CXCL8 is a potential biomarker for predicting disease progression in gastric carcinoma

Identification of angiogenesis-related subtypes and risk models for predicting the prognosis of gastric cancer patients

Gastric cancer (GC) is a leading cause of cancer-related mortality and is characterized by significant heterogeneity, highlighting the need for further studies aimed at personalized treatment strategies. Tumor angiogenesis is critical for tumor development and metastasis, yet its role in molecular subtyping and prognosis prediction remains underexplored. This study aims to identify angiogenesis-related subtypes and develop a prognostic model for GC patients. Using data from The Cancer Genome Atlas (TCGA), we performed consensus cluster analysis on differentially expressed angiogenesis-related genes (ARGs), identifying two patient subtypes with distinct survival outcomes. Differentially expressed genes between the subtypes were analyzed via Cox and LASSO regression, leading to the establishment of a subtype-based prognostic model using a machine learning algorithm. Patients were classified into high- and low-risk groups based on the risk score. Validation was performed using independent datasets (ICGC and GSE15459). We utilized a deconvolution algorithm to investigate the tumor immune microenvironment in different risk groups and conducted analyses on genetic profiling, sensitivity and combination of anti-tumor drug. Our study identified ten prognostic signature genes, enabling the calculation of a risk score to predict prognosis and overall survival. This provides critical data for stratified diagnosis and treatment upon patient admission, monitoring disease progression throughout the entire course, evaluating immunotherapy efficacy, and selecting personalized medications for GC patients.

Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer

OBJECTIVES

The necessity to develop a method for prognostication and to identify novel biomarkers for personalized medicine in patients with head and neck squamous cell carcinoma (HNSCC) cannot be overstated. Recently, pathomics, which relies on quantitative analysis of medical imaging, has come to the forefront. CXCL8, an essential inflammatory cytokine, has been shown to correlate with overall survival (OS). This study examined the relationship between CXCL8 mRNA expression and pathomics features and aimed to explore the biological underpinnings of CXCL8.

METHODS

Clinical information and transcripts per million mRNA sequencing data were obtained from The Cancer Genome Atlas (TCGA)-HNSCC dataset. We identified correlations between CXCL8 mRNA expression and patient survival rates using a Kaplan-Meier survival curve. A retrospective analysis of 313 samples diagnosed with HNSCC in the TCGA database was conducted. Pathomics features were extracted from hematoxylin and eosin-stained images, and then the minimum redundancy maximum relevance, with recursive feature elimination (mRMR-RFE) method was applied, followed by screening with the logistic regression algorithm.

RESULTS

Kaplan-Meier curves indicated that high expression of CXCL8 was significantly associated with decreased OS. The logistic regression pathomics model incorporated 16 radiomics features identified by the mRMR-RFE method in the training set and demonstrated strong performance in the testing set. Calibration plots showed that the probability of high gene expression predicted by the pathomics model was in good agreement with actual observations, suggesting the model's high clinical applicability.

CONCLUSION

The pathomics model of CXCL8 mRNA expression serves as an effective tool for predicting prognosis in patients with HNSCC and can aid in clinical decision-making. Elevated levels of CXCL8 expression may lead to reduced DNA damage and are associated with a pro-inflammatory tumor microenvironment, offering a potential therapeutic target.

CXCL8 and the peritoneal metastasis of ovarian and gastric cancer

CXCL8 is the most representative chemokine produced autocrine or paracrine by tumor cells, endothelial cells and lymphocytes. It can play a key role in normal tissues and tumors by activating PI3K-Akt, PLC, JAK-STAT, and other signaling pathways after combining with CXCR1/2. The incidence of peritoneal metastasis in ovarian and gastric cancer is extremely high. The structure of the peritoneum and various peritoneal-related cells supports the peritoneal metastasis of cancers, which readily produces a poor prognosis, low 5-year survival rate, and the death of patients. Studies show that CXCL8 is excessively secreted in a variety of cancers. Thus, this paper will further elaborate on the mechanism of CXCL8 and the peritoneal metastasis of ovarian and gastric cancer to provide a theoretical basis for the proposal of new methods for the prevention, diagnosis, and treatment of cancer peritoneal metastasis.

CXCL2 acts as a prognostic biomarker and associated with immune infiltrates in stomach adenocarcinoma

BACKGROUND

STAD ranked 5th most common in the incidence of malignant tumors and 3rd most common in the death rate of cancer worldwide. CXC chemokines affect the biological progress of various tumors, resulting in therapeutic failure. The role of CXCL2 in STAD was still a mystery.

METHODS

The expression, prognostic value, and clinical function of CXCL2 were analyzed using several online bioinformatics tools and clinical tissues.

RESULTS

CXCL2 level was significantly upregulated in STAD tissues. Strong correlation was obtained between CXCL2 level and immune cells as well as immune biomarkers. High CXCL2 expression in STAD was correlated with a favorable prognosis. Further analysis revealed that CXCL2, pTNM stage and age were independent factors affecting the prognosis of STAD patients. A predictive nomogram indicated that the calibration plots for the 1-year, 3-year and 5-year OS rates were predicted relatively well compared with an ideal model in the entire cohort. Validation analysis revealed that CXCL2 expression was upregulated in STAD and high CXCL2 level had a better overall survival. CXCL2 was associated with resistance to numerous drugs or small molecules in STAD.

CONCLUSIONS

We identified CXCL2 as a novel therapeutic target and associated with immune infiltration in STAD.

Targeting IL8 as a sequential therapy strategy to overcome chemotherapy resistance in advanced gastric cancer

Systemic chemotherapy with multiple drug regimens is the main therapy option for advanced gastric cancer (GC) patients. However, many patients develop relapse soon. Here, we evaluated the therapeutic potential of targeting interleukin-8 (IL8) to overcome resistance to chemotherapy in advanced GC. RNA sequencing revealed crucial molecular changes after chemotherapy resistance, in which the expression of IL8 was significantly activated with the increase in drug resistance. Subsequently, the clinical significance of IL8 expression was determined in GC population specimens. IL8-targeted by RNA interference or reparixin reversed chemotherapy resistance with limited toxicity in vivo and vitro experiments. Sequential treatment with first-line, second-line chemotherapy and reparixin inhibited GC growth, reduced toxicity and prolonged survival. Collectively, our study provides a therapeutic strategy that targeting IL8 as a sequential therapy after chemotherapy resistance in advanced GC.

Chemokine-targeted therapies: An opportunity to remodel immune profiles in gastro-oesophageal tumours

Immunotherapies are transforming outcomes for many cancer patients and are quickly becoming the fourth pillar of cancer therapy. However, their efficacy of only ∼25% in gastro-oesophageal cancer has been disappointing. This is attributed to factors such as insufficient patient stratification and the pro-tumourigenic immune landscape of gastro-oesophageal tumours. The chemokine profiles of solid tumours and the availability of effector immune cells greatly influence the immune infiltrate, producing 'cold' or 'immune-excluded' tumours in which immunotherapies are unable to reinvigorate the immune response. Other biological functions for chemokines have emerged, such as promoting cell survival, polarising T cell responses, and supporting several hallmarks of cancer. Therefore, chemokine networks may be exploited with therapeutic intent to mobilise and polarise anti-tumour immune cells, with further utility as combination treatments to augment the efficacy of current cancer immunotherapies. Few studies have demonstrated the clinical benefit of chemokine-targeted therapies as monotherapies, and this review proposes their consideration as combination treatments. Herein, we explore the anti-tumour and pro-tumour implications of chemokine signalling in gastro-oesophageal cancer and discuss their value as prognostic and predictive biomarkers in response to treatment.