Proteomic profiling of gastric cancer with peritoneal metastasis identifies a protein signature associated with immune microenvironment and patient outcome

Extracellular vesicles: messengers of cross-talk between gastric cancer cells and the tumor microenvironment

Gastric cancer is a common malignancy characterized by an insidious onset and high mortality rate. Exosomes, a special type of extracellular vesicle, contain various bioactive molecules and have been found to play crucial roles in maintaining normal physiological functions and homeostasis in the body. Recent research has shown that the contents of exosome play a significant role in the progression and metastasis of gastric cancer through communication and regulatory functions. These mechanisms involve promoting gastric cancer cell proliferation and drug resistance. Additionally, other cells in the gastric cancer microenvironment can regulate the progression of gastric cancer through exosomes. These include exosomes derived from fibroblasts and immune cells, which modulate gastric cancer cells. Therefore, in this review, we provide a brief overview of recent advances in the contents and occurrence mechanisms of exosome. This review specifically focused on the regulatory mechanisms of exosomes derived from gastric cancer and other cellular subtypes in the tumor microenvironment. Subsequently, we summarize the latest research progress on the use of exosomes in liquid biopsy, discussing the potential of gastric cancer exosomes in clinical applications.

Comprehensive proteomic analysis and multidimensional model construction of peritoneal metastasis in gastric cancer

Peritoneal metastasis following gastric cancer surgery is often associated with a poor prognosis. This study aimed to investigate the mechanisms underlying peritoneal metastasis and to develop a predictive model for the risk of postoperative peritoneal metastases in gastric cancer. We performed a comprehensive analysis of the protein mass spectra and tumor microenvironment in paraffin-embedded primary tumor sections from gastric cancer patients, both with and without postoperative peritoneal metastases. Using proteomic profiling, we identified 9595 proteins and stratified patients into three distinct proteomic subgroups (Pro1, Pro2, Pro3) based on differential protein expression. Simultaneously, immune cell profiling allowed us to classify patients into four immune subgroups (IG-I, IG-II, IG-III, IG-IV). The relationships between these proteomic, immune, and metastasis classifications were further explored to uncover potential associations and mechanisms driving metastasis. Building on these insights, we developed an integrative model combining proteomics, immunological, and radiomics data for predicting postoperative peritoneal metastases. This model demonstrated high predictive efficacy, offering a robust tool for identifying high-risk patients. Our findings provide a deeper understanding of the biological processes underlying peritoneal metastasis in gastric cancer, highlighting the interplay between proteomic and immune factors. By establishing novel patient subgroups and an effective prediction model, this study lays the groundwork for early diagnosis and tailored therapeutic strategies to improve outcomes for gastric cancer patients.

Sialic acid metabolism-based classification reveals novel metabolic subtypes with distinct characteristics of tumor microenvironment and clinical outcomes in gastric cancer

BACKGROUND

High heterogeneity in gastric cancer (GC) remains a challenge for standard treatments and prognosis prediction. Dysregulation of sialic acid metabolism (SiaM) is recognized as a key metabolic hallmark of tumor immune evasion and metastasis. Herein, we aimed to develop a SiaM-based metabolic classification in GC.

METHODS

SiaM-related genes were obtained from the MsigDB database. Bulk and single-cell transcriptional data of 956 GC patients were acquired from the GEO, TCGA, and MEDLINE databases. Proteomic profiles of 20 GC samples were derived from our institution. The consensus clustering algorithm was applied to identify SiaM-based clusters. The SiaM-based model was established via LASSO regression and evaluated via Kaplan‒Meier curve and ROC curve analyses. In vitro and in vivo experiments were conducted to explore the function of ST3GAL1 in GC.

RESULTS

Three SiaM clusters presented distinct patterns of clinicopathological features, transcriptomic alterations, and tumor immune microenvironment landscapes in GC. Compared with clusters A and B, cluster C presented elevated SiaM activity, higher metastatic potential, more abundant immunosuppressive features, and a worse prognosis. Based on the differentially expressed genes between these clusters, a risk model for six genes (ARHGAP6, ST3GAL1, ADAM28, C7, PLCL1, and TTC28) was then constructed. The model exhibited robust performance in predicting peritoneal metastasis and prognosis in four independent cohorts. As a hub gene in the model, ST3GAL1 promoted GC cell migration and invasion in vitro and in vivo.

