Journal of gastrointestinal oncology最新文献

Background: Colorectal cancer liver metastasis (CRCLM) remains a leading cause of mortality in colorectal cancer (CRC) patients. While primary tumor resection combined with chemotherapy has shown promise, robust prognostic models for these patients remain scarce. This study aimed to develop and validate a nomogram to predict overall survival (OS) in CRCLM patients undergoing primary tumor resection and chemotherapy.

Methods: Data from 3,252 CRCLM patients (2010-2015) were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into training (70%, n=2,276) and validation (30%, n=976) cohorts. Independent prognostic factors were identified using Cox regression. A nomogram was constructed to predict 1-, 3-, and 5-year OS, with performance assessed via receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). A web-based calculator was developed for clinical use.

Results: Multivariate analysis identified race, age, marital status, tumor site, grade, carcinoembryonic antigen (CEA) level, tumor deposits, regional nodes examined, regional nodes positive, and liver metastases surgery as independent prognostic factors. The nomogram demonstrated good discrimination, with area under the curves (AUCs) of 0.729 (1-year), 0.710 (3-year), and 0.714 (5-year) in the training cohort and 0.717, 0.736, and 0.737 respectively in the validation cohort. Calibration curves showed strong agreement between predicted and observed outcomes. Through 1,000 bootstrap resampling iterations, the Cox model demonstrated superior discriminative accuracy with a mean C-index of 0.657 compared to the Fong clinical risk score (0.609), Basingstoke index (0.558), and tumor-node-metastasis (TNM) staging system (0.629) (all P<0.001). DCA indicated clinical utility across risk thresholds. Patients were stratified into low-, intermediate-, and high-risk groups (scores <220, 220-301, >301). The web calculator was accessible at https://lxt134520.shinyapps.io/output/.

Conclusions: This nomogram provides individualized OS prediction for CRCLM patients treated with resection and chemotherapy. However, limitations include the lack of external validation, exclusion of chemotherapy regimens/molecular markers, and potential socioeconomic confounders affecting race associations. Future studies should incorporate treatment-specific variables and validate the model in diverse cohorts to enhance generalizability.

Background: Stemness has been shown to play an important role in immunotherapy and chemotherapy response. Machine learning has been used to predict stemness-based cancer subtypes. We aimed to conduct a stemness-based classification of gastric cancer (GC) for the early identification of patients at risk for GC and guide treatment.

Methods: Stemness indices [mRNA stemness index (mRNAsi)] of 389 patients with GC from The Cancer Genome Atlas (TCGA) database were generated using one-class logistic regression (OCLR) algorithm. Consensus clustering was performed to divide the patients with GC into two subtypes based on their stemness indices. Finally, four machine learning algorithms were used to construct a logistic regression model containing 12 critical genes. An external cohort was used as the validation cohort.

Results: Stemness subtype cluster1 had higher mRNAsi scores and a significantly better prognosis, while stemness subtype cluster2 had higher immunocompetence. In terms of the prediction of therapeutic efficacy, patients in cluster2 may have a better response to anti-cytotoxic T lymphocyte antigen 4 (anti-CTLA4) therapy, whereas no significant response to anti-programmed cell death 1 (anti-PD1) therapy was observed in either subtype. The two subtypes showed significant differences in tolerance to chemotherapy. A total of 1,863 differentially expressed genes (DEGs) were identified based on the stemness signature of GC, of which 12 critical genes were selected to predict the stemness subtype. The consistency of the results in the validation cohort indicated a promising application of this stemness-based classification and predictive model.

Conclusions: Our machine learning approach performed an overall analysis of the relationship between the stemness of GC and the therapeutic effect, identified a promising stemness-based classification of GC to predict prognosis and treatment efficacy, and developed a predictive model to make the stemness-based classification accessible for clinical practice.

Background: Hepatocellular carcinoma (HCC) maintains a significant mortality burden, primarily attributable to the lack of validated prognostic molecular signatures. While N6-methyladenosine (m6A) reader proteins, especially YT521-B homology domain family proteins (YTHDFs), show emerging relevance in oncogenesis, their regulatory interplay and clinical utility as combinatorial biomarkers remain unclear. Our study aimed to explore the regulatory network and clinical relevance of YTHDF paralogs in HCC.

