The protein encoded by CD3D is part of the T-cell receptor/CD3 complex (TCR/CD3 complex) and is involved in T-cell development and signal transduction. Previous studies have shown that CD3D is associated with prognosis and treatment response in breast, colorectal, and liver cancer. However, the expression and clinical significance of CD3D in gastric cancer are not clear. In this study, we collected 488 gastric cancer tissues and 430 paired adjacent tissues to perform tissue microarrays (TMAs). Then, immunohistochemical staining of CD3D, CD3, CD4, CD8 and PD-L1 was conducted to investigate the expression of CD3D in gastric cancer and the correlation between the expression of CD3D and tumor infiltrating lymphocytes (TILs) and PD-L1. The results showed that CD3D was highly expressed in gastric cancer tissues compared with paracancerous tissues (P<0.000). Univariate and multivariate analyses showed that CD3D was an independent good prognostic factor for gastric cancer (P=0.004, HR=0.677, 95%CI: 0.510-0.898 for univariate analyses; P=0.046, HR=0.687, 95%CI: 0.474-0.994 for multivariate analyses). In addition, CD3D was negatively correlated with the tumor location, Borrmann type and distant metastasis (P=0.012 for tumor location; P=0.007 for Borrmann type; P=0.027 for distant metastasis). In addition, the expression of CD3D was highly positively correlated with the expression of CD3, CD4, CD8, and PD-L1, and the combination of CD3D with CD3, CD4, CD8 and PD-L1 predicted the best prognosis (P=0.043). In summary, CD3D may play an important regulatory role in the tumor immune microenvironment of gastric cancer and may serve as a potential indicator of prognosis and immunotherapy response.
Background: Neoadjuvant chemotherapy (NACT) and radical gastrectomy are the gold standard treatments for resectable advanced gastric cancer (GC). However, the prognostic value of the pathological tumor regression grade (TRG) of NACT remains controversial. This retrospective study aimed to investigate the correlation between the TRG after NACT and clinicopathological features as well as its prognostic value in advanced GC. Methods: In total, 551 patients with GC who received NACT combined with surgical resection at the Zhejiang Cancer Hospital from April 2004 to December 2019 were included. The demographic characteristics, treatment response, tumor characteristics, treatment regimens, and survival data were reviewed from the medical records of all patients. The Chi-square test was used to analyze the correlation between TRG and clinicopathological factors. Kaplan-Meier univariate analysis and Cox regression multivariate analysis were used to determine the independent risk factors affecting the prognosis of GC patients.Results: Among the 551 patients with advanced GC who accepted NACT treatment, 14 were determined to be in TRG 0, 98 in TRG 1, 257 in TRG 2, and 182 in TRG 3. Also, TRG was significantly correlated with the cT stage (P=0.015), ypT stage (P<0.001), ypN stage (P<0.001), ypTNM stage (P<0.001), vascular tumor thrombus (P<0.001), Borrmann classification (P=0.042), and lymph node ratio (LNR) (P<0.001). Furthermore, patients who had a good pathological response to NACT had a better prognosis, with a 3-year overall survival (OS) of 70.9% versus 48.8% in patients who had a poor pathological response. We also found that TRG (P=0.042, HR =1.65) was an independent prognostic factor affecting the OS of GC patients.Conclusions: TRG plays a significant role in the prognostic value in neoadjuvant chemotherapy for gastric adenocarcinoma. Patients with higher cT stage, higher levels of pre-CA199 and pre-CA125 may have worse pathological response.
Background: Neoadjuvant chemotherapy (NCT) was developed to improve the prognosis of patients with advanced gastric cancer (AGC). Some studies have confirmed the diagnostic and prognostic value of various serum tumor markers in gastric cancer. However, most of these studies were focused on the value of preoperative and postoperative tumor markers in patients undergoing surgery with or without adjuvant therapy, and only a few studies focused on AGC patients undergoing NCT. Methods:We retrospectively analyzed the data of consecutive patients with histologically confirmed AGC who received NCT prior to surgical resection at Zhejiang Cancer Hospital from January 2010 to September 2018. The prognostic impact of tumor markers before and after NCT, including Carcinoembryonic antigen (CEA), Carbohydrate antigen199 (CA199), Carbohydrate antigen125 (CA125), Alpha-FetoProtein (AFP), Carbohydrate antigen242 (CA242), and Carbohydrate antigen724 (CA724), were evaluated using Kaplan-Meier log-rank survival analysis. The association between tumor marker normalization during preoperative chemotherapy and clinicopathological characteristics was also investigated.Results: Four hundred and seventy-two patients were included in the study. The levels of CEA, CA199, CA125, CA242, and CA724 before NCT could predict prognosis, and the levels of CA199, CA125, CA242, and CA724 after NCT were correlated with prognosis. The overall survival (OS) rate decreased with an increasing number of positive tumor markers before and after preoperative chemotherapy. Tumor marker abnormalization after NCT was not related to chemotherapy, whereas patients with tumor marker normalization after NCT obtained survival benefits.Conclusions: Tumor markers before and after NCT, such as CA199, CA125, CA242, and CA724, have a discriminatory ability for patients with GC. The normalization of tumor markers after NCT was associated with better survival.
Background: Early noninvasive screening of patients who would benefit from neoadjuvant chemotherapy (NCT) is essential for personalized treatment of locally advanced gastric cancer (LAGC). The aim of this study was to identify radio-clinical signatures from pretreatment oversampled computed tomography (CT) images to predict the response to NCT and prognosis of LAGC patients. Methods: LAGC patients were retrospectively recruited from six hospitals from January 2008 to December 2021. An SE-ResNet50-based chemotherapy response prediction system was developed from pretreatment CT images preprocessed with an imaging oversampling method (i.e. DeepSMOTE). Then, the deep learning (DL) signature and clinic-based features were fed into the deep learning radio-clinical signature (DLCS). The predictive performance of the model was evaluated based on discrimination, calibration, and clinical usefulness. An additional model was built to predict overall survival (OS) and explore the survival benefit of the proposed DL signature and clinicopathological characteristics. Results: A total of 1060 LAGC patients were recruited from six hospitals; the training cohort (TC) and internal validation cohort (IVC) patients were randomly selected from center I. An external validation cohort (EVC) of 265 patients from five other centers was also included. The DLCS exhibited excellent performance in predicting the response to NCT in the IVC [area under the curve (AUC), 0.86] and EVC (AUC, 0.82), with good calibration in all cohorts ( P >0.05). Moreover, the DLCS model outperformed the clinical model ( P <0.05). Additionally, we found that the DL signature could serve as an independent factor for prognosis [hazard ratio (HR), 0.828, P =0.004]. The concordance index (C-index), integrated area under the time-dependent ROC curve (iAUC), and integrated Brier score (IBS) for the OS model were 0.64, 1.24, and 0.71 in the test set. Conclusion: The authors proposed a DLCS model that combined imaging features with clinical risk factors to accurately predict tumor response and identify the risk of OS in LAGC patients prior to NCT, which can then be used to guide personalized treatment plans with the help of computerized tumor-level characterization.
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