Introduction

Gastric cancer with high morbidity and mortality afflicts a significant proportion of the worldwide population. Long non-coding RNAs (lncRNAs) play an important role in the development of gastric cancer and are closely associated with programmed cell death (PCD), which can be influenced by post-translational protein modifications.

Aims & Methods

Sequencing data of lncRNAs in patients with gastric cancer were obtained from TCGA and ARCG database. Important prognostic lncRNAs were identified through LASSO algorithm and Kaplan–Meier survival curve analysis and receiver operating characteristic curve analysis were used to evaluate the prognostic power of this model. Their effects on the biological functions of gastric cancer cells were investigated by in-vitro experiments, as well as the relationship between these lncRNAs and PCD, glycosylation.

Results

WGCNA and correlation analyses identified 168 PCD and glycosylation-related lncRNAs, 36 of which were of differential expression and prognostic value by univariate Cox analysis. A 11-lncRNA signature including MIR4435-2HG, LINC02381, LINC00106, LSAMP-AS1, LINC01140, LINC01614, RPH3AL-AS1, LINC02864, LEF1-AS1, LINC00844 and DPH6-DT was established and showed good performance in both TCGA and ARCG set. Differences in immune cells, immune functions and gene mutation were also found between high-risk and low-risk groups. In vitro experiments showed that PCD and glycosylation were influenced by RPH3AL-AS1 and LINC00106 knockdown.

Conclusion

This PCD and glycosylation-related signature may be a promising biomarker for predicting clinical outcomes in patients with gastric cancer.

References

Zhuo W, Liu Y, Li S, Guo D, Sun Q, Jin J, et al. Long Noncoding RNA GMAN, Up-regulated in Gastric Cancer Tissues, Is Associated With Metastasis in Patients and Promotes Translation of Ephrin A1 by Competitively Binding GMAN-AS. Gastroenterology. 2019;156(3):676-91.e11.
Zhang E, He X, Zhang C, Su J, Lu X, Si X, et al. A novel long noncoding RNA HOXC-AS3 mediates tumorigenesis of gastric cancer by binding to YBX1. Genome Biol. 2018;19(1):154.
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Chen Y, Jia K, Sun Y, Zhang C, Li Y, Zhang L, et al. Predicting response to immunotherapy in gastric cancer via multi-dimensional analyses of the tumour immune microenvironment. Nat Commun. 2022;13(1):4851.
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Introduction

Lymphoplasmacytic-rich and neutrophilic-rich tumour immune microenvironment (TIM) have been described in Epstein-Barr-virus-associated (EBV+) and microsatellite-unstable (MSI-high) gastric cancer (GC), respectively. Aim: to explore associations between EBV+/MSI-high status, TIM, peripheral blood cell ratios, tumour serum biomarkers and prognostic outcomes in GC.

Aims & Methods

Lymphoplasmacytic-rich and neutrophilic-rich tumour immune microenvironment (TIM) have been described in Epstein-Barr-virus-associated (EBV+) and microsatellite-unstable (MSI-high) gastric cancer (GC), respectively. Aim: to explore associations between EBV+/MSI-high status, TIM, peripheral blood cell ratios, tumour serum biomarkers and prognostic outcomes in GC.

Results

The series encompassed 7/140 EBV+ (5.0%) and 38/140 MSI-high (27.1%) GCs, characterised by lymphoplasmacytic-rich (EBV+:71.4% vs EBV-:5.3%, p<0.001) and neutrophilic-rich (MSI-high:28.9% vs MSS:2.0%, p<0.001) TMI, respectively. Prior to surgery, patients who underwent NAC harboured lower NLR compared to those submitted to surgery alone (3.05 vs 2.53, p=0.021). Pre-operative NLR and post-operative CRP levels were lower in neutrophilic-rich TMI GC (p=0.015 and p=0.006, respectively). Serum tumour biomarkers (CA125, CA19.9, CEA) were not related to morphological features or molecular subgroups. Overall, GC patients with lower CRP after surgery (<1.1mg/dl) showed better overall survival (p=0.032), even when stratified by type of therapy (NAC versus surgery alone), MSI status and presence of neutrophilic-rich TMI.