Methods: Spatial co-expression patterns were analyzed via immunohistochemistry and spatial transcriptomics in HCC tissues. Protein/RNA levels of YTHDFs were quantified by western blot and quantitative real-time polymerase chain reaction (PCR). RNA immunoprecipitation (RIP), enhanced crosslinking and immunoprecipitation (e-CLIP) sequencing, dual fluorescence reporter assay, and RNA stability experiments were conducted to verify underlying regulatory mechanisms. Univariate and multivariate Cox regression analysis and Kaplan-Meier survival analysis were utilized to estimate the potential clinical significances.

Results: YTHDF1 overexpression correlated with poor prognosis and emerged as the strongest prognostic marker among the YTHDF paralogs. YTHDF1 could bind to the messenger RNA (mRNA) of YTHDF2 and YTHDF3 at specific m6A sites. However, in vitro and in vivo experiments revealed that YTHDF1 did not impact the mRNA and protein levels of YTHDF3, and instead enhanced the mRNA stability and translation efficiency of YTHDF2 in an m6A-dependent manner. Interestingly, YTHDF1 and YTHDF2 exhibited comparable spatial expression patterns and demonstrated correlation in terms of spatial features and expression levels in HCC, which had not been previously reported. Clinical analysis conducted on two clinical cohorts corroborated the notable correlations between YTHDF1 and YTHDF2, and revealed that HCC patients with higher expression of both proteins had worse prognosis.

Conclusions: YTHDF1 drives HCC progression through m6A-mediated stabilization of YTHDF2, revealing their functional synergy. Their combined spatial and expression profiles offer a superior prognostic biomarker, suggesting novel therapeutic targets.

Background: Financial toxicity (FT) among patients with cancer has been associated with significantly worse outcomes, however little is known about FT specifically in the context of hepatocellular carcinoma (HCC). We sought to cross-sectionally assess patient-reported FT in newly-diagnosed HCC and identify patient-, disease-, and treatment-related factors associated with FT.

Methods: Patients were recruited pre-treatment initiation at a multidisciplinary liver cancer clinic between January 2023 and November 2024. The Comprehensive Score for Financial Toxicity-Functional Assessment of Chronic Illness Therapy (COST-FACIT), a validated survey evaluating financial distress in cancer, was completed during the initial visit. Baseline sociodemographic, clinical, and planned treatment characteristics were evaluated by questionnaires or medical record review. Quality of life (QOL) was evaluated using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ)-HCC18. Mean COST-FACIT scores were compared across these characteristics via t-test or analysis of variance (ANOVA).

Results: A total of 38/50 (76%) enrolled patients completed the COST-FACIT. Younger age (<65 vs. ≥65 years; mean, 21.2 vs. 28.6; P=0.04), Hispanic ethnicity (Hispanic vs. non-Hispanic ethnicity; mean, 16.1 vs. 27.3; P=0.05), non-English primary language (non-English vs. English; mean, 16.2 vs. 27.6; P=0.03), lack of employment (not employed vs. retired vs. employed; mean, 15.5 vs. 30.5 vs. 24.8; P=0.03), less completed education (high school or earlier vs. college or beyond; mean, 23.0 vs. 32.6; P=0.01), worse QOL (EORTC QLQ-HCC18 score > cohort median vs. ≤ cohort median; mean, 22.1 vs. 29.3; P=0.04), and no plan for surgical treatment (no plan vs. planned; mean, 24.6 vs. 35.6; P=0.03) were significantly associated with worse FT. Lower household income (<$50,000 vs. $50,000-$99,999 vs. ≥$100,000; mean, 25.0 vs. 23.1 vs. 33.0; P=0.09) trended towards worse FT.

Conclusions: Among patients with newly-diagnosed HCC, significantly higher patient-reported FT is associated with younger age, Hispanic ethnicity, non-English primary language, lack of employment, less completed education, no plan for surgical treatment, and worse QOL. The study cohort is being longitudinally followed to assess the course of self-reported FT over 1-year follow-up and its associations with treatment adherence and survival.

