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BackgroundImmune checkpoint inhibitors (ICIs) have shown promising prospects in locally advanced, resectable gastric or gastroesophageal junction adenocarcinoma (GC/GEJC) immunotherapy, but their efficacy in neoadjuvant settings remains unclear. This study aimed to assess the efficacy and safety of integrating programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) inhibitors into neoadjuvant chemotherapy (NACT) of GC/GEJC treatment.MethodsPubMed, Cochrane Library, Embase, ClinicalTrials.gov, and main oncology conference databases were systematically searched up to 19 November 2022, and randomized controlled trials (RCTs) and cohort studies that evaluated the efficacy and safety of PD-1/PD-L1 inhibitors plus NACT were included. The main outcomes were pathological complete response (pCR), major pathological response (MPR), R0 resection rate, and treatment-related adverse events (TRAEs).ResultsA total of 753 patients from 20 prospective studies were included in this meta-analysis. The pooled pCR and MPR rates from studies reporting were 21.7% [95% confidence interval (CI), 18.1%–25.5%] and 44.0% (95% CI, 34.1%–53.8%), respectively. The pooled incidence rate of total TRAEs was 89.1% (95% CI, 82.7%–94.3%), and the incidence rate of grade 3 to 4 TRAEs was 34.4% (95% CI, 17.8%–66.5%). The pooled R0 resection rate was reported to be 98.9% (95% CI, 97.0%–99.9%). Subgroup analysis has not found significant differences in efficacy and safety among different PD-1/PD-L1 inhibitors. Moreover, the efficacy in patients with positive PD-L1 expression (combined positive score ≥1) was comparable with that in the entire study population [pCR, 22.5% vs. 21.2% (p > 0.05); MPR, 48.6% vs. 43.7% (p > 0.05)].ConclusionThis systematic review and meta-analysis found that PD-1/PD-L1 inhibitors combined with NACT for locally advanced GC/GEJC were well tolerated and may confer therapeutic advantages. The integration of ICIs into NACT has shown the potential for application in any PD-L1 expression population.

BackgroundMena, a cytoskeletal regulatory protein, is involved in actin-based regulation of cell motility and adhesion, and contributes to tumor invasion and metastasis. However, the role of Mena in oral squamous cell carcinoma remains unclear. This is the first research focusing on the prognostic value of Mena in OSCC. In this study, we aimed to investigate the correlation between Mena expression and clinicopathological significance, as well as prognostic value in OSCC.MethodsMena gene expression profiles of OSCC and normal tissues were collected from Oncomine, TCGA, and GEO databases. Biological function was analyzed through GO, KEGG and GSEA enrichment. Further, the expression level of Mena and tumor-related markers in 151 OSCC specimens was examined by IHC staining based on tissue microarray. Kaplan-Meier analysis was used to assess the prognostic performance of Mena in OSCC.ResultMena was generally upregulation in various malignancies, especially OSCC. The functional analyses indicated that Mena was involved in the assembly and regulation of actin, cell movement, and EMT. IHC staining revealed that high expression of Mena in OSCC was correlated with Lymphatic metastasis, TNM stage, E-cadherin, Vimentin, and MMP-2, but insignificantly Ki67. Kaplan-Meier analysis demonstrated that elevated expression of Mena was significantly associated with poor overall survival and disease-free survival of OSCC patients.ConclusionMena could be a novel biomarker for predicting the prognosis of OSCC patients, which supports a theoretical basis for developing molecular target therapy.