Conclusion

This study contributes to the growing body of evidence of blood-based biomarkers as predictive markers of GC prognosis. A reduction of systemic pro-inflammatory status after surgery (e.g. CRP<1.1md/dl) might contribute to better overall prognostic outcomes of GC patients. Moreover, we observed that GC patients with neutrophilic-rich TMI, a morphologic feature associated with MSI-high status, had lower levels of neutrophils in peripheral blood (low pre-operative NLR). Possible explanations include neutrophil sequestration within the tumour microenvironment and/or consumption by the tumour.

Introduction

Gastric cancer (GC) remains a challenging disease mainly due to its heterogeneity and complex tumour microenvironment[1]. GC has a 5-year survival rate of less than 40%, however, advanced GC has a much lower survival rate of around 5%. Standard treatment for resectable gastric cancer consists of surgical resection in combination with perioperative chemotherapy. Based on the FLOT4 trial, perioperative fluorouracil, leucovorin, oxaliplatin, and docetaxel (FLOT) chemotherapy improved overall survival in locally advanced, resectable esophagogastric adenocarcinoma compared to perioperative ECF/ECX[2]. Despite advances, around 50% of patients do not respond to any of these treatments, and the majority will have recurrence and treatment resistance. By suppressing the immune system and remodelling the extracellular matrix, cancer-associated fibroblasts (CAFs) have been found to promote drug resistance in several tumor types, including GC[3]. Given their high plasticity, the exact role of CAFs in tumor development and treatment resistance in GC remains unknown.

Aims & Methods

This project aims to establish mini-tumour (MT) models that accurately reflect the characteristics of gastric tumours, facilitating the evaluation of the role of stroma upon gastric cancer therapy. The study focuses on two key objectives: (1) evaluating whether CAF subsets in gastric MTs resemble those in parental tumour tissues, ensuring a reflective model for personalized treatment strategies, and (2) assess drug responses in MTs, identifying the most effective therapies, and evaluating CAF-tumor interactions in treatment resistance.
First, we isolate and culture patient-derived tumor organoids and CAFs from gastric cancer resected material, which forms our patient GC biobank. Then, we establish MTs consisting of co-cultured matched patient-derived organoids and CAFs. MT establishment includes mimicking parental tumor tissue tumour-stroma ratio, hotspot mutation data, and specific culture conditions. Once established, treatment assays are designed to study chemotherapy responses (single-drug and combination drugs), as well as CAF subset changes. Untreated and treated MTs are subsequently fixed and stained with cell proliferation, death, and tumor/stroma markers.

Results

Up to date, a total of n=3 MT models have been established, which recapitulate the primary tumour, based on mutation status (e.g. HER2, KRAS, CTNNB1, TP53) , tumor/stroma structures and CAF enrichment. Upon treatment, majority of chemotherapeutics used as single drug showed to be non-effective, and only a few combinations seemed to be clinically relevant (≤ 50% cell viability). Preliminary data shows that CAF sensitivity to chemotherapy differs between patients, particularly between treatment naïve and FLOT-treated, and seems to resemble patient responses.

Conclusion

Overall, MT models display patient-specific features, showcasing interpatient heterogeneity, and highlighting the need for tailored models to optimally evaluate therapy responses. Future experiments will evaluate intrinsic resistance mechanisms potentially associated with specific CAF subsets. To further look into these, spatial profiling of gastric MTs and primary tumours will be evaluated using a stromal 42-marker panel by Imaging Mass Cytometry. To evaluate the immune cell compartment in these MTs, incorporating cytomegalovirus (CMV)-stimulated T cells or patient-derived tumor infiltrating lymphocytes is planned.

