Frequency and Prognosis of Pulmonary Metastases in Newly Diagnosed Gastric Cancer

Downregulation of PDIA3 inhibits gastric cancer cell growth through cell cycle regulation

OBJECTIVE

Protein disulfide isomerase A3 (PDIA3) promotes the correct folding of newly synthesized glycoproteins in the endoplasmic reticulum. PDIA3 is overexpressed in most tumors, and it may become a biomarker of cancer prognosis and immunotherapy. Our study aims to detect the expression level of PDIA3 in gastric cancer (GC) and its association with GC development as wells as the underlying mechanisms.

METHODS

GC cell lines with PDIA3 knockdown by siRNA, CRISPR-cas9 sgRNAs or a pharmacological inhibitor of LOC14 were prepared and used. PDIA3 knockout GC cells were established by CRISPR-cas9-PDIA3 system. The proliferation, migration, invasion and cell cycle of GC cells were analyzed by cell counting kit-8 assay, wound healing assay, transwell assay and flow cytometry, respectively. Immunodeficient nude mice was used to evaluate the role of PDIA3 in tumor formation. Quantitative PCR and western blot were used for examining gene and protein expressions. RNA sequencing was performed to see the altered gene expression.

RESULTS

The expressions of PDIA3 in GC tissues and cells were increased significantly, and its expression was negatively correlated with the three-year survival rate of GC patients. Down-regulation of PDIA3 by siRNA, LOC14 or CRISPR-cas9 significantly inhibited proliferation, invasion and migration of GC cells TMK1 and AGS, with cell cycle arrested at G2/M phase. Meanwhile, decreased PDIA3 significantly inhibited growth of tumor xenograft in vivo. It was found that cyclin G1 (encoded by CCNG1 gene) expression was decreased by downregulation of PDIA3 in GC cells both in vitro and in vivo. In addition, protein levels of other cell cycle related factors including cyclin D1, CDK2, and CDK6 were also significantly decreased. Further study showed that STAT3 was associated with PDIA3-mediated cyclin G1 regulation.

CONCLUSION

PDIA3 plays an oncogenic role in GC. Our findings unfolded the functional role of PDIA3 in GC development and highlighted a novel target for cancer therapeutic strategy.

Establishment of Golgi apparatus-related genes signature to predict the prognosis and immunotherapy response in gastric cancer patients

The Golgi apparatus plays a crucial role in intracellular protein transportation, processing, and sorting. Dysfunctions of the Golgi apparatus have been implicated in tumorigenesis and drug resistance. This study aimed to investigate the prognostic and treatment response assessment value of Golgi apparatus-related gene (GARGs) features in gastric cancer patients. Transcriptome data and clinical information of gastric cancer patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Cox regression analysis was employed to assess the prognostic significance of GARGs and construct risk features. The immune landscape, drug sensitivity, immune therapy response, gene expression patterns, and somatic mutation characteristics were analyzed between different risk groups. A nomogram model for predicting gastric cancer prognosis was developed and evaluated. Among 1643 GARGs examined, 365 showed significant associations with gastric cancer prognosis. Five independent prognostic GARGs (NGF, ABCG1, CHAC1, GBA2, PCSK7) were selected to construct risk features for gastric cancer patients. These risk features effectively stratified patients into high-risk and low-risk groups, with the former exhibiting worse prognosis than the latter. Patients in the high-risk group displayed higher levels of immune cell infiltration, while the expression levels of NGF, CHAC1, GBA2, PCSK7 were significantly correlated with immune cell infiltration. Notably, the low-risk group exhibited higher sensitivity to epothilone.B, metformin, and tipifarnib compared to the high-risk group. Moreover, patients in the low-risk group demonstrated greater responsiveness to immune therapy than those in the high-risk group. In terms of biological processes and KEGG pathways related to immunity regulation, significant suppression was observed in the high-risk group compared to the low-risk group; meanwhile cell cycle pathways exhibited significant activation in the high-risk group. Furthermore, the low-risk group exhibited a higher tumor mutation burden compared to the high-risk group. The risk features derived from GARGs, in conjunction with age, were identified as independent risk factors for gastric cancer. The nomogram incorporating these factors demonstrated improved performance in predicting gastric cancer prognosis. Our study established risk features derived from GARGs that hold potential clinical utility in prognostic assessment and immune therapy response evaluation of gastric cancer patients.

Artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer

Background

Gastric cancer with synchronous distant metastases indicates a dismal prognosis. The success in survival improvement mainly relies on our ability to predict the potential benefit of a therapy. Our objective is to develop an artificial intelligence annotated clinical-pathologic risk model to predict its outcomes.

Methods

In participants (n=47553) with gastric cancer of the surveillance, epidemiology, and end results program, we selected patients with distant metastases at first diagnosis, complete clinical-pathologic data and follow-up information. Patients were randomly divided into the training and test cohort at 7:3 ratio. 93 patients with advanced gastric cancer from six other cancer centers were collected as the external validation cohort. Multivariable analysis was used to identify the prognosis-related clinical-pathologic features. Then a survival prediction model was established and validated. Importantly, we provided explanations to the prediction with artificial intelligence SHAP (Shapley additive explanations) method. We also provide novel insights into treatment options.

Results

Data from a total 2549 patients were included in model development and internal test (median age, 61 years [range, 53-69 years]; 1725 [67.7%] male). Data from an additional 93 patients were collected as the external validation cohort (median age, 59 years [range, 48-66 years]; 51 [54.8%] male). The clinical-pathologic model achieved a consistently high accuracy for predicting prognosis in the training (C-index: 0.705 [range, 0.690-0.720]), test (C-index: 0.737 [range, 0.717-0.757]), and external validation (C-index: 0.694 [range, 0.562-0.826]) cohorts. Shapley values indicated that undergoing surgery, chemotherapy, young, absence of lung metastases and well differentiated were the top 5 contributors to the high likelihood of survival. A combination of surgery and chemotherapy had the greatest benefit. However, aggressive treatment did not equate to a survival benefit. SHAP dependence plots demonstrated insightful nonlinear interactive associations among predictors in survival benefit prediction. For example, patients who were elderly, or poor differentiated, or presence of lung or bone metastases had a worse prognosis if they undergo surgery or chemotherapy, while patients with metastases to liver alone seemed to gain benefit from surgery and chemotherapy.

Conclusion

In this large multicenter cohort study, we developed an artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer. It could be used to discuss treatment options.

Gastric cancer-derived exosomes facilitate pulmonary metastasis by activating ERK-mediated immunosuppressive macrophage polarization

Gastric cancer (GC) with pulmonary metastasis is one of the deadliest diseases in the world; however, the underlying pathological mechanisms and potential therapeutic targets remain to be elucidated. As exosomes play indispensable roles in the formation of premetastatic niches (PMN) and cancer metastasis. Therefore, investigating the underlying mechanisms of exosome-mediated pulmonary metastasis of GC may shed new light on identifying novel therapeutic targets for GC treatment. GC-derived exosomes were isolated from the conditioned medium of mouse forestomach carcinoma (MFC) cell line. The effects of MFC-derived exosomes on pulmonary macrophage polarization were analyzed by reverse- transcription polymerase chain reaction and flow cytometry. Expression of PD-L1 and other proteins was evaluated by Western blot. Exosomal microRNAs (miRNAs) were analyzed by microarray. GC-derived exosomes (GC-exo) accumulated in high numbers in the lungs and were ingested by macrophages. The extracellular-signal-regulated kinase (ERK) signaling pathway was activated by GC-exo, inducing macrophage immunosuppressive-phenotype differentiation and increased PD-L1 expression. miRNA-sequencing identified 130 enriched miRNAs in GC-exo. Among the enriched miRNAs, miR-92a-3p plays a major role in activating ERK signaling via inhibition of PTEN expression. In addition, inhibiting ERK signaling with PD98059 significantly reduced the expression of PD-L1 in macrophages and, therefore, reversed the immunosuppressive PMN and inhibited the colonization of GC cells in the lungs. This study identified a novel mechanism of GC-exo mediated PD-L1 expression in lung macrophages that facilitates lung PMN formation and GC pulmonary metastasis, which also provided a potential therapeutic target for GC with pulmonary metastasis treatment.

