lymph node sections.ResultsWe identified specific metabolic patterns of the false-negative group. Significantly, CD36 IHC score of primary lesions was higher in false-negative group than true-positive group. Moreover, we validated pro-invasive biological effects of CD36 by bioinformatics analysis as well as experiments. Conclusion: IHC examination of CD36 expression, which is a lipid metabolism marker, in primary lesions could distinguish HNSCC patients’ lymph nodes false negatives in 18FDG-PET/CT.

PurposeTo establish a high-risk prediction model for aromatase inhibitor-associated bone loss (AIBL) in patients with hormone receptor-positive breast cancer.MethodsThe study included breast cancer patients who received aromatase inhibitor (AI) treatment. Univariate analysis was performed to identify risk factors associated with AIBL. The dataset was randomly divided into a training set (70%) and a test set (30%). The identified risk factors were used to construct a prediction model using the eXtreme gradient boosting (XGBoost) machine learning method. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods were used for comparison. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the model in the test dataset.ResultsA total of 113 subjects were included in the study. Duration of breast cancer, duration of aromatase inhibitor therapy, hip fracture index, major osteoporotic fracture index, prolactin (PRL), and osteocalcin (OC) were found to be independent risk factors for AIBL (p < 0.05). The XGBoost model had a higher AUC compared to the logistic model and LASSO model (0.761 vs. 0.716, 0.691).ConclusionThe XGBoost model outperformed the logistic and LASSO models in predicting the occurrence of AIBL in patients with hormone receptor-positive breast cancer receiving aromatase inhibitors.

BackgroundPrimary central nervous system lymphoma (PCNSL) is an uncommon variant of non-Hodgkin lymphoma (NHL) with high aggressiveness and poor prognosis. Although complete remission (CR) could be achieved with therapy, some patients remain refractory or recurrently with a worse response to salvage treatment and poor prognosis. No consensus on rescue therapy has been established currently. This study is aimed to evaluate the efficacy of radiotherapy or chemotherapy in first-time relapsed or refractory progressed PCNSL (R/R PCNSL) and analysis the prognostic factors, to explore differences between relapsed and refractory PCNSL.MethodsTotally 105 R/R PCNSL patients from Huashan Hospital between 1 January 2016 and 31 December 2020 were enrolled, underwent salvage radiotherapy or chemotherapy and received response assessments after each course. PFS1 was defined as the time from diagnosis to the first time of recurrence or refractory progression. Statistical analysis was performed with SPSS version 26.0.ResultsResponse and survival were analyzed over a 17.5months (median) follow-up. Compared to relapsed PCNSL (n = 42), refractory PCNSL (n = 63) had a shorter median PFS1 related to deep lesions. 82.4% of cases were discovered as the second relapse or progression. ORR and PFS were both higher in relapsed PCNSL than those in refractory PCNSL. ORR of radiotherapy in both relapsed and refractory PCNSL was higher than that of chemotherapy. Elevated CSF protein and ocular involvement were related to PFS and OS after recurrence respectively in relapsed PCNSL. Age ≥ 60y was unfavorable to OS-R (OS after recurrence or progression) in refractory PCNSL.ConclusionsOur results indicate that relapsed PCNSL responds well to inducing and salvage therapy and has a better prognosis compared to refractory PCNSL. Radiotherapy is effective for PCNSL after the first relapse or progression. Age, CSF protein level, and ocular involvement could be potential factors to predict prognosis.