References

1. Lei ZN, Teng QX, Tian Q, Chen W, Xie Y, Wu K, Zeng Q, Zeng L, Pan Y, Chen ZS, He Y. Signaling pathways and therapeutic interventions in gastric cancer. Signal Transduct Target Ther. 2022 Oct 8;7(1):358. doi: 10.1038/s41392-022-01190-w.
2. Möhring C, Mańczak A, Timotheou A, et al. Perioperative therapy with FLOT4 significantly increases survival in patients with gastroesophageal and gastric cancer in a large real-world cohort. Int J Cancer. 2023; 153(3): 609-622. doi:10.1002/ijc.34511
3. Ozmen, E., Demir, T. D., & Ozcan, G. (2024). Cancer-associated fibroblasts: protagonists of the tumor microenvironment in gastric cancer. Frontiers in Molecular Biosciences, 11. https://doi.org/10.3389/fmolb.2024.1340124

Disclosure

No conflict of interest.

Introduction

Gastric cancer (GC) remains one of the most lethal malignancies worldwide, with limited treatment success largely due to immune evasion and tumor heterogeneity ¹. The advent of immune checkpoint inhibitors has revolutionized the treatment of solid tumors ²; however, only a subset of GC patients benefits from PD-1/PD-L1 blockade, suggesting the presence of intrinsic mechanisms regulating immune escape ³. Leukemia Inhibitory Factor (LIF), a multifunctional interleukin (IL)-6 cytokine family, plays an essential role in epithelial repair, embryonic stem cell pluripotency, and immune regulation ⁴. In several type of tumors, LIF is emerging as a key mediator of tumor progression and immune modulation. LIF signals through a heterodimeric receptor complex composed of LIF-receptor (LIFR) and GP130 and promotes JAK/STAT3 pathway activation ^5,6. Notably, oncogenic STAT3 signaling has been shown to drive PD-L1 expression and promote immune suppression across multiple tumor types, including breast cancer ⁷ and NK/T-cell lymphoma, supporting the broader relevance of this axis in tumor immune escape ⁸. Although LIF is known to activate JAK/STAT3 signaling ⁴, its role in regulating PD-L1 expression and establishing an immunosuppressive microenvironment in GC is not fully defined.

Aims & Methods

We aimed to dissect the role of LIF in promoting PD-L1 expression and immune escape in GC and to explore the therapeutic potential of LIF inhibition. Neoplastic and non-neoplastic gastric mucosa from 61 GC patients were analyzed by transcriptomic profiling, immunofluorescence (IF), and tSNE analysis. Expression levels of LIF, PD-L1, and key signaling molecules were quantified. In vitro mechanistic studies, employed human gastric cancer cells (MKN45). MKN45 were exposed to recombinant LIF, with or without LRI-305, a novel LIF receptor antagonist. Western blotting, RT-qPCR, IF, and chromatin immunoprecipitation (ChIP) assays were used to evaluate downstream pathway activation and transcriptional regulation of PD-L1.

Results

Transcriptome analysis revealed a significant upregulation of LIF and PD-L1 in tumor samples, which was confirmed at the protein level by Immunofluorescence analysis. Elevated LIF expression correlated with reduced overall survival and an immunosuppressive tumor microenvironment characterized by increased infiltration of PD1⁺ T cells, Tregs, and LIF⁺ TAM. Functional assays demonstrated that LIF stimulation activated the JAK1/STAT3 pathway, leading to enhanced PD-L1 transcription. This was supported by increased phosphorylation of JAK1 and STAT3 and ChIP data confirming STAT3 recruitment to the PD-L1 promoter. LIF also promoted cell proliferation and epithelial-mesenchymal transition (EMT), indicated by upregulation of SNAIL1. Importantly, LRI-305 treatment abolished these effects, downregulating PD-L1 expression, reducing EMT markers, and restoring pro-apoptotic responses. These findings confirmed that LIF sustains tumor growth and play a pivotal role in immune resistance through STAT3-mediated PD-L1 upregulation.