Malignancies in Patients with Celiac Disease: Diagnostic Challenges and Molecular Advances

Celiac disease (CD) is a multiorgan autoimmune disorder of the chronic intestinal disease group characterized by duodenal inflammation in genetically predisposed individuals, precipitated by gluten ingestion. The pathogenesis of celiac disease is now widely studied, overcoming the limits of the purely autoimmune concept and explaining its hereditability. The genomic profiling of this condition has led to the discovery of numerous genes involved in interleukin signaling and immune-related pathways. The spectrum of disease manifestations is not limited to the gastrointestinal tract, and a significant number of studies have considered the possible association between CD and neoplasms. Patients with CD are found to be at increased risk of developing malignancies, with a particular predisposition of certain types of intestinal cancer, lymphomas, and oropharyngeal cancers. This can be partially explained by common cancer hallmarks present in these patients. The study of gut microbiota, microRNAs, and DNA methylation is evolving to find the any possible missing links between CD and cancer incidence in these patients. However, the literature is extremely mixed and, therefore, our understanding of the biological interplay between CD and cancer remains limited, with significant implications in terms of clinical management and screening protocols. In this review article, we seek to provide a comprehensive overview of the genomics, epigenomics, and transcriptomics data on CD and its relation to the most frequent types of neoplasms that may occur in these patients.

Survival nomogram for different grades of gastric cancer patients based on SEER database and external validation cohort

Background

Influencing factors varied among gastric cancer (GC) for different differentiation grades which affect the prognosis accordingly. This study aimed to develop a nomogram to effectively identify the overall survival (OS).

Methods

Totally, 9,568 patients with GC were obtained from the SEER database as the training cohort and internal validation cohort. We then retrospectively enrolled patients diagnosed with GC to construct the external validation cohort from the First Affiliated Hospital of Anhui Medical University. The prognostic factors were integrated into the multivariate Cox regression to construct a nomogram. To test the accuracy of the model, we used the calibration curves, receiver operating characteristics (ROC) curves, C-index, and decision curve analysis (DCA).

Results

Race chemotherapy, tumor size, and other four factors were significantly associated with the prognosis of Grade III GC Patients. On this basis, we developed a nomogram. The discrimination of the nomogram revealed good prognostic accuracy The results of the area under the curve (AUC) calculated by ROC for five-year survival were 0.828 and 0.758 in the training set and external validation cohort, higher than that of the TNM staging system. The calibration plot revealed that the estimated risk was close to the actual risk. DCA also suggested an excellent predictive value of the nomogram. Similar results were obtained in Grade-I and Grade-II GC patients.

Conclusions

The nomogram developed in this study and other findings could help individualize the treatment of GC patients and assist clinicians in their shared decision-making with patients.

A Nomogram Combining Neutrophil-to-Lymphocyte Ratio and D-Dimer Predicts Chemosensitivity of Oxaliplatin-Based First-Line Chemotherapy in Patients with Unresectable Advanced Gastric Cancer