Background: Hepatic epithelioid hemangioendothelioma (HEH) is a rare vascular malignancy with imaging features often overlapping with hepatic metastatic tumors (HMTs), leading to frequent misdiagnosis. Current diagnostic reliance on qualitative imaging signs lacks specificity, while biopsy-the gold standard-is invasive and impractical for multifocal lesions. The study aimed to assess the diagnostic performance of multi-phase contrast-enhanced computed tomography (CECT) in differentiating HEH from HMT.

Methods: This retrospective comparative study included 33 patients (mean age: 42.06 years; male: 27.27%) with histologically confirmed HEH and 36 patients (mean age: 64.22 years; male: 66.67%) with HMT between January 2017 and July 2024. Only non-coalescent hepatic lesions at the time of the initial emergence were analyzed to avoid confounding imaging features. Quantitative parameters including contrast enhancement ratio (CER), lesion-to-liver contrast ratio (LLC), arterial phase related absolute and relative percentage washout (APW_arterial and RPW_arterial), portal phase related absolute and relative percentage washout (APW_portal and RPW_portal) derived from multiphase hepatic CECT were evaluated and compared between HEH and HMT. Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic performance of these parameters.

Results: The study found that APW_arterial, APW_portal and RPW_portal were significantly lower in HEH compared to HMT (P=0.01, P=0.001 and P=0.001, respectively). According to the ROC curve, APW_portal and RPW_portal showed moderate diagnostic significance with the area under the curve (AUC) values of 0.729 and 0.728, respectively. The cut-off values for APW_portal and RPW_portal were -17.08% and -6.90%, respectively.

Conclusions: Multiphase CECT-derived parameters, particularly APW_portal and RPW_portal, demonstrate potential value in differentiating HEH from HMT.

Background and objective: Biliary tract cancers (BTCs), including gallbladder cancer and cholangiocarcinoma, represent a rare yet aggressive class of malignancies with limited therapeutic options and poor survival outcomes. BTCs have been traditionally managed with gemcitabine and cisplatin (GC), but this standard of care has shown limited efficacy due to early recurrence, high resistance rates, and molecular heterogeneity. This review comprehensively summarizes the latest developments in systemic therapies for BTCs, emphasizing recent advances in chemotherapy, targeted therapy, immunotherapy, and rational combination regimens. Particular focus is given to molecularly guided treatment strategies and the evolving role of biomarkers in personalizing therapy.

Methods: A thorough search of PubMed, EMBASE, Google Scholar, and the Cochrane Library was conducted for literature published from December 2010 to December 2024. Peer-reviewed clinical trials, meta-analyses, and relevant conference abstracts were examined, especially those related to emerging systemic treatments, molecular targets, and ongoing trials. Particular attention was given to dosing strategies and the integration of novel agents in optimizing patient outcomes.

Key content and findings: Recent breakthroughs in molecular profiling have identified actionable alterations such as FGFR2 fusions, IDH1/2 mutations, HER2 amplification, and BRAF V600E mutations, enabling the development of targeted therapies with promising efficacy. Immune checkpoint inhibitors (ICIs), especially when combined with chemotherapy, have demonstrated survival benefits in landmark trials such as TOPAZ-1 and KEYNOTE-966. Additionally, novel regimens including nanoliposomal irinotecan and dual ICI combinations represent viable second-line options. The integration of genomic biomarkers and individualized treatment approaches is reshaping the therapeutic landscape of BTCs.

Conclusions: Systemic therapy for BTCs is undergoing a transformative shift, driven by precision oncology and immunotherapeutic advances. Personalized treatment guided by molecular characteristics is now central to improving outcomes. Future efforts must prioritize the identification of biomarkers, clarification of resistance mechanisms, and optimization of multimodal treatment strategies to maximize patient benefit.

Background: Chemotherapy is an important treatment for intrahepatic cholangiocarcinoma (ICC) patients, but there is a lack of survival prediction models. This study aims to develop a nomogram to predict cancer-specific survival (CSS) in ICC patients receiving chemotherapy.