Conclusion

Our data identify the LIF/STAT3/PD-L1 axis as a crucial pathway driving immune evasion in GC. LIF promotes a tolerogenic tumor microenvironment, impairs anti-tumor immunity and correlates with aggressive tumor behavior and poor prognosis. Pharmacological blockade of LIF signaling using LRI-305 effectively reverses these effects, offering a novel combinatorial strategy to enhance the efficacy of immune checkpoint inhibitors in GC patients.

References

1. Smyth, E. C. et al. Gastric cancer. Lancet 396, 635–648 (2020).
2. Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).
3. Fuchs, C. S. et al. Safety and Efficacy of Pembrolizumab Monotherapy in Patients With Previously Treated Advanced Gastric and Gastroesophageal Junction Cancer: Phase 2 Clinical KEYNOTE-059 Trial. JAMA Oncol. 4, e180013 (2018).
4. Di Giorgio, C. et al. Next-Generation Sequencing Analysis of Gastric Cancer Identifies the Leukemia Inhibitory Factor Receptor as a Driving Factor in Gastric Cancer Progression and as a Predictor of Poor Prognosis. Front. Oncol. 12, 939969 (2022).
5. Pascual-García, M. et al. LIF regulates CXCL9 in tumor-associated macrophages and prevents CD8(+) T cell tumor-infiltration impairing anti-PD1 therapy. Nat. Commun. 10, 2416 (2019).
6. Yu, S. et al. Tumor-derived LIF promotes chemoresistance via activating tumor-associated macrophages in gastric cancers. Exp. Cell Res. 406, 112734 (2021).
7. Zerdes, I. et al. STAT3 Activity Promotes Programmed-Death Ligand 1 Expression and Suppresses Immune Responses in Breast Cancer. Cancers (Basel). 11, (2019).
8. Song, T. L. et al. Oncogenic activation of the STAT3 pathway drives PD-L1 expression in natural killer/T-cell lymphoma. Blood 132, 1146–1158 (2018).

Introduction

Gastric cancer (GC) is a major cause of cancer-related death worldwide. Although immune checkpoint inhibitors (ICIs) have improved outcomes in a subset of patients, only 20–30% achieve long-term benefit. Identifying robust biomarkers to predict immunotherapy response remains a critical need. With the rise of digital pathology and deep learning (DL), it is now possible to derive prognostic features from whole-slide images (WSIs), offering a novel means of evaluating the tumor immune microenvironment.

Aims & Methods

This study aimed to develop a Digital Pathology-based Immunotherapy Response Signature (DPIRS) using DL to predict prognosis in GC patients treated with ICIs. We retrospectively analyzed 110 GC patients receiving ICIs at The First Hospital of Jilin University between 2019–2023. H&E-stained WSIs were segmented into 256×256 pixel tiles. A convolutional neural network (ResNet18) was trained to detect immune-relevant histopathological patterns. The DPIRS score for each patient was defined as the proportion of positively scored image tiles. DPIRS was correlated with progression-free survival (PFS), overall survival (OS), immune checkpoint molecule expression, and immune infiltration characteristics.

Results

The DL model showed excellent predictive performance (AUC >0.85) across internal and TCGA validation cohorts. Patients with high DPIRS scores had significantly longer PFS and OS (p<0.01), as well as higher PD-L1 expression and greater tumor-infiltrating lymphocyte (TIL) density. Multivariate Cox regression confirmed DPIRS as an independent prognostic factor for immunotherapy efficacy.

Conclusion

DPIRS is a novel, non-invasive biomarker derived from deep learning-based analysis of digital pathology, enabling precise prediction of immunotherapy response in gastric cancer. This tool holds strong potential for clinical application in personalized treatment strategies.

