Dissecting the genetic heterogeneity of gastric cancer

An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer

Background

This study aims to develop an oral microbiota-based model for gastric cancer (GC) risk stratification and prognosis prediction.

Methods

Oral microbial markers for GC prognosis and risk stratification were identified from 99 GC patients, and their predictive potential was validated on an external dataset of 111 GC patients. The identified bacterial markers were used to construct a Deep Neural Network (DNN) model, a Random Forest (RF) model, and a Support Vector Machine (SVM) model for predicting GC prognosis.

Results

GC patients with <3 years of survival showed a higher abundance of Aggregatibacter and diminished abundances of Filifactor and Moryella than those who survived ≥3 years. The Boruta algorithm unearthed Leptotrichia as another significant marker for GC prognosis. Consequently, a DNN model was constructed based on the relative abundances of these bacteria, predicting 3-year and 5-year survival in GC patients with Area Under Curve of 0.814 and 0.912, respectively. Notably, the DNN model outperformed the TNM staging system, SVM and RF models. The prognostic value of these bacterial markers was further reinforced by external validation.

Conclusion

The oral microbiota-based DNN model may advance GC prognosis. The biological functions of these oral bacterial markers warrant further investigation from the perspective of GC progression.

Investigation of unfavorable prognostic factors for survival in Chinese patients with gastric gastrointestinal stromal tumors

Background

Gastrointestinal stromal tumor (GIST) was very rare in the gastrointestinal (GI) tract. Most GISTs were asymptomatic at early stage. Therefore, it was of great significance to explore the prognostic factors of patients with GIST. This investigation aimed to assess the unfavorable prognostic factors for overall survival (OS) and disease-free survival (DFS) in 106 Chinese patients with GISTs.

Methods

A total of 106 Chinese patients, including 68 women and 38 men, with confirmed gastric GIST treated at the General Hospital of Ningxia Medical University in China from 2012 to 2018 were included. Kaplan-Meier analysis and Cox regression models were applied to evaluate the unfavorable prognostic risk factors for survival.

Results

Kaplan-Meier analysis demonstrated that blood type A was significantly related to poor OS (P=0.01), and tumor invasion, higher Ki-67 index, synchronous gastric cancer (GC), and tumor necrosis were significantly associated with poor DFS (all P<0.05). Multivariate analysis further demonstrated that blood type A was a significant independent prognostic factor with both OS and DFS (both P<0.05). Synchronous GC and age ≥60 years were also significant independent prognostic factor for DFS (both P<0.05).

Conclusions

Blood type A, age ≥60 years, and synchronous GC were unfavorable prognostic factors for survival in Chinese patients with gastric GISTs. The mechanism underlying the prognostic role of these factors warrants further investigation.

Swedish Genome-Wide Haplotype Association Analysis Suggests Breast Cancer Loci with Varying Risk-Modifying Effects

Background: To find support for risk-modifying genes in breast cancer, a haplotype GWAS in sporadic breast cancer cases was undertaken. The results were compared with the results from previous analyses in familial cases and all cases from the same Swedish cohort. Methods: In total, 2550 women with sporadic invasive breast cancer and 5021 healthy controls were included in a sliding-window haplotype GWAS using PLINK 1.07. Results: The analysis of sporadic cases confirmed the loci on chromosomes 10q26.13, 11q13.3, and 16q12.1 and suggested one novel locus on chromosome 12p11.21 (OR = 1.42 p = 4.55 × 10-8). A comparison between these loci and the same loci in the analyses of familial cases and all breast cancer cases was undertaken. Conclusions: Haplotype GWAS in sporadic cases of Swedish breast cancer cases supported known risk loci and suggested another risk locus. The loci identified in the analysis of sporadic and all breast cancer cases were suggested to act as modifiers of the risk of breast cancer. Haplotype analysis identified other loci with higher odds ratios than single-variant analysis. Further studies are needed to find out how to best include the findings in breast cancer prevention.

Proteomic Heterogeneity of the Extracellular Matrix Identifies Histologic Subtype-Specific Fibroblast in Gastric Cancer

Gastric cancer (GC) is a highly heterogeneous disease regarding histologic features, genotypes, and molecular phenotypes. Here, we investigate extracellular matrix (ECM)-centric analysis, examining its association with histologic subtypes and patient prognosis in human GC. We performed quantitative proteomic analysis of decellularized GC tissues that characterizes tumorous ECM, highlighting proteomic heterogeneity in ECM components. We identified 20 tumor-enriched proteins including four glycoproteins, serpin family H member 1 (SERPINH1), annexin family (ANXA3/4/5/13), S100A family (S100A6/8/9), MMP14, and other matrisome-associated proteins. In addition, histopathological characteristics of GC reveals differential expression in ECM composition, with the poorly cohesive carcinoma-not otherwise specified (PCC-NOS) subtype being distinctly demarcated from other histologic subtypes. Integrating ECM proteomics with single-cell RNA sequencing, we identified crucial molecular markers in the PCC-NOS-specific stroma. PCC-NOS-enriched matrisome proteins and gene expression signatures of adipogenic cancer-associated fibroblasts (CAFadi) are closely linked, both associated with adverse outcomes in GC. Using tumor microarray analysis, we confirmed the CAFadi surface marker, ATP binding cassette subfamily A member 8 (ABCA8), predominantly present in PCC-NOS tumors. Our ECM-focused analysis paves the way for studies to determine their utility as biomarkers for patient stratification, offering valuable insights for linking molecular and histologic features in GC.