BackgroundCircular RNAs (circRNAs) regulate multiple malignant behaviors of various types of cancer. The role of circDNMT1, a newly identified circRNA, remains unknown in gastric cancer (GC). This study aimed to elucidate the underlying mechanisms of circDNMT1 in regulating GC progression.MethodsmicroRNA (miRNA) and circRNA expression was detected by quantitative real-time PCR. Western blotting was performed to measure hypoxia inducible factor-1 alpha (HIF-1α) protein expression. Sanger sequencing, gel electrophoresis and fluorescence in situ hybridization were performed to identify the presence of circDNMT1. The clinicopathological features and overall survival of patients were analyzed based on circDNMT1 expression. The proliferation, migration and invasion of GC cells were determined by cell counting kit-8, 5-ethynyl-2’-deoxyuridine, wound healing and transwell assays. Glycolysis of GC cells was detected based on the levels of glucose uptake, the lactate acid, ATP and pyruvic acid production and the extracellular acidification and oxygen consumption rates. The binding sites between miR-576-3p and circDNMT1 or HIF-1α were predicted by online bioinformatic tools and were validated using RNA pull-down and luciferase reporter assays. Xenograft models were established to determine the effects of the circDNMT1/miR-576-3p/HIF-1α axis on GC growth and metastasis in vivo.ResultscircDNMT1 was successfully identified and shown to be overexpressed in GC tissues and cell lines. The expression levels of circDNMT1 were correlated with pathological T stage, pathological TNM stage and shorter survival time of GC patients. circDNMT1 knockdown inhibited the proliferation, migration, invasion and glycolysis of GC cells. circDNMT1 functioned as an oncogenic factor by sponging miR-576-3p. HIF-1α was negatively regulated by miR-576-3p via binding its mRNA 3’ untranslated region. circDNMT1 promoted malignant behaviors and metabolic reprogramming of GC by targeting the miR-576-3p/HIF-1α axis both in vitro and in vivo.ConclusionThese findings demonstrated that circDNMT1 knockdown inhibited GC proliferation, migration, invasion and glycolysis through sponging miR-576-3p/HIF-1α axis. circDNMT1 may be a novel target for GC treatment.

Background and AimsPrediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer is critical for surgical planning and evaluation of NAC efficacy. The purpose of this project was to assess the efficiency of a novel nomogram based on ultrasound and clinicopathological features for predicting pCR after NAC.MethodsThis retrospective study included 282 patients with advanced breast cancer treated with NAC from two centers. Patients received breast ultrasound before NAC and after two cycles of NAC; and the ultrasound, clinicopathological features and feature changes after two cycles of NAC were recorded. A multivariate logistic regression model was combined with bootstrapping screened for informative features associated with pCR. Then, we constructed two nomograms: an initial-baseline nomogram and a two-cycle response nomogram. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) were analyzed. The C-index was used to evaluate predictive accuracy.ResultsSixty (60/282, 21.28%) patients achieved pCR. Triple-negative breast cancer (TNBC) and HER2-amplified types were more likely to obtain pCR. Size shrinkage, posterior acoustic pattern, and elasticity score were identified as independent factors by multivariate logistic regression. In the validation cohort, the two-cycle response nomogram showed better discrimination than the initial-baseline nomogram, with the C-index reaching 0.79. The sensitivity, specificity, and NPV of the two-cycle response nomogram were 0.77, 0.77, and 0.92, respectively.ConclusionThe two-cycle response nomogram exhibited satisfactory efficiency, which means that the nomogram was a reliable method to predict pCR after NAC. Size shrinkage after two cycles of NAC was an important in dependent factor in predicting pCR.

IntroductionThe axillary lymph node (ALN) status of breast cancer patients is an important prognostic indicator. The use of primary breast mass features for the prediction of ALN status is rare. Two nomograms based on preoperative ultrasound (US) images of breast tumors and ALNs were developed for the prediction of ALN status.MethodsA total of 743 breast cancer cases collected from 2016 to 2019 at the Second Affiliated Hospital of Harbin Medical University were randomly divided into a training set (n = 523) and a test set (n = 220). A primary tumor feature model (PTFM) and ALN feature model (ALNFM) were separately generated based on tumor features alone, and a combination of features was used for the prediction of ALN status. Logistic regression analysis was used to construct the nomograms. A receiver operating characteristic curve was plotted to obtain the area under the curve (AUC) to evaluate accuracy, and bias-corrected AUC values and calibration curves were obtained by bootstrap resampling for internal and external verification. Decision curve analysis was applied to assess the clinical utility of the models.ResultsThe AUCs of the PTFM were 0.69 and 0.67 for the training and test sets, respectively, and the bias-corrected AUCs of the PTFM were 0.67 and 0.67, respectively. Moreover, the AUCs of the ALNFM were 0.86 and 0.84, respectively, and the bias-corrected AUCs were 0.85 and 0.81, respectively. Compared with the PTFM, the ALNFM showed significantly improved prediction accuracy (p < 0.001). Both the calibration and decision curves of the ALNFM nomogram indicated greater accuracy and clinical practicality. When the US tumor size was ≤21.5 mm, the Spe was 0.96 and 0.92 in the training and test sets, respectively. When the US tumor size was greater than 21.5 mm, the Sen was 0.85 in the training set and 0.87 in the test set. Our further research showed that when the US tumor size was larger than 35 mm, the Sen was 0.90 in the training set and 0.93 in the test set.ConclusionThe ALNFM could effectively predict ALN status based on US images especially for different US tumor size.