BackgroundThe number of publications on acupuncture for cancer pain is increasing rapidly with an upward tendency. Considering that no bibliometric articles related to this topic have been published yet. It is necessary to evaluate the global scientific output of research in this field, and shed light on the direction of clinical cancer pain management in the future.MethodsResearch publications regarding acupuncture on cancer pain from inception to 2022 were downloaded from the Web of Science Core Collection. Bibliometric analyses were performed using CiteSpace software, the bibliometrix R package, and VOSviewer software. Network maps were generated to assess the collaborations between different countries, institutions, authors, and keywords. And clusters map was generated to evaluate reference.ResultsA total of 790 articles related to acupuncture therapy for cancer pain were identified. We observe that the number of publications is gradually increasing over time. China and the United States were the main contributors. Mem Sloan Kettering Canc Ctr (38 papers) and Beijing Univ Chinese Med (28 papers) contributed the most publications, becoming the leading contributors in this field. Although J Clin Oncol (28 articles) ranked ninth in terms of publication volume, it was the journal with the most citations and the highest number of IF (50.717) and H-index (494) at the same time. MAO J from Mem Sloan Kettering Canc Ctr was the most prolific author (23 articles). The main hot topics included matters related to acupuncture (239 times), pain (199 times), management (139 times), quality of life (107 times), electroacupuncture (100 times), and breast cancer (82 times).ConclusionOur bibliometric analysis provides a comprehensive overview of the development of acupuncture for cancer pain, enabling relevant authors and research teams to identify the current research status in this field. At the same time, acupuncture for breast cancer (BC) pain, aromatase inhibitor-induced arthralgia (AIA), and chemotherapy-induced peripheral neuropathy (CIPN) may soon become prospective focus.

BackgroundChronic atrophic gastritis (CAG) is a precancerous condition. It is not easy to detect CAG in endoscopy. Improving the detection rate of CAG under endoscopy is essential to reduce or interrupt the occurrence of gastric cancer. This study aimed to construct a deep learning (DL) model for CAG recognition based on endoscopic images to improve the CAG detection rate during endoscopy.MethodsWe collected 10,961 endoscopic images and 118 video clips from 4,050 patients. For model training and testing, we divided them into two groups based on the pathological results: CAG and chronic non-atrophic gastritis (CNAG). We compared the performance of four state-of-the-art (SOTA) DL networks for CAG recognition and selected one of them for further improvement. The improved network was called GAM-EfficientNet. Finally, we compared GAM-EfficientNet with three endoscopists and analyzed the decision basis of the network in the form of heatmaps.ResultsAfter fine-tuning and transfer learning, the sensitivity, specificity, and accuracy of GAM-EfficientNet reached 93%, 94%, and 93.5% in the external test set and 96.23%, 89.23%, and 92.37% in the video test set, respectively, which were higher than those of the three endoscopists.ConclusionsThe CAG recognition model based on deep learning has high sensitivity and accuracy, and its performance is higher than that of endoscopists.

ObjectiveTo develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR).MethodsThis study included 100 post-breast-conserving patients with the pCT images, CBCT images, and the target contours, which the physicians delineated. The CT images were generated from CBCT images via the proposed CLG model. We used the Sct images as the fixed images instead of the CBCT images to achieve the multi-modality image registration accurately. The deformation vector field is applied to propagate the target contour from the pCT to CBCT to realize the automatic target segmentation on CBCT images. We calculate the Dice similarity coefficient (DSC), 95 % Hausdorff distance (HD95), and average surface distance (ASD) between the prediction and reference segmentation to evaluate the proposed method.ResultsThe DSC, HD95, and ASD of the target contours with the proposed method were 0.87 ± 0.04, 4.55 ± 2.18, and 1.41 ± 0.56, respectively. Compared with the traditional method without the synthetic CT assisted (0.86 ± 0.05, 5.17 ± 2.60, and 1.55 ± 0.72), the proposed method was outperformed, especially in the soft tissue target, such as the tumor bed region.ConclusionThe CLG model proposed in this study can create the high-quality sCT from low-quality CBCT and improve the performance of DIR between the CBCT and the pCT. The target segmentation accuracy is better than using the traditional DIR.

