Journal of gastrointestinal oncology最新文献

Background: The Cancer Genome Atlas (TCGA) has identified four distinct molecular subtypes of gastric cancer (GC) with prognostic significance: Epstein-Barr virus (EBV)-positive, microsatellite instability (MSI)-high, genomically stable (GS), and chromosomal instability (CIN). Unfortunately, the complex analysis required for TCGA classification limits its practical use in clinical settings. Our study sought to devise a next-generation sequencing (NGS)-based method to classify GC more efficiently, serving as a promising biomarker for prognosis and immunotherapy efficacy.

Methods: This study was a retrospective observation study, and we employed 2 independent GC cohorts. The 3DMed cohort (n=765), comprising data on 733 cancer-related genes along with 4 EBV-encoded genes, was utilized to develop the new NGS classification. Additionally, the secondary Korean cohort (n=55), which includes both genomic data and information on immune checkpoint inhibitor (ICI) treatment, was employed to establish a correlation between NGS subtypes and ICI responsiveness.

Results: In the 3DMed cohort, we identified 5.2% EBV, 4.6% MSI, 30.6% GS, and 59.6% CIN subtypes. The MSI subtype exhibited the highest number of mutation events, along with the highest tumor mutational burden (TMB) and strong programmed cell death ligand 1 (PD-L1) expression. CIN tumors showed extensive copy number variations (CNVs) and genomic heterogeneity. The EBV subtype presented recurrent ARID1A and PIK3CA mutations and fewer TP53 mutations. GS tumors exhibited specific mutations in CDH1 and ARID1A. In the Korean cohort, ICIs were most effective in MSI and EBV cases, showing disease control rates of 100%, compared to 62.9% in GS and 12.5% in CIN subtypes.

Conclusions: The NGS method successfully maps the mutational landscape of GC, providing a practical TCGA classification surrogate to optimize patient-specific treatment strategies.

Background: Gemcitabine (GEM) and cisplatin (CDDP) combination therapy (GC therapy) is the standard 1st-line regimen for incurable biliary tract cancers (BTCs). However, the correlation between dynamic changes in renal function and the outcomes of GC therapy remains unclear. This study aimed to clarify the association between renal function alterations and treatment outcomes after GC therapy.

Methods: We retrospectively examined 44 patients with incurable BTC who underwent GC therapy (January 2015 to December 2022). The patients were stratified according to their baseline estimated glomerular filtration rate (eGFR). Changes in eGFR, overall survival (OS), and progression-free survival (PFS).

Results: The median baseline eGFRs were 65.0 mL/min/1.73 m² (low group, n=22) and 90.7 mL/min/1.73 m² (high group, n=22). No significant background differences were observed between the groups. During the 1st course, 86.4% and 54.5% of patients in the low and high groups underwent dose adjustments and/or administration postponement, which was found to be significantly greater in the low group. In the high group, eGFR decreased with an increase in the CDDP dose (100 mg =-12.0, 200 mg =-12.7, 300 mg =-25.9, and 400 mg =-25.7 mL/min/1.73 m²). In the low group, eGFR remained stable (100 mg =0.8, 200 mg =7.5, 300 mg =4.5, and 400 mg =-0.3 mL/min/1.73 m²). The decrease in the eGFR in the high group was significantly greater at each CDDP dose. However, the median OS and PFS were longer in the low group (OS: 16.3 vs. 9.2 months, P=0.02; PFS: 5.4 vs. 3.6 months, P=0.02). No significant differences in adverse events were observed between the groups.

Conclusions: Adjusting GC therapy based on baseline estimated glomerular eGFR may be pivotal for therapeutic benefits and renal function protection in patients with incurable BTC.

Background: Colorectal cancer (CRC) is a common intestinal malignancy worldwide, posing a serious threat to public health. Due to its high heterogeneity, prognosis and drug response of different CRC patients vary widely, limiting the effectiveness of traditional treatment. Therefore, this study aims to construct a novel CRC prognostic signature using machine learning algorithms to assist in making informed clinical decisions and improving treatment outcomes.

Methods: Gene expression matrix and clinical information of CRC patients were obtained from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then, genes with prognostic value were identified through univariate Cox regression analysis. Next, nine machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), gradient boosting machine (GBM), CoxBoost, plsRcox, Ridge, Enet, StepCox, SuperPC and survivalSVM were integrated to form 97 combinations, which was employed to screen the best strategy for building a prognostic model based on the average C-index in the three CRC cohorts. Kaplan Meier survival analysis, receiver operating curve (ROC) analysis and multivariate regression analysis were conducted to assess the predictive performance of the constructed signature. Furthermore, the CIBERSORT and ESTIMATE algorithms were utilized to quantify the infiltration level of immune cells. Besides, a nomogram were developed to predict 1-, 2-, and 3-year overall survival (OS) probabilities for individual patient.

Results: A prognostic signature consisting of 13 genes was developed utilizing LASSO Cox regression and GBM methods. Across both the training and validation datasets, the performance evaluation consistently indicated the signature's capacity to accurately predict the prognosis of CRC patients. Especially, compared with 30 published signatures, the 13-gene model exhibited dramatically superior predictive power. Even within clinical subgroups, it could still precisely stratify the prognosis. Functional analysis revealed a robust association between the signature and the immune status as well as chemotherapy response in CRC patients. Furthermore, a nomogram was created based on the signature-derived risk score, which demonstrated a strong predictive ability for OS in CRC patients.

Conclusions: The 13-gene prognostic signature is expected to be a valuable tool for risk stratification, survival prediction, and treatment evaluation of patients with CRC.

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: Partial hepatectomy (PH) is the primary method used for treating liver injury and transplantation. The regeneration process after hepatectomy requires an adequate energy supply, and mitochondria serve as the primary source of energy. Alterations in genes related to the respiratory chain complex may impact the liver regeneration process. The aim of this study was the changes in mitochondrial structure and mitochondrial function in 85% PH.

Methods: A PH (up to 85%) model was developed using male C57BL/6 mice, and the regenerated liver tissue was harvested after 24 hours. Hematoxylin and eosin staining and transmission electron microscopy were used for morphological studies. In terms of proliferation, a positive proliferating cell nuclear antigen (PCNA) rate was detected via immunohistochemistry. Real-time polymerase chain reaction was performed to identify differentially expressed genes (DEGs), which were screened using a P value of <0.05 and a |fold change| of ≥1.5. The Hiplot online tool was used for generating a volcano plot and conducting correlation analyses. R software was employed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for the DEGs. A combined Search Tool for the Retrieval of Interacting Genes/Proteins (STRING)-Cytoscape method was used for protein-protein interaction (PPI) network analyses, whereas cytoHubba was used to the screen core DEGs.

Results: After 85% PH, we observed steatosis, an increased PCNA positivity rate, mitochondrial swelling, and a reduced number of cristae due to cristae disintegration. We screened 30 DEGs that were associated with different processes, including oxidation-reduction, oxidoreductase activity, electron transfer activity, organelle envelope, inner mitochondrial membrane processes, and oxidative phosphorylation as well as those involved in nonalcoholic fatty liver disease (NAFLD). We identified a total of six hub genes: COX4I1, ATP5B, UQCRC2, CYC1, ATP5O, and ATP5A1.

Conclusions: The 85% PH model promotes mitochondrial complex protein expression, thereby providing energy for liver regeneration. The enriched genes were associated with oxidation-reduction, electron transfer activity, and inner mitochondrial membrane processes.

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: 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.