Journal of gastrointestinal oncology最新文献

Peritoneal metastases synchronously occurring in the patient with primary colon cancer causes that patient to be at high risk for subsequent disease progression within the abdomen and pelvis. If peritoneal metastases are preoperatively diagnosed, patients are likely to be treated with neoadjuvant chemotherapy with or without biological therapy prior to cytoreductive surgery (CRS). However, if one only considers patients with peritoneal metastases unexpectedly identified at the time of primary colon cancer resection, the optimal management strategy is neither standardized nor evidence based. These authors present an opinion regarding treatment options in unexpectedly (incidentally) detected peritoneal metastases. The primary colon cancer may be asymptomatic (elective list) or may present as an emergency with obstruction or with perforation. The fitness of the patient, the condition of the colon, availability of a colonic stent, consent of the patient and capabilities of the institution for management of peritoneal metastases by CRS and intraperitoneal chemotherapy cannot be ignored and must all be considered. These patients with known peritoneal metastases should not be allowed to return for further treatment with advanced disease after multiple regimens of systemic chemotherapy. Delay in definitive management will cause peritoneal metastases to be unresectable and not amenable to cure. It is time to debate optimal management strategies for unexpectedly detected peritoneal metastases. The authors find the data compelling that the modifications presented in the management of unexpected peritoneal metastases documented at the time of colon cancer resection changes a palliative approach to treatment to a plan that has curative intent.

Background: The preoperative evaluation of the expression levels of Ki-67, p53, and epidermal growth factor receptor (EGFR) based on magnetic resonance imaging (MRI) of rectal cancer is necessary to facilitate individualized therapy. This study aimed to develop and validate radiomics models for the evaluation of the expression levels of Ki-67, p53, and EGFR of rectal cancer from preoperative MRI.

Methods: In this retrospective study, 124 patients (38 in the test group and 86 in the training group) with rectal cancer who underwent preoperative MRI and postoperative Ki-67, p53 and EGFR assay were included in Longyan First Affiliated Hospital of Fujian Medical University from June 2015 to October 2019. A total of 796 radiomics features were acquired from both diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI). Least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR) were used to select the most predictive texture features, and then the radiomics score (Rad-score) models were derived to evaluate Ki-67, p53, and EGFR expression status based on the radiomics signature. The receiver operating characteristic (ROC) was used to assess the model's performance, and the reliability was verified via accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results: The Rad-score evaluation of Ki-67 expression status yielded area under the curve (AUC) values of 0.91 [95% confidence interval (CI): 0.87-0.95] and 0.81 (95% CI: 0.66-0.96) in the training and test groups. The evaluation of p53 expression produced AUC values of 0.82 (95% CI: 0.77-0.88) and 0.80 (95% CI: 0.65-0.96). For evaluating EGFR expression status in both training and test groups, the AUC values were 0.86 (95% CI: 0.81-0.91) and 0.76 (95% CI: 0.58-0.93), respectively. While Rad-score of Ki-67 expression status in the training group obtained the top accuracy, sensitivity, specificity, and PPV with values of 0.85, 0.80, 0.92, and 0.93.

Conclusions: Preoperative MRI-based radiomics analysis has the ability to noninvasively assess the postoperative Ki-67, p53, and EGFR of rectal cancer.

Background: Hepatocellular carcinoma (HCC) is currently one of the most common malignant tumors with the highest mortality rates in the world. Most patients with HCC have lost the opportunity for surgery at the time of initial diagnosis. This study aims to introduce a new conversion strategy: trans-arterial chemo-emobilization (TACE) combined with laparoscopic portal vein ligation (PVL) and terminal branches portal vein embolization (PVE).

Methods: From November 2018 to February 2023, patients with HCC and insufficient future liver remnant (FLR) were included for this novel treatment strategy. At first, TACE was performed. Then, these patients underwent laparoscopic PVL and terminal branches PVE. After hypertrophy of FLR, these patients underwent the second stage of liver resection. All patients were followed up regularly postoperatively.