BackgroundTumor grade is associated with the treatment and prognosis of endometrial cancer (EC). The accurate preoperative prediction of the tumor grade is essential for EC risk stratification. Herein, we aimed to assess the performance of a multiparametric magnetic resonance imaging (MRI)-based radiomics nomogram for predicting high-grade EC.MethodsOne hundred and forty-three patients with EC who had undergone preoperative pelvic MRI were retrospectively enrolled and divided into a training set (n =100) and a validation set (n =43). Radiomic features were extracted based on T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted images. The minimum absolute contraction selection operator (LASSO) was implemented to obtain optimal radiomics features and build the rad-score. Multivariate logistic regression analysis was used to determine the clinical MRI features and build a clinical model. We developed a radiomics nomogram by combining important clinical MRI features and rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the performance of the three models. The clinical net benefit of the nomogram was assessed using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination index (IDI).ResultsIn total, 35/143 patients had high-grade EC and 108 had low-grade EC. The areas under the ROC curves of the clinical model, rad-score, and radiomics nomogram were 0.837 (95% confidence interval [CI]: 0.754–0.920), 0.875 (95% CI: 0.797–0.952), and 0.923 (95% CI: 0.869–0.977) for the training set; 0.857 (95% CI: 0.741–0.973), 0.785 (95% CI: 0.592–0.979), and 0.914 (95% CI: 0.827–0.996) for the validation set, respectively. The radiomics nomogram showed a good net benefit according to the DCA. NRIs were 0.637 (0.214–1.061) and 0.657 (0.079–1.394), and IDIs were 0.115 (0.077–0.306) and 0.053 (0.027–0.357) in the training set and validation set, respectively.ConclusionThe radiomics nomogram based on multiparametric MRI can predict the tumor grade of EC before surgery and yield a higher performance than that of dilation and curettage.

BackgroundB-cell receptor–associated protein 31 (BAP31) has been recognized as a tumor-associated protein and has largely been shown to promote metastasis in a variety of cancers. Cancer metastasis arises through multistep pathways, and the induction of angiogenesis is shown to be a rate-limiting step in the process of tumor metastasis.Methods and resultsThis study explored the effect of BAP31 on colorectal cancer (CRC) angiogenesis by regulating the tumor microenvironment. First, exosomes from BAP31-regulated CRCs affected the transition of normal fibroblasts to proangiogenic cancer-associated fibroblasts (CAFs) in vivo and in vitro. Next, microRNA sequencing was performed to analyze the microRNA expression profile of exosomes secreted from BAP31- overexpressing CRCs. The results indicated that the expression of BAP31 in CRCs significantly altered the levels of exosomal microRNAs, such as miR-181a- 5p. Meanwhile, an in vitro tube formation assay showed that fibroblasts with high levels of miR-181a-5p significantly promoted endothelial cell angiogenesis. Critically, we first identified that miR-181a-5p directly targeted the 3'-untranslated region (3′UTR) of reversion-inducing cysteine-rich protein with kazal motifs (RECK) using the dual-luciferase activity assay, which drove fibroblast transformation into proangiogenic CAFs by upregulating matrix metalloproteinase-9 (MMP-9) and phosphorylation of mothers against decapentaplegic homolog 2/Mothers against decapentaplegic homolog 3 (Smad2/3). ConclusionExosomes from BAP31-overexpressing/BAP31-knockdown CRCs are found to manipulate the transition of fibroblasts into proangiogenic CAFs by the miR-181a-5p/RECK axis.

PurposeTo establish a fast and accurate detection method for interspecies contaminations in the patient-derived xenograft (PDX) models and cell lines, and to elucidate possible mechanisms if interspecies oncogenic transformation is detected.MethodsA fast and highly sensitive intronic qPCR method detecting Gapdh intronic genomic copies was developed to quantify if cells were human or murine or a mixture. By this method, we documented that murine stromal cells were abundant in the PDXs; we also authenticated our cell lines to be human or murine.ResultsIn one mouse model, GA0825-PDX transformed murine stromal cells into a malignant tumorigenic murine P0825 cell line. We traced the timeline of this transformation and discovered three subpopulations descended from the same GA0825-PDX model: epithelium-like human H0825, fibroblast-like murine M0825, and main passaged murine P0825 displayed differences in tumorigenic capability in vivo. P0825 was the most aggressive and H0825 was weakly tumorigenic. Immunofluorescence (IF) staining revealed that P0825 cells highly expressed several oncogenic and cancer stem cell markers. Whole exosome sequencing (WES) analysis revealed that TP53 mutation in the human ascites IP116-generated GA0825-PDX may have played a role in the human-to-murine oncogenic transformation.ConclusionThis intronic qPCR is able to quantify human/mouse genomic copies with high sensitivity and within a time frame of a few hours. We are the first to use intronic genomic qPCR for authentication and quantification of biosamples. Human ascites transformed murine stroma into malignancy in a PDX model.