CONCLUSIONS

Our study proposed a novel SiaM-based classification that identified three metabolic subtypes with distinct characteristics of tumor microenvironment and clinical outcomes in GC.

Tumor-Stroma Ratio is a Critical Indicator of Peritoneal Metastasis in Gastric Cancer

Objective

This study aims to investigate the correlation between the tumor-stroma ratio (TSR) and peritoneal metastasis (PM) in gastric cancer (GC) and constructs a diagnostic model based on preoperative examination data.

Methods

To determine the feasibility of obtaining TSR in GC patients through preoperative examinations, the consistency of TSR between endoscopic biopsy tissues and postoperative histopathological tissues was evaluated. Additionally, the correlation between TSR and PM in GC was analyzed using Gene Expression Omnibus (GEO) datasets. To validate TSR's clinical potential in diagnosing PM, 640 GC patients from two medical centers were enrolled. A training cohort of 330 patients evaluated TSR and synchronous PM correlation, and a validation cohort of 310 patients was used. An additional cohort of 510 patients was established to investigate TSR and metachronous PM. A diagnostic model based on preoperative data was developed and a nomogram constructed.

Results

The TSR shows good consistency between endoscopic biopsy tissues and postoperative histopathological tissues. A significant correlation between TSR and PM was observed. The TSR-based model, combined with CA125, CA724 and Borrmann type, exhibited strong diagnostic effectiveness and considerable predictive efficacy, with an Area Under the Curve (AUC) of 0.85 in the training cohort, 0.73 in the external validation cohort, and 0.72 in the metachronous PM cohort.

Conclusion

The TSR emerges as a crucial marker for PM in GC, with the developed model, based on TSR and preoperative examination data, demonstrating substantial diagnostic and predictive capabilities.

Transcriptional landscape and predictive potential of long noncoding RNAs in peritoneal recurrence of gastric cancer

BACKGROUND

Long noncoding RNAs (lncRNAs) play a critical role in gastric cancer (GC) progression and metastasis. However, research comprehensively exploring tissue-derived lncRNAs for predicting peritoneal recurrence in patients with GC remains limited. This study aims to investigate the transcriptional landscape of lncRNAs in GC with peritoneal metastasis (PM) and to develop an integrated lncRNA-based score to predict peritoneal recurrence in patients with GC after radical gastrectomy.

METHODS

We analyzed the transcriptome profile of lncRNAs in paired peritoneal, primary gastric tumor, and normal tissue specimens from 12 patients with GC in the Sun Yat-sen University Cancer Center (SYSUCC) discovery cohort. Key lncRNAs were identified via interactive analysis with the TCGA database and SYSUCC validation cohort. A score model was constructed using the LASSO regression model and nomogram COX regression and evaluated using receiver operating characteristic curves. The role of lncRNAs in the PM of GC was then examined through wound healing, Transwell, 3D multicellular tumor spheroid invasion, and peritoneal cavity xenograft tumorigenicity assays in mice.

RESULT

Five essential lncRNAs were screened and incorporated into the PM risk score to predict peritoneal recurrence-free survival (pRFS). We developed a comprehensive, integrated nomogram score, including the PM risk score, pT, pN, and tumor size, which could effectively predict the 5-year pRFS with an Area under the curve of 0.79 (95% CI: 0.71-0.88). Subsequently, we demonstrated that one of these lncRNAs, CASC15, promoted the invasion and migration of GC cells in vitro and facilitated the PM of GC cells in vivo, initially verifying that lncRNAs contribute to the PM of GC. Mechanistic analysis demonstrated that CASC15 promoted EMT and metastasis by activating the JNK and p38 pathways.

CONCLUSION

A lncRNA-based integrated score was constructed in this study to predict peritoneal recurrence in patients clinically.

Construction and validation of the prognostic nomogram model for patients with diffuse-type gastric cancer based on the SEER database

OBJECTIVE

The prognostic factors of diffuse GC patients were screened the prognostic nomogram was constructed, and the prediction accuracy was verified.

METHODS

From 2006 to 2018, there were 2877 individuals pathologically diagnosed with diffuse gastric cancer; the clinicopathological features of these patients were obtained from the SEER database & randomly divided into a training cohort (1439) & validation cohort (1438).To create prognostic nomograms & choose independent prognostic indicators to predict the overall survival (OS) of 1, 3, & 5 years, log-rank & multivariate COX analysis were utilized & discrimination ability of nomogram prediction using consistency index and calibration curve.