Results: A total of 13 patients with HCC were included. All patients underwent the TACE and the first stage of laparoscopic PVL and terminal branches PVE. After a mean of 28.7 days after the first stage of operation, the FLR increased by a mean of 183.4 cm³, equivalent to 49%. All patients underwent the second stage of liver resection. There was no surgical mortality. The mean postoperative hospital stay was 9.1 days. The median survival was 24.5 months.

Conclusions: The treatment strategy of preoperative TACE combined with laparoscopic PVL and terminal branches PVE and second stage of liver resection is a preliminarily feasible and relatively safe new strategy which deserves further exploration in the future.

Background: The tumor microenvironment (TME) could be critical in carcinogenesis, immune evasion, and treatment response. TME-related genes are limited in their ability to predict gastric cancer (GC) outcomes. We utilized data from The Cancer Genome Atlas (TCGA) to investigate the functional roles of TME-related genes in GC.

Methods: We acquired single-cell data, bulk sequencing data, and clinical characteristics of GC patients from the TCGA database. The CD8T cell genes associated with the TME were selected for bioinformatic analysis in GC. Tumor classification of GC was established through consistent cluster analysis. We then evaluated the prognosis and immune cell infiltration in connection with a CD8T cell-related model for GC.

Results: The single-cell messenger RNA (mRNA) sequencing (scRNA-Seq) dataset of GSE134520 was utilized to investigate the pathogenesis and disease-specific cell types in GC. Interestingly, compared to healthy tissue, the proportions of CD8Tex cells, malignant cells, and gland mucous increased in GC, whereas the proportion of pit mucous decreased in GC. Since CD8Tex cells may play a vital role in pancreatic adenocarcinoma (PAAD), based on the 612 differentially expressed genes (DEGs) involved in CD8Tex cells, TCGA-GC patients were stratified into low- and high-risk groups. The downregulated DEGs in the low-risk G1 group were associated with proteoglycans in cancer, the cGMP-PKG signaling pathway, focal adhesion, and cell adhesion molecules (CAMs), whereas the upregulated DEGs were associated with viral protein interaction with cytokine and cytokine receptors, the tumor necrosis factor (TNF) signaling pathway, the interleukin (IL)-17 signaling pathway, and the chemokine signaling pathway. Combined with univariate Cox analysis, we ultimately identified 23 CD8T cell-related prognostic genes: TCIM, AADAC, SLC2A3, ZNF331, TSC22D3, CMTM3, ZFP36, VIM, CLDND1, GABARAPL1, SOCS3, RGS1, TCEAL9, RGS2, CD59, SPRY1, EMP3, ZEB2, PDE4B, GLIPR1, ERRFI1, and LBH. Using the Cox regression model to prioritize the 23 CD8T cell-related genes, we finally selected 7 genes: CXCR4, AADAC, SLC2A3, CMTM3, RGS2, CD59, and ZEB2.

Conclusions: CD8T cell-related genes have a strong association with tumor classification and immune response in GC patients. A CD8T cell-related signature demonstrated robust prognostic predictive performance for GC. Our findings may reveal novel insights into the diagnosis and treatment of GC.

Background: Large hepatocellular carcinoma (HCC) with a diameter ≥5 cm remains a significant challenge of poor survival and raises the need for prognosis evaluation. This study aimed to develop and validate a nomogram-based prognostic stratification to assess overall survival (OS) of patients with large HCC.

Methods: Data of patients with large HCC were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database and our hospital, and were divided into the training cohort, internal validation cohort and external validation cohort. Cox analysis was performed to identify independent prognostic factors for the construction of nomogram in training cohort. The predictive ability of the nomogram was validated compared with the tumor node metastasis (TNM) classification staging system. Furthermore, prognostic stratification system based on nomogram was developed.