Introduction

Gastric cancer (GC) ranks as the sixth most common cancer globally and is the fifth leading cause of cancer-related mortality. Previous studies have shown that the five-year survival rate for early-stage GC exceeds 90%. Gastroscopy is the gold standard for GC diagnosis, but its invasive nature and high cost limit its clinical application. Non-invasive tests have been reported in GC prediction, yet current biomarkers fall short in sensitivity or specificity, particularly for early GC prediction, leading to misdiagnosis and potential delays in GC treatment, which may reduce patient survival rates. Multi-omics biomarker panels significantly enhance diagnostic efficacy through multidimensional information complementarity, compared to single-omics or individual biomarkers. Therefore, developing non-invasive multi-omics biomarker combinations holds great promise for enhancing GC diagnostic accuracy and increasing the likelihood of early diagnosis and treatment.

Aims & Methods

This study utilized mass spectrometry-based proteomics and metabolomics to comprehensively analyze the peripheral blood components of non-gastric cancer (NGC, including patients with chronic superficial gastritis and precancerous lesions such as atrophic gastritis, intestinal metaplasia, and dysplasia) and gastric cancer (GC, including patients with well-defined staging from stage IA (T1N0M0) to advanced stages). We employed machine learning algorithms to develop a multi-omics biomarker combination capable of identifying GC and enabling early GC diagnosis.The studies involving humans were approved by the Medical Research Ethics Committee of Peking University Third Hospital.

Results

Through differential analysis of plasma proteins and metabolites in GC (n=53) and NGC (n=41) (FDR<0.05, log2FC>1), we identified 333 upregulated proteins and 570 upregulated metabolites. LASSO regression further narrowed down the candidates to 10 proteins (10-P) and 10 metabolites (10-M) as potential biomarkers. Subsequently, we constructed gastric cancer prediction models using random forest algorithms. In the validation set (n=27), the 10-P Model achieved a prediction accuracy of 0.85 (AUC: 0.91, sensitivity 0.83, specificity 0.86), while the 10-M Model had a prediction accuracy of 0.77 (AUC: 0.82, sensitivity 0.83, specificity 0.77). The Multi-Omics Integrated Model (MOIM), built via machine learning-based Voting Classifier, reached a prediction accuracy of 0.92 (AUC: 0.96, sensitivity 0.91, specificity 0.93), surpassing the former two models. For stage IA GC (n=22) prediction, MOIM's accuracy was 0.93, higher than the 10-P Model's 0.86 and the 10-M Model's 0.79. Finally, we streamlined the model to include only 7 proteins and 3 metabolites, forming the Simplified Multi-Omics Integrated Model (sMOIM), which achieved accuracies of 0.91 for GC and 0.92 for stage IA GC.

Conclusion

The selected plasma protein and metabolite biomarker combination enables highly accurate prediction of GC, especially early-stage GC. Moreover, the multi-omics integrated analysis achieves superior accuracy, sensitivity, and specificity compared to single-omics prediction, suggesting its potential as an effective strategy for the precise early diagnosis of gastric cancer.

Introduction

Gastric Cancer (GC) is one of the most common carcinomas world-wide (1) characterised by a peculiar contribution of the tumour microenvironment to the aggressive biology of this malignancy (2). GC progression is characterised by striking changes in lymphatic vessels (LVs) and lymphatic metastasis is the most common feature of GC spread, representing the most significant prognostic factor determining clinical outcomes (3). Recent publications have demonstrated a functional link between the Extra-Cellular Matrix protein EMILIN-1, tumor growth, and lymphangiogenesis. EMILIN-1 is a key structural and functional element in the maintenance of a competent lymphatic vasculature and, as demonstrated in skin and colitis-associated cancer mouse models, exhibits anti-proliferative and oncosuppressive properties through the interaction with α4β1 integrin (4,5,6).

