CCL24 Signaling in the Tumor Microenvironment

The Role of CCL24 in Systemic Sclerosis

Systemic sclerosis (SSc) is a chronic immune-mediated disease characterized by microangiopathy, immune dysregulation, and progressive fibrosis of the skin and internal organs. Though not fully understood, the pathogenesis of SSc is dominated by microvascular injury, endothelial dysregulation, and immune response that are thought to be associated with fibroblast activation and related fibrogenesis. Among the main clinical subsets, diffuse SSc (dSSc) is a progressive form with rapid and disseminated skin thickening accompanied by internal organ fibrosis and dysfunction. Despite recent advances and multiple randomized clinical trials in early dSSc patients, an effective disease-modifying treatment for progressive skin fibrosis is still missing, and there is a crucial need to identify new targets for therapeutic intervention. Eotaxin-2 (CCL24) is a chemokine secreted by immune cells and epithelial cells, which promotes trafficking of immune cells and activation of pro-fibrotic cells through CCR3 receptor binding. Higher levels of CCL24 and CCR3 were found in the skin and sera of patients with SSc compared with healthy controls; elevated levels of CCL24 and CCR3 were associated with fibrosis and predictive of greater lung function deterioration. Growing evidence supports the potency of a CCL24-blocking antibody as an anti-inflammatory and anti-fibrotic modulating agent in multiple preclinical models that involve liver, skin, and lung inflammation and fibrosis. This review highlights the role of CCL24 in orchestrating immune, vascular, and fibrotic pathways, and the potential of CCL24 inhibition as a novel treatment for SSc.

A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data

Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and data integration techniques are powerful tools to achieve this goal. This study aimed to introduce a machine learning method to integrate three types of biological data, and investigate the performance of two other methods, in identifying the survival dependency of patients. The data included TCGA RNA-seq gene expression, DNA methylation, and clinical data from 368 patients with colon cancer also we use an independent external validation data set, containing 232 samples. Three methods including, hyper-parameter optimized autoencoders (HPOAE), normal autoencoder, and penalized principal component analysis (PPCA) were used for simultaneous data integration and estimation under a COX hazards model. The HPOAE was thought to outperform other methods. The HPOAE had the Log Rank Mantel-Cox value of 14.27 ± 2, and a Breslow-Generalized Wilcoxon value of 13.13 ± 1. Ten miRNA, 11 methylated genes, and 28 mRNA all by (importance of marginal cutoff > 0.95) were identified. The study demonstrated that hsa-miR-485-5p targets both ZMYM1 and tp53, the latter of which has been previously associated with cancer in numerous studies. Furthermore, compared to other methods, the HPOAE exhibited a greater capacity for identifying survival subgroups and the genes associated with them in patients with colon cancer. However, all of the results were obtained by computational methods, and clinical and experimental studies are needed to validate these results.

Novel chemokine related LncRNA signature correlates with the prognosis, immune landscape, and therapeutic sensitivity of esophageal squamous cell cancer

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is closely correlated with malignant biological characteristics and poor survival. Recently, chemokines have been reported to be involved in the progression of tumors, and they can also regulate the tumor microenvironment. However, it is unclear whether chemokine-related long noncoding RNAs (lncRNAs) affect the prognosis of ESCC.

METHODS

We downloaded RNA-seq and clinical data from the Gene Expression Omnibus (GEO database. Chemokine-related lncRNAs were screened by differential analysis and Pearson correlation analysis. Then, prognosis-related lncRNAs were screened by using univariate COX regression, and risk models were constructed after the least absolute shrinkage and selection operator (LASSO) regression and multivariate COX regression. The predictive value of the signature was assessed using Kaplan-Meier test, time-dependent receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and calibration curve. Moreover, a nomogram to predict patients' 1-year 3-year and 5-year prognosis was constructed. Gene set enrichment analyses (GSEA), Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG), evaluation of immune cell infiltration, and estimation of drug sensitivity were also conducted.

RESULTS

In this study, 677 chemokine-related lncRNAs were first obtained by differential analysis and Pearson correlation. Then, six chemokine-related lncRNAs were obtained by using univariate COX, LASSO and multivariate COX to construct a novel chemokine-related lncRNAs risk model. The signature manifested favorable predictive validity and accuracy both in the testing and training cohorts. The chemokine-related signature could classify ESCC patients into two risk groups well, which indicated that high-risk group exhibited poor prognostic outcome. In addition, this risk model played an important role in predicting signaling pathways, immune cell infiltration, stromal score, and drug sensitivity in ESCC patients.

CONCLUSIONS

These findings elucidated the critical role of novel prognostic chemokine-related lncRNAs in prognosis, immune landscape, and drug therapy, thus throwing light on prognostic evaluation and therapeutic targets for ESCC patients.

A novel immune score model predicting the prognosis and immunotherapy response of breast cancer

Breast cancer (BC) is one of the most common malignancies. However, the existing pathological grading system cannot accurately and effectively predict the survival rate and immune checkpoint treatment response of BC patients. In this study, based on The Cancer Genome Atlas (TCGA) database, a total of 7 immune-related genes (IRGs) were screened out to construct a prognostic model. Subsequently, the clinical prognosis, pathological characteristics, cancer-immunity cycle, tumour immune dysfunction and exclusion (TIDE) score, and immune checkpoint inhibitor (ICI) response were compared between the high- and low-risk groups. In addition, we determined the potential regulatory effect of NPR3 