RESULTS

Age, T, N, M, TNM, surgical status, chemotherapy status, & all seven markers were independent predictors of OS (P < 0.05), & a nomogram of OS at 1, 3, & 5 years was created using these independent predictors. The nomogram's c-index was 0.750 (95% CI 0.734 ~ 0.766), greater than the TNM staging framework 0.658 (95%CI 0.639 ~ 0.677); the c-index was 0.753 (95% CI 0.737 ~ 0.769) as well as superior to the TNM staging mechanism 0.679 (95% CI 0.503-0.697). According to the calibration curve, the projected survival rate using the nomogram & the actual survival rate are in good agreement.

CONCLUSIONS

Prognostic nomograms are useful tools for physicians to assess every individual's individualised prognosis & create treatment strategies for those with diffuse gastric cancer. They can reliably predict the prognosis for individuals with diffuse gastrointestinal carcinoma.

Genomic and immune microenvironment features influencing chemoimmunotherapy response in gastric cancer with peritoneal metastasis: a retrospective cohort study

BACKGROUND

Patients with peritoneal metastasis (PM) from gastric cancer (GC) exhibit poor prognosis. Chemoimmunotherapy offers promising clinical benefits; however, its efficacy and predictive biomarkers in a conversion therapy setting remain unclear. The authors aimed to retrospectively evaluate chemoimmunotherapy efficacy in a conversion therapy setting for GC patients with PM and establish a prediction model for assessing clinical benefits.

MATERIALS AND METHODS

A retrospective evaluation of clinical outcomes encompassed 55 GC patients with PM who underwent chemoimmunotherapy in a conversion therapy setting. Baseline PM specimens were collected for genomic and transcriptomic profiling. Clinicopathological factors, gene signatures, and tumor immune microenvironment were evaluated to identify predictive markers and develop a prediction model.

RESULTS

Chemoimmunotherapy achieved a 41.8% objective response rate and 72.4% R0 resection rate in GC patients with PM. Patients with conversion surgery showed better overall survival (OS) than those without the surgery (median OS: not reached vs 7.82 m, P <0.0001). Responders to chemoimmunotherapy showed higher ERBB2 and ERBB3 mutation frequencies, CTLA4 and HLA-DQB1 expression, and CD8+ T cell infiltration, but lower CDH1 mutation and naïve CD4+ T cell infiltration, compared to nonresponders. A prediction model was established integrating CDH1 and ERBB3 mutations, HLA-DQB1 expression, and naïve CD4+ T cell infiltration (AUC=0.918), which were further tested using an independent external cohort (AUC=0.785).

CONCLUSION

This exploratory study comprehensively evaluated clinicopathological, genomic, and immune features and developed a novel prediction model, providing a rational basis for the selection of GC patients with PM for chemoimmunotherapy-involved conversion therapy.

Decoding the secrets of small extracellular vesicle communications: exploring the inhibition of vesicle-associated pathways and interception strategies for cancer treatment

Cancer disease is the second leading cause of death worldwide. In 2023, about 2 million new cancer cases and 609,820 cancer deaths are projected to occur in the United States. The driving forces of cancer progression and metastasis are widely varied and comprise multifactorial events. Although there is significant success in treating cancer, patients still present with tumors at advanced stages. Therefore, the discovery of novel oncologic pathways has been widely developed. Tumor cells communicate with each other through small extracellular vesicles (sEVs), which contribute to tumor-stromal interaction and promote tumor growth and metastasis. sEV-specific inhibitors are being investigated as a next-generation cancer therapy. A literature search was conducted to discuss different options for targeting sEV pathways in cancer cells. However, there are some challenges that need to be addressed in targeting sEVs: i) specificity and toxicity of sEV inhibitor, ii) targeted delivery of sEV inhibitors, iii) combination of sEV inhibitors with current standard chemotherapy to improve patients' clinical outcomes, and iv) data reproducibility and applicability at distinct levels of the disease. Despite these challenges, sEV inhibitors have immense potential for effectively treating cancer patients.

Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis

Peritoneal carcinomatosis (PC) is a type of secondary cancer which is not sensitive to conventional intravenous chemotherapy. Treatment strategies for PC are usually palliative rather than curative. Recently, artificial intelligence (AI) has been widely used in the medical field, making the early diagnosis, individualized treatment, and accurate prognostic evaluation of various cancers, including mediastinal malignancies, colorectal cancer, lung cancer more feasible. As a branch of computer science, AI specializes in image recognition, speech recognition, automatic large-scale data extraction and output. AI technologies have also made breakthrough progress in the field of peritoneal carcinomatosis (PC) based on its powerful learning capacity and efficient computational power. AI has been successfully applied in various approaches in PC diagnosis, including imaging, blood tests, proteomics, and pathological diagnosis. Due to the automatic extraction function of the convolutional neural network and the learning model based on machine learning algorithms, AI-assisted diagnosis types are associated with a higher accuracy rate compared to conventional diagnosis methods. In addition, AI is also used in the treatment of peritoneal cancer, including surgical resection, intraperitoneal chemotherapy, systemic chemotherapy, which significantly improves the survival of patients with PC. In particular, the recurrence prediction and emotion evaluation of PC patients are also combined with AI technology, further improving the quality of life of patients. Here we have comprehensively reviewed and summarized the latest developments in the application of AI in PC, helping oncologists to comprehensively diagnose PC and provide more precise treatment strategies for patients with PC.

Genomic characterization of peritoneal lavage cytology-positive gastric cancer

Objective

Positive peritoneal lavege cytology (CY1) gastric cancer is featured by dismal prognosis, with high risks of peritoneal metastasis. However, there is a lack of evidence on pathogenic mechanism and signature of CY1 and there is a continuous debate on CY1 therapy. Therefore, exploring the mechanism of CY1 is crucial for treatment strategies and targets for CY1 gastric cancer.

Methods

In order to figure out specific driver genes and marker genes of CY1 gastric cancer, and ultimately offer clues for potential marker and risk assessment of CY1, 17 cytology-positive gastric cancer patients and 31 matched cytology-negative gastric cancer patients were enrolled in this study. The enrollment criteria were based on the results of diagnostic laparoscopy staging and cytology inspection of exfoliated cells. Whole exome sequencing was then performed on tumor samples to evaluate genomic characterization of cytology-positive gastric cancer.

Results

Least absolute shrinkage and selection operator (LASSO) algorithm identified 43 cytology-positive marker genes, while MutSigCV identified 42 cytology-positive specific driver genes. CD3G and CDKL2 were both driver and marker genes of CY1. Regarding mutational signatures, driver gene mutation and tumor subclone architecture, no significant differences were observed between CY1 and negative peritoneal lavege cytology (CY0).

Conclusions

There might not be distinct differences between CY1 and CY0, and CY1 might represent the progression of CY0 gastric cancer rather than constituting an independent subtype. This genomic analysis will thus provide key molecular insights into CY1, which may have a direct effect on treatment recommendations for CY1 and CY0 patients, and provides opportunities for genome-guided clinical trials and drug development.

Plasma Exosome Proteins ILK1 and CD14 Correlated with Organ-Specific Metastasis in Advanced Gastric Cancer Patients

The exosome plays important roles in driving tumor metastasis, while the role of exosome proteins during organ-specific metastasis in gastric cancer has not been fully understood. To address this question, peripheral blood samples from 12 AGC patients with organ-specific metastasis, including distant lymphatic, hepatic and peritoneal metastasis, were collected to purify exosomes and to detect exosome proteins by Nano-HPLC-MS/MS. Gastric cancer cell lines were used for in vitro experiments. Peripheral blood sample and ascites sample from one patient were further analyzed by single-cell RNA sequencing. GO and KEGG enrichment analysis showed different expression proteins of hepatic metastasis were correlated with lipid metabolism. For peritoneal metastasis, actin cytoskeleton regulation and glycolysis/gluconeogenesis could be enriched. ILK1 and CD14 were correlated with hepatic and peritoneal metastasis, respectively. Overexpression of CD14 and ILK1 impacted the colony formation ability of gastric cancer and increased expression of Vimentin. CD14 derived from immune cells in malignant ascites correlated with high activation of chemokine- and cytokine-mediated signaling pathways. In summary, biological functions of plasma exosome proteins among AGC patients with different metastatic modes were distinct, in which ILK1 and CD14 were correlated with organ-specific metastasis.