ObjectiveBy identifying the clinicopathological characteristics and prognostic influences of patients with triple-positive breast cancer (TPBC) at Xijing Hospital in China compared with those in the United States, this study aims to construct a nomogram model to forecast the overall survival rate (OS) of TPBC patients.MethodThe Surveillance, Epidemiology, and End Results (SEER) database was used to screen 5769 patients as the training cohort, and 191 patients from Xijing Hospital were used as the validation cohort. Cox risk-proportional model was applied to select variables and the nomogram model was constructed based on the training cohort. The performance of the model was evaluated by calculating the C-index and generating calibration plots in the training and validation cohorts.ResultsCox multifactorial analysis showed that age, chemotherapy, radiotherapy, M-stage, T-stage, N-stage, and the mode of surgery were all independent risk factors for the prognosis of TPBC patients (all P

BackgroundAngiogenesis is a major promotor of tumor progression and metastasis in gastric adenocarcinoma (STAD). We aimed to develop a novel lncRNA gene signature by identifying angiogenesis-related genes to better predict prognosis in STAD patients.MethodsThe expression profiles of angiogenesis-related mRNA and lncRNA genes were collected from The Cancer Genome Atlas (TCGA). Then, the “limma” package was used to identify differentially expressed genes (DEGs). The expression profiles of angiogenesis-related genes were clustered by consumusclusterplus. The Pearson correlation coefficient was further used to identify lncRNAs coexpressed with angiogenesis-related clustere genes. We used Lasso Cox regression analysis to construct the angiogenesis-related lncRNAs signature. Furthermore, the diagnostic accuracy of the prognostic risk signature were validated by the TCGA training set, internal test sets and external test set. We used multifactor Cox analysis to determine that the risk score is an independent prognostic factor different from clinical characteristics. Nomogram has been used to quantitatively determine personal risk in a clinical environment. The ssGSEA method or GSE176307 data were used to evaluate the infiltration state of immune cells or predictive ability for the benefit of immunotherapy by angiogenesis-related lncRNAs signature. Finally, the expression and function of these signature genes were explored by RT–PCR and colony formation assays.ResultsAmong angiogenesis-related genes clusters, the stable number of clusters was 2. A total of 289 DEGs were identified and 116 lncRNAs were screened to have a significant coexpression relationship with angiogenic DEGs (P value0.5). A six-gene signature comprising LINC01579, LINC01094, RP11.497E19.1, AC093850.2, RP11.613D13.8, and RP11.384P7.7 was constructed by Lasso Cox regression analysis. The multifactor Cox analysis and Nomogram results showed that our angiogenesis-related lncRNAs signature has good predictive ability for some different clinical factors. For immune, angiogenesis-related lncRNAs signature had the ability to efficiently predict infiltration state of 23 immune cells and immunotherapy. The qPCR analysis showed that the expression levels of the six lncRNA signature genes were all higher in gastric adenocarcinoma tissues than in adjacent tissues. The functional experiment results indicated that downregulation of the expression of these six lncRNA signature genes suppressed the proliferation of ASG and MKN45 cells.ConclusionSix angiogenesis-related genes were identified and integrated into a novel risk signature that can effectively assess prognosis and provide potential therapeutic targets for STAD patients.