Introduction: No effective peripheral blood predictors have been establoshed for first-line chemotherapy in patients with advanced gastric cancer. In this study, a nomogram combining the neutrophil-to-lymphocyte ratio/D-dimer with gender, number of metastases, and histological grade was established to predict progression-free survival in patients with unresectable advanced gastric cancer. Methods: We retrospectively collected baseline clinical characteristics and blood parameters from 153 patients diagnosed with advanced gastric cancer that underwent oxaliplatin-based first-line chemotherapy. Kaplan–Meier analysis and Cox regression analysis were used to determine the factors associated with progression-free survival. The concordance index (C-index) and calibration curve were used to determine the prediction accuracy and discriminative ability of the nomogram as a visual complement to the prognostic score system. Results: Determined by the X-tile software, the optimal cut-off points for the neutrophil-to-lymphocyte ratio and D-dimer were 3.18 and 0.56 mg/L, respectively. Multivariate analysis identified four independent prognostic factors: two or more metastatic organs (HR: 1.562, 95% CI: 1.009-2.418, P = .046), poor differentiation (HR: 0.308, 95% CI: 0.194-0.487, P < .001), neutrophil-to-lymphocyte ratio >3.18 (HR: 1.427, 95% CI: 1.024-1.989, P = .036), and D-dimer >0.56 mg/L (HR: 1.811, 95% CI: 1.183-2.773, P = .006). Receiver operating characteristic curves showed that the combination of the neutrophil-to-lymphocyte ratio and D-dimer in the prediction model exhibited the highest predictive performance (area under the curve, 0.800). The prognostic nomogram yielded a C-index of 0.800. Decision curve analysis demonstrated that the prognostic nomogram was clinically useful. A nomogram-based risk classification system was also constructed to facilitate risk stratification of advanced gastric cancer for optimal clinical management. Conclusion: We identified the neutrophil-to-lymphocyte ratio and D-dimer level as independent prognostic factors for advanced gastric cancer. The prognostic nomogram combining the neutrophil-to-lymphocyte ratio and D-dimer level can be applied in the individualized prediction of treatment outcome in patients with advanced gastric cancer.

Identifying Factors Affecting Cancer-related Death in Patients with Adenocarcinoma Gastric Cancer; An Analysis in the Presence of Competing Risks

Background: Adenocarcinoma is the most common type of gastric cancer that has shorter survival than other types of gastric cancer. The death of patients with this type of cancer may be due to the progression of cancer or other related causes. Objectives: The aim of this study is to determine the factors affecting death due to the cancer progression in gastric cancer patients with the diagnosis of adenocarcinoma, using competing risk models. Methods: This retrospective cohort study was performed on 306 gastric cancer patients diagnosed with adenocarcinoma referring to Imam Khomeini clinic in Hamadan from 2002 to 2017. Death due to the cancer progression was considered an interest event and death due to without progression as a competing event. To determine the effect of covariates on hazard, the cause-specific and subdistribution hazard regression models were used. Data analysis was performed, using R3.6.1 software and cmprsk and survival packages. Results: The mean (SD) age of patients was 62.3 (12.5) years and 74.3% were male. The effect of the stage, the number of involved lymphomas, and the type of treatment were significant on the hazard of death due to the disease progression in both cause-specific and subdistribution hazard models. Conclusions: The results showed that most deaths occur in the first 3 years of follow-up. The higher stage and higher number of lymph nodes have increased the hazard of death but supplementary treatment significantly decreased the hazard of death due to cancer progression in adenocarcinoma gastric cancer patients in both competing risk models.

The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning

Background

Gastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies.

Methods

Based on the abundance of tumor-infiltrating immune cells in GC patients from The Cancer Genome Atlas, we used unsupervised consensus clustering algorithm to identify robust clusters of patients, and assessed their reproducibility in an independent cohort from Gene Expression Omnibus. We further confirmed the feasibility of our immune subtypes in five independent pan-cancer cohorts. Finally, functional enrichment analyses were provided, and a deep learning model studying the pathological images was constructed to identify the immune subtypes.

Results

We identified and validated three reproducible immune subtypes presented with diverse components of tumor-infiltrating immune cells, molecular features, and clinical characteristics. An immune-inflamed subtype 3, with better prognosis and the highest immune score, had the highest abundance of CD8+ T cells, CD4+ T–activated cells, follicular helper T cells, M1 macrophages, and NK cells among three subtypes. By contrast, an immune-excluded subtype 1, with the worst prognosis and the highest stromal score, demonstrated the highest infiltration of CD4+ T resting cells, regulatory T cells, B cells, and dendritic cells, while an immune-desert subtype 2, with an intermediate prognosis and the lowest immune score, demonstrated the highest infiltration of M2 macrophages and mast cells, and the lowest infiltration of M1 macrophages. Besides, higher proportion of EVB and MSI of TCGA molecular subtyping, over expression of CTLA4, PD1, PDL1, and TP53, and low expression of JAK1 were observed in immune subtype 3, which consisted with the results from Gene Set Enrichment Analysis. These subtypes may suggest different immunotherapy strategies. Finally, deep learning can predict the immune subtypes well.