Prognostic Relevance of the Proximal Resection Margin Distance in Distal Gastrectomy for Gastric Adenocarcinoma

BACKGROUND

The risk for recurrence in patients with distal gastric cancer can be reduced by surgical radicality. However, dispute exists about the value of the proposed minimum proximal margin distance (PMD). Here, we assess the prognostic value of the safety distance between the proximal resection margin and the tumor.

PATIENTS AND METHODS

This is a single-center cohort study of patients undergoing distal gastrectomy for gastric adenocarcinoma (2001-2021). Cohorts were defined by adequacy of the PMD according to the European Society for Medical Oncology (ESMO) guidelines (≥ 5 cm for intestinal and ≥ 8 cm for diffuse Laurén's subtypes). Overall survival (OS) and time to progression (TTP) were assessed by log-rank and multivariable Cox-regression analyses.

RESULTS

Of 176 patients, 70 (39.8%) had a sufficient PMD. An adequate PMD was associated with cancer of the intestinal subtype (67% vs. 45%, p = 0.010). Estimated 5-year survival was 63% [95% confidence interval (CI) 51-78] and 62% (95% CI 53-73) for adequate and inadequate PMD, respectively. Overall, an adequate PMD was not prognostic for OS (HR 0.81, 95% CI 0.48-1.38) in the multivariable analysis. However, in patients with diffuse subtype, an adequate PMD was associated with improved oncological outcomes (median OS not reached versus 131 months, p = 0.038, median TTP not reached versus 88.0 months, p = 0.003).

CONCLUSION

Patients with diffuse gastric cancer are at greater risk to undergo resection with an inadequate PMD, which in those patients is associated with worse oncological outcomes. For the intestinal subtype, there was no prognostic association with PMD, indicating that a distal gastrectomy with partial preservation of the gastric function may also be feasible in the setting where an extensive PMD is not achievable.

GastricAITool: A Clinical Decision Support Tool for the Diagnosis and Prognosis of Gastric Cancer

BACKGROUND/OBJECTIVE

Gastric cancer (GC) is a complex disease representing a significant global health concern. Advanced tools for the early diagnosis and prediction of adverse outcomes are crucial. In this context, artificial intelligence (AI) plays a fundamental role. The aim of this work was to develop a diagnostic and prognostic tool for GC, providing support to clinicians in critical decision-making and enabling personalised strategies.

METHODS

Different machine learning and deep learning techniques were explored to build diagnostic and prognostic models, ensuring model interpretability and transparency through explainable AI methods. These models were developed and cross-validated using data from 590 Spanish Caucasian patients with primary GC and 633 cancer-free individuals. Up to 261 variables were analysed, including demographic, environmental, clinical, tumoral, and genetic data. Variables such as Helicobacter pylori infection, tobacco use, family history of GC, TNM staging, metastasis, tumour location, treatment received, gender, age, and genetic factors (single nucleotide polymorphisms) were selected as inputs due to their association with the risk and progression of the disease.

RESULTS

The XGBoost algorithm (version 1.7.4) achieved the best performance for diagnosis, with an AUC value of 0.68 using 5-fold cross-validation. As for prognosis, the Random Survival Forest algorithm achieved a C-index of 0.77. Of interest, the incorporation of genetic data into the clinical-demographics models significantly increased discriminatory ability in both diagnostic and prognostic models.

CONCLUSIONS

This article presents GastricAITool, a simple and intuitive decision support tool for the diagnosis and prognosis of GC.

Genetic Markers of Susceptibility in Gastric Cancer: A Comprehensive Systematic Review

This systematic review synthesizes findings from various studies that examine genetic markers associated with susceptibility to gastric cancer. By conducting a comprehensive search across multiple databases, we analyzed studies on the relationship between specific genetic polymorphisms and the risk of developing gastric cancer. Our review highlights significant genetic markers, including mucin 1 (MUC1), prostate stem cell antigen (PSCA), tumor necrosis factor-alpha (TNF-α), DNA methyltransferases (DNMTs), matrix metalloproteinase-7 (MMP-7), and interleukin-8 (IL-8), emphasizing their roles across different ethnic and demographic contexts. The findings demonstrate a robust association between these markers and gastric cancer susceptibility, particularly noting variations in risk among diverse populations. Such variations could inform personalized treatment and screening strategies. The review also underscores the need for further research to explore how these polymorphisms influence cancer development and to confirm their potential clinical applications. We discuss the implications of these genetic markers for global health strategies and personalized medicine, highlighting the importance of integrating genetic testing into current gastric cancer management protocols.