Results: Independent prognostic factors including histological grade, T stage, M stage, alpha fetoprotein (AFP), fibrosis score and surgery, were incorporated to construct nomogram. C-indexes of nomogram were 0.730, 0.726 and 0.724 in the training, internal and external validation cohorts, respectively. Importantly, nomogram harbored a superior discrimination and clinical benefit than the TNM staging system. Nomogram-based prognostic stratification divided patients into three groups: 345-414 (low-risk group), 415-460 (medium-risk group) and 461-513 (high-risk group). As shown in Kaplan-Meier curves, there were significant differences in OS among low-, medium- and high-risk groups (P<0.01).

Conclusions: Nomogram showed a superior prognostic predictive ability compared with the TNM staging system. The prognostic stratification serves as a valuable tool to assist clinicians on the selection of optimal treatment method and follow-up plan, particularly for the high-risk population.

Background: Pancreatic adenocarcinoma (PAAD) is a highly aggressive malignant tumor with a poor prognosis. Integrin subunit genes (ITGs) serve as biomarkers for various types of cancers; however, to date, no prognostic research has been conducted on the ITGs in PAAD. This study aims to fill this gap by investigating the role of ITGs in PAAD prognosis.

Methods: RNA-sequencing data, clinicopathological features, and survival information from The Cancer Genome Atlas (TCGA) database were sourced via GTEx. The GSE62452 data set was acquired from the Gene Expression Omnibus (GEO) database. A single-sample gene set enrichment analysis (ssGSEA) was first conducted to classify the PAAD samples from TCGA and GEO data sets with different ITG scores. A differential analysis was employed to identify the differentially expressed genes (DEGs) between the normal and PAAD samples, and between the high and low ITG score groups in both TCGA and GEO data sets.

Results: A total of 22 key differentially expressed ITGs (KDE-ITGs) were identified and enriched in eight Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) signaling pathway, focal adhesion, and the extracellular matrix (ECM)-receptor interaction. A prognostic model comprising the eight KDE-ITGs was established. Additionally, 2,371 DEGs were found between the high- and low-risk groups, which were mainly enriched in the Gene Ontology (GO) terms of cell morphogenesis and cytokine production, and KEGG pathways such as necroptosis, lysosome, and ferroptosis. Further, the proportions of T cells and cluster of differentiation 8 (CD8) T cells, and the expression levels of immune checkpoints, such as cluster of differentiation 274 (CD274) and lymphocyte activating gene 3 (LAG3), differed significantly between the two risk groups.

Conclusions: The eight identified KDE-ITGs in PAAD were used to establish a new prognosis model, which might have clinical application, especially in immunotherapy.

Background: Previous studies found it difficult to differentiate hepatoid adenocarcinoma of the stomach (HAS) from conventional gastric adenocarcinoma (CGA). We aimed to assess the efficacy of a computed tomography (CT)-based radiomics nomogram in identifying HAS.

Methods: Portal phase CT images were collected from 59 patients with HAS and 122 patients with CGA. HAS and CGA were differentiated through univariate analysis of clinical characteristics, serum biochemical biomarkers, and CT features. The construction of the radiomics signature involved the application of the least absolute shrinkage and selection operator (LASSO) regression model. Multivariable logistic regression analysis was employed to establish the CT-based radiomics nomogram.

Results: The separation of HAS patients from CGA patients relied on the serum alpha-fetoprotein (AFP) level and radiomics signature. The area under the curve (AUC) of AFP was 0.726 [95% confidence interval (CI): 0.639-0.801] in the training cohort and 0.681 (95% CI: 0.541-0.800) in the test cohort, whereas the radiomic signature demonstrated a significantly higher AUC of 0.949 (95% CI: 0.895-0.980) in the training cohort and 0.868 (95% CI: 0.749-0.944) in the test cohort. The nomogram model yielded excellent accuracy for identifying HAS, achieving an AUC of 0.970 (95% CI: 0.923-0.992) in the training cohort and 0.905 (95% CI: 0.796-0.968) in the test cohort.

Conclusions: Radiomics analysis offers promise for differentiating HAS from CGA, and the CT-based radiomics nomogram is likely to have significant clinical implications on HAS distinction.