Conclusion

This study offers a conceptual frame to better understand the tumor immune microenvironment of GC. Future work is required to estimate its reference value for the design of immune-related studies and immunotherapy selection.

A population-based study: how to identify high-risk T1 gastric cancer patients?

In T1 gastric cancer (GC), lymph nodes metastasis (LNM) is considered as a significant prognostic predictor and closely associated with following therapeutic approaches as well as distant metastasis (DM). This study aimed to not only seek risk factors of LNM and DM but also unpack the prognosis in T1 GC patients. We performed a retrospective study enrolling 5547 patients in T1 GC from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression models were produced to recognize independent risk factors of LNM and DM. Cox regression analyses were performed to identify important prognostic factors of overall survival (OS). Cancer-specific cumulative incidence was plotted by cumulative incidence function. Three nomograms of LNM, DM and OS were established and validated by receiver operating characteristic (ROC) and calibration curves to evaluate discrimination and accuracy. Decision curve analysis (DCA), clinical impact curves (CIC) and subgroups based on risk scores were constructed to measure nomograms clinical utility. The area under the curve (AUC) of LNM nomogram and DM nomogram were 0.735 and 0.896, respectively. OS nomogram was constructed and the corresponding C-index was 0.797. In conclusion, our user-friendly nomograms, which aimed to predict LNM, DM and OS in T1 gastric cancer patients, have shown high efficiency of discrimination and accuracy. These useful and visual tools may have advantageous clinical utility to identify high-risk T1 gastric patients and help clinicians to draw up an individual therapeutic strategy.

Prognostic Value of the Site of Distant Metastasis and Surgical Interventions in Metastatic Gastric Cancer: A Population-Based Study

BACKGROUND

Studies on the prognostic significance of site-specific distant metastasis, multiple-site metastases, and the impact of surgery of the primary tumor and metastatic lesion on survival outcomes of patients with metastatic gastric cancer (GC) remain elusive. Therefore, this study aimed to investigate the prognostic significance of the site of distant metastasis among patients with metastatic GC. Furthermore, the effect of surgery of the primary tumor and metastatic lesion on the prognosis of metastatic GC was also analyzed.

METHODS

The data of 4,221 eligible patients, who were diagnosed with metastatic GC between 2010 and 2015, were identified from the Surveillance Epidemiology and End Results (SEER) database. Multivariate logistic regression analysis was performed to assess the association between potential prognostic factors, including the site of metastasis and surgery, and survival of patients with metastatic GC. Overall survival (OS) and cause-specific survival (CSS) were determined using the Kaplan-Meier survival curves and differences were assessed using the Log-rank test.

RESULTS

Out of the total 4,221 GC patients with definite organ metastases, 3312 patients had single-site metastasis while 909 patients had multiple-site metastases. GC patients with single-site metastasis of liver or lung exhibited better CSS and OS compared to those with bone metastasis. Furthermore, GC patients with liver metastasis benefited from surgery of both the primary and metastatic lesions, while those with lung metastasis benefited from surgery of metastasis resection only. Multivariate Cox regression analysis revealed that GC patients with single-site metastasis, well-differentiated tumors, GC patients who underwent surgery of the primary tumor and those who received chemotherapy exhibited favorable prognosis.

CONCLUSIONS

The site of metastasis was an independent prognostic factor for metastatic GC. Surgery had survival benefits in certain cases of metastatic GC; however, further studies are warranted to clarify these benefits in carefully selected patients.