Genomic analyses identify 15 susceptibility loci and reveal HDAC2, SOX2-OT, and IGF2BP2 in a naturally-occurring canine model of gastric cancer

Gastric cancer (GC) is the fifth most common human cancer worldwide, but the genetic etiology is largely unknown. We performed a Bayesian genome-wide association study and selection analyses in a naturally-occurring canine model of GC, the Belgian Tervuren and Sheepdog breeds, to elucidate underlying genetic risk factors. We identified 15 loci with over 90% predictive accuracy for the GC phenotype. Variant filtering revealed germline putative regulatory variants for the EPAS1 (HIF2A) and PTEN genes and a coding variant in CD101. Although closely related to Tervuren and Sheepdogs, Belgian Malinois rarely develop GC. Across-breed analyses uncovered protective haplotypes under selection in Malinois at SOX2-OT and IGF2BP2. Among Tervuren and Sheepdogs, HDAC2 putative regulatory variants were present at comparatively high frequency and were associated with GC. Here, we describe a complex genetic architecture governing GC in a dog model, including genes such as PDZRN3, that have not been associated with human GC.

Prognostic relevance of prognostic nutritional indices in gastric or gastro-esophageal junction cancer patients receiving immune checkpoint inhibitors: a systematic review and meta-analysis

Background

The Prognostic Nutritional Index (PNI) has become an important predictive tool for assessing patients' nutritional status and immune competence. It is widely used in prognostic evaluations for various cancer patients. However, the prognostic relevance of the Prognostic Nutritional Index (PNI) in gastric or gastro-esophageal junction cancer patients (GC/GEJC) undergoing immune checkpoint inhibitors (ICIs) treatment remains unclear. This meta-analysis aimed to determine the prognostic impact of PNI in this specific patient cohort.

Methods

We conducted a thorough literature search, covering prominent databases such as PubMed, Embase, Web of Science, SpringerLink, and the Cochrane Library. The search spanned from the inception of these databases up to December 5, 2023. Employing the 95% confidence interval and Hazard Ratio (HR), the study systematically evaluated the relationship between PNI and key prognostic indicators, including the objective remission rate (ORR), disease control rate (DCR), overall survival (OS) and progression-free survival (PFS) in GC/GEJC patients undergoing ICI treatment.

Results

Eight studies comprising 813 eligible patients were selected. With 7 studies consistently demonstrating superior Overall Survival (OS) in the high-Prognostic Nutritional Index (PNI) group compared to their low-PNI counterparts (HR 0.58, 95% CI: 0.47-0.71, P<0.001). Furthermore, the results derived from 6 studies pointed out that the significant correlation between he low-PNI and poorer progression-free survival (PFS) (HR 0.58, 95% CI: 0.47-0.71, P<0.001). Subgroup analyses were performed to validate the robustness of the results. In addition, we conducted a meta-analysis of three studies examining the correlation between PNI and objective response rate/disease control rate (ORR/DCR) and found that the ORR/DCR was significantly superior in the high PNI group (ORR: RR: 1.24, P=0.002; DCR: RR: 1.43, P=0.008).

Conclusion

This meta-analysis indicates that the low-PNI in GC/GEJC patients undergoing ICI treatment is significantly linked to worse OS and PFS. Therefore, PNI can serve as a prognostic indicator of post-treatment outcomes in patients with GC receiving ICIs. Further prospective studies are required to assess the reliability of these findings.

Systematic review registration

https://inplasy.com/, identifier INPLASY202450133.

Targeted Sequencing in Gastric Cancer: Association with Tumor Molecular Characteristics and FLOT Therapy Effectiveness

Heterogeneity of gastric cancer (GC) is the main trigger of the disease's relapse. The aim of this study was to investigate the connections between targeted genes, cancer clinical features, and the effectiveness of FLOT chemotherapy. Twenty-one patients with gastric cancers (GCs) were included in this study. Tumor-targeted sequencing was conducted, and real-time PCR was used to assess the expression of molecular markers in tumors. Seven patients with stabilization had mutations that were related to their response to therapy and were relevant to the tumor phenotype. Two patients had two mutations. The number of patients with TP53 mutations increased in HER2-positive tumor status. PD-L1-positive cancers had mutations in KRAS, TP53, PIK3CA, PTEN, and ERBB, which resulted in an increase in PD-1 expression. TP53 mutation and PTEN mutation are associated with changes in factors associated with neoangiogenesis. In concusion, patients who did not have aggressive growth markers that were verified by molecular features had the best response to treatment, including complete morphologic regression.