Methods: A retrospective analysis was performed using data from the Surveillance, Epidemiology, and End Results (SEER) database involving 1,363 ICC patients who receiving chemotherapy between 2010 and 2019. The patients were randomly allocated in a 7:3 ratio to the training cohort and validation cohort. Cox proportional hazards regression analysis was employed to identify prognostic factors for nomogram construction. The accuracy of the model was assessed using the concordance index (C-index), area under the curve (AUC) value, and calibration curve. Additionally, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were utilized to evaluate the clinical value of the nomogram and to compare it with tumor staging based on American Joint Committee on Cancer (AJCC) criteria.

Results: Multivariate Cox regression analysis selected seven variables to establish the nomogram. The C-index and AUC value indicate that the nomogram has high accuracy. The calibration curve shows good consistency between the actual observed values and the nomogram-predicted CSS. Meanwhile, DCA, NRI, and IDI demonstrate that the nomogram has significant clinical applicability compared to tumor staging based on AJCC criteria. Furthermore, a risk classification system with satisfactory ability to identify different-risk patients was established.

Conclusions: We have developed a nomogram for predicting the prognosis of ICC patients receiving chemotherapy, which can effectively assess the prognosis of this patient population.

Background: Rectal cancer with synchronous lung metastases has a poor prognosis and high mortality. Despite treatment advancements, effective prognostic tools for early diagnosis and personalized treatment remain limited. This study develops and validates a survival nomogram to improve prognosis prediction and guide treatment strategies.

Methods: A total of 1,257 patients with rectal cancer and synchronous lung metastasis were identified from the Surveillance, Epidemiology, and End Results (SEER) database. They were divided into a training cohort (n=880) and an internal validation cohort (n=377). An external validation cohort of 132 patients was retrospectively collected from Affiliated Dongyang Hospital of Wenzhou Medical University. A survival nomogram was developed using variables identified through univariate and multivariate Cox regression analyses and assessed using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and calibration curves. Kaplan-Meier analysis and log-rank tests were used to compare overall survival (OS) outcomes.

Results: Six key risk factors were identified: carcinoembryonic antigen (CEA) level, chemotherapy, tumor stage 2, tumor grade I, radiation therapy, and tumor size (5-100 mm). The nomogram demonstrated strong predictive accuracy for 1-, 3-, and 5-year OS, with area under the curve (AUC) ranging from 0.65 to 0.94. High-risk patients (score ≥104) had significantly worse OS than low-risk patients (P<0.001). Subgroup analysis confirmed that chemotherapy and radiotherapy significantly influenced survival (P<0.05).

Conclusions: This validated survival nomogram provides a reliable tool for prognosis prediction and treatment planning in rectal cancer with synchronous lung metastasis, assisting in clinical decision-making.

Background: Colorectal cancer (CRC) is a global health burden characterized by significant morbidity and mortality rates. While current therapeutic strategies, including surgical intervention and adjuvant chemotherapy, have shown moderate success, patients with advanced-stage CRC frequently encounter substantial therapeutic obstacles, primarily stemming from acquired drug resistance and tumor immune evasion. Emerging research suggests that phytochemicals are promising therapeutic candidates due to their pleiotropic regulatory capacities, particularly their capability to modulate immune checkpoint inhibitor (ICI) resistance pathways. These bioactive compounds could be used to develop novel therapeutic approaches based on the epigenetic reprogramming of tumor cells and the remodeling of the metabolism-immune crosstalk axis in the tumor microenvironment (TME). This study aimed to investigate the effects and underlying mechanisms of andrographolide in targeting mitochondrial function and remodeling the tumor immune microenvironment in CRC.

Methods: This study used Cell Counting Kit-8, live and dead cell staining, immunofluorescence, western blotting, enzyme-linked immunosorbent assay, flow analysis (using CT26 cells), and mouse xenografts to explore the anti-tumor effect and mechanism of andrographolide, a natural product, in CRC.

Results: By targeting the voltage-dependent anion channel (VDAC) protein, andrographolide affects the mitochondrial membrane potential of CRC cells, activates the natural immune pathway of cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) during tumor proliferation, and reshapes the TME of CRC by recruiting dendric cells, CD4⁺ T cells, and CD8⁺ T cells, and reducing immunosuppressive regulatory T cells.

Conclusions: This study revealed the anti-tumor effect of andrographolide in CRC and the mechanism of immune metabolism regulation. Our findings provide a theoretical basis for the application of natural products in CRC immunotherapy.