Aims & Methods

We have observed that EMILIN1 is present at lower levels in human gastric malignant and pre-neoplastic samples and this correlates with the presence of abnormal LVs (unpublished preliminary data). The aim of this study is to investigate the role of EMILIN-1 in gastric tumor onset and progression using mouse models of GC.
We used the syngeneic YTN16 gastric cancer cell line (7) in genetically modified EMILIN-1 mice (KO, knock out mice; E933A KI, E933A-mutated Emilin1 Knock-In animals expressing an EMILIN-1 mutant that is unable to interact with α4/α9β1 integrins). KI animals were included to specifically define the effects related to integrin engagement. Cells were injected intraperitoneally (i.p.), sub-cutaneously (s.c.) or into the footpad of 8-week-old C57BL/6 mice; tumors were analyzed after 3 weeks. A protocol using N-methyl-N-nitrosourea (MNU) was also performed on the same animal models to better investigate the role of EMILIN-1 when a chemically-induced gastric carcinogenesis approach was used. Mice were given drinking water ad libitum containing 240 ppm MNU on alternate weeks for a total exposure of 5 weeks and sacrificed after 30 weeks.

Results

To assess tumor growth, mice were injected with the syngeneic GC cell line YTN16 subcutaneously into both flanks. KO and E933A KI mice developed larger and earlier tumors; tumor samples were stained with Lyve-1 and CD31 to identify lymphatic and blood vessels, respectively. While no differences were observed for CD31-positive structures, a statistically significant increase in intratumoral lymphangiogenesis was observed in KO and E933A KI animals. Lymphatic spread was analyzed in detail by means of intra-footpad injection; enhanced lymph node (LN) metastasis of YTN16 transplanted tumors was observed in KO and E933A KI mice. In a model of peritoneal carcinomatosis and dissemination (by intra-peritoneal injection of YTN16), EMILIN-1 mutant animals also had higher histopathologic scores for invasion;
The use of the MNU protocol, that represents an interesting approach to investigate how deficiency of the ECM molecule EMILIN-1 may affect the incidence and frequency of tumour development, further showed that KO mice had higher tumour incidence, bigger adenomas and lower survival after MNU treatment. Similar results were obtained with the E933A KI model.

Conclusion

These results clearly demonstrate that EMILIN-1, exerts a pivotal role in GC onset, progression and dissemination through the involvement of α4/α9β1 integrin. Loss of EMILIN-1 may lead to alterations in the microenvironment able to promote tumorigenesis in the stomach. Our study also provides a novel animal model that, for the first time, targets a component of the gastric tumor microenvironment.

References

(1) Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet Lond Engl. 2020;396:635–48.
(2) Rojas A, Araya P, Gonzalez I, Morales E. Gastric Tumor Microenvironment.Adv Exp Med Biol. 2020;1226:23–35.8
(3) Deng J-Y, Liang H. Clinical significance of lymph node metastasis in gastric cancer. World J Gastroenterol. 2014;20:3967– 75.
(4) Capuano A, Pivetta E, Sartori G, Bosisio G, Favero A, Cover E, et al.Abrogation of EMILIN1-β1 integrin interaction promotes experimental colitis and colon carcinogenesis.Matrix Biol J Int Soc Matrix Biol. 2019;83:97–115.
(5) Capuano A, Pivetta E, Baldissera F, Bosisio G, Wassermann B, Bucciotti F, et al.Integrin binding site within the gC1q domain orchestrates EMILIN-1-induced lymphangiogenesis. Matrix Biol J Int Soc Matrix Biol. 2019;81:34–49.
(6) Danussi C, Petrucco A, Wassermann B, Pivetta E, Modica TM, Del Bel Belluz L, et al.EMILIN1-α4/α9 integrin interaction inhibits dermal fibroblast and keratinocyte proliferation. JCell Biol. 2011;195:131–45.
(7) Yamamoto M, Nomura S, Hosoi A, Nagaoka K, Iino T, Yasuda T, Saito T, Matsushita H, Uchida E, et al. Established gastric cancer cell lines transplantable into C57BL/6 mice show fibroblast growth factor receptor 4 promotion of tumor growth. Cancer Sci. 2018 May;109(5):1480-1492