ObjectiveThis study aimed to analyze and visualize the current research focus, research frontiers, evolutionary processes, and trends of artificial intelligence (AI) in the field of gastric cancer using a bibliometric analysis.MethodsThe Web of Science Core Collection database was selected as the data source for this study to retrieve and obtain articles and reviews related to AI in gastric cancer. All the information extracted from the articles was imported to CiteSpace to conduct the bibliometric and knowledge map analysis, allowing us to clearly visualize the research hotspots and trends in this field.ResultsA total of 183 articles published between 2017 and 2022 were included, contributed by 201 authors from 33 countries/regions. Among them, China (47.54%), Japan (21.86%), and the USA (13.11%) have made outstanding contributions in this field, accounting fsor 82.51% of the total publications. The primary research institutions were Wuhan University, Tokyo University, and Tada Tomohiro Inst Gastroenterol and Proctol. Tada (n = 12) and Hirasawa (n = 90) were ranked first in the top 10 authors and co-cited authors, respectively. Gastrointestinal Endoscopy (21 publications; IF 2022, 9.189; Q1) was the most published journal, while Gastric Cancer (133 citations; IF 2022, 8.171; Q1) was the most co-cited journal. Nevertheless, the cooperation between different countries and institutions should be further strengthened. The most common keywords were AI, gastric cancer, and convolutional neural network. The “deep-learning algorithm” started to burst in 2020 and continues till now, which indicated that this research topic has attracted continuous attention in recent years and would be the trend of research on AI application in GC.ConclusionsResearch related to AI in gastric cancer is increasing exponentially. Current research hotspots focus on the application of AI in gastric cancer, represented by convolutional neural networks and deep learning, in diagnosis and differential diagnosis and staging. Considering the great potential and clinical application prospects, the related area of AI applications in gastric cancer will remain a research hotspot in the future.

Colorectal cancer (CRC) is among the most commonly diagnosed gastrointestinal malignancies worldwide. It is inadequate to handle in terms of staging and restaging only based on morphological imaging modalities and serum surrogate markers. And the correct and timely staging of CRC is imperative to prognosis and management. When compared to established sequential, multimodal conventional diagnostic methods, the molecular and functional imaging 18F-FDG PET/CT shows superiorities for tailoring appropriate treatment maneuvers to each patient. This review aims to summarize the utilities of 18F-FDG PET/CT in CRC, focusing on primary staging, follow-up assessment of tumor responses and diagnostic of recurrence. In addition, we also summarize the technical considerations of PET/CT and the conventional imaging modalities in those patients who are either newly diagnosed with CRC or has already been treated from this cancer.

BackgroundsLung adenocarcinoma (LUAD) is the most common subtype of lung cancer, which is the leading cause of cancer death. Dysregulation of cell proliferation and death plays a crucial role in the development of LUAD. As of recently, the role of a new form of cell death, cuproptosis, and it has attracted more and more attention. As of yet, it is not clear whether cuproptosis is involved in the progression of LUAD.MethodsAn integrated set of bioinformatics tools was utilized to analyze the expression and prognostic significance of cuproptosis-related genes. Meanwhile, a robust risk signature was developed using machine learning based on prognostic cuproptosis-related genes and explored the value of prognostic cuproptosis-related signature for clinical applications, functional enrichment and immune landscape. Lastly, the dysregulation of the cuproptosis-related genes in LUAD was validated by in vitro experiment.ResultsIn this study, first, cuproptosis-related genes were found to be differentially expressed in LUAD patients of public databases, and nine of them had prognostic value. Next, a cuproptosis-related model with five features (DLTA, MTF1, GLS, PDHB and PDHA1) was constructed to separate the patients into high- and low-risk groups based on median risk score. Internal validation set and external validation set were used for model validation and evaluation. What’s more, Enrichment analysis of differential genes and the WGCNA identified that cuproptosis-related signatures affected tumor prognosis by influencing tumor immunity. Small molecule compounds were predicted based on differential expressed genes to improve poor prognosis in the high-risk group and a nomogram was constructed to further advance clinical applications. In closing, our data showed that FDX1 affected the prognosis of lung cancer by altering the expression of cuproptosis-related signature.ConclusionA new cuproptosis-related signature for survival prediction was constructed and validated by machine learning algorithm and in vitro experiments to reflect tumor immune infiltration in LUAD patients. The purpose of this article was to provide a potential diagnostic and therapeutic strategy for LUAD.