Chemotherapy Use and Survival Among Young and Middle-Aged Patients With Gastric Cancer

INTRODUCTION:

Treatments for young patients with gastric cancer (GC) remain poorly defined, and their effects on survival are uncertain. We aimed to investigate the receipt of chemotherapy by age category (18–49, 50–64, and 65–85 years) and explore whether age differences in chemotherapy matched survival gains in patients with GC.

METHODS:

Patients who were histologically diagnosed with GC were included from a Chinese multi-institutional database and the Surveillance, Epidemiology, and End Results database. There were 5,122 and 31,363 patients aged 18–85 years treated between 2000 and 2014, respectively. Overall survival and stage-specific likelihood of receiving chemotherapy were evaluated.

RESULTS:

Of the 5,122 and 31,363 patients in China and Surveillance, Epidemiology, and End Result data sets, 3,489 (68.1%) and 18,115 (57.8%) were men, respectively. Younger (18–49 years) and middle-aged (50–64 years) patients were more likely to receive chemotherapy compared with older patients (65–85 years) (64.9%, 56.7%, and 45.4% in the 3 groups from the China data set). Among patients treated with surgery alone, a significantly better prognosis was found in younger and middle-aged patients than their older counterparts; however, no significant differences were found in overall survival among age subgroups in patients who received both surgery and chemotherapy, especially in the China data set. The survival benefit from chemotherapy was superior among older patients (all P < 0.0001) compared with that among younger and middle-aged patients in stage II and III disease.

DISCUSSION:

Potential overuse of chemotherapy was found in younger and middle-aged patients with GC, but the addition of chemotherapy did not bring about matched survival improvement, especially in the China data set.

Genomics Score Based on Genome-Wide Network Analysis for Prediction of Survival in Gastric Cancer: A Novel Prognostic Signature

Purpose

Gastric cancer (GC) is a product of multiple genetic abnormalities, including genetic and epigenetic modifications. This study aimed to integrate various biomolecules, such as miRNAs, mRNA, and DNA methylation, into a genome-wide network and develop a nomogram for predicting the overall survival (OS) of GC.

Materials and Methods

A total of 329 GC cases, as a training cohort with a random of 150 examples included as a validation cohort, were screened from The Cancer Genome Atlas database. A genome-wide network was constructed based on a combination of univariate Cox regression and least absolute shrinkage and selection operator analyses, and a nomogram was established to predict 1-, 3-, and 5-year OS in the training cohort. The nomogram was then assessed in terms of calibration, discrimination, and clinical usefulness in the validation cohort. Afterward, in order to confirm the superiority of the whole gene network model and further reduce the biomarkers for the improvement of clinical usefulness, we also constructed eight other models according to the different combinations of miRNAs, mRNA, and DNA methylation sites and made corresponding comparisons. Finally, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were also performed to describe the function of this genome-wide network.

Results

A multivariate analysis revealed a novel prognostic factor, a genomics score (GS) comprising seven miRNAs, eight mRNA, and 19 DNA methylation sites. In the validation cohort, comparing to patients with low GS, high-GS patients (HR, 12.886; P < 0.001) were significantly associated with increased all-cause mortality. Furthermore, after stratification of the TNM stage (I, II, III, and IV), there were significant differences revealed in the survival rates between the high-GS and low-GS groups as well (P < 0.001). The 1-, 3-, and 5-year C-index of whole genomics-based nomogram were 0.868, 0.895, and 0.928, respectively. The other models have comparable or relatively poor comprehensive performance, while they had fewer biomarkers. Besides that, DAVID 6.8 further revealed multiple molecules and pathways related to the genome-wide network, such as cytomembranes, cell cycle, and adipocytokine signaling.

Conclusion

We successfully developed a GS based on genome-wide network, which may represent a novel prognostic factor for GC. A combination of GS and TNM staging provides additional precision in stratifying patients with different OS prognoses, constituting a more comprehensive sub-typing system. This could potentially play an important role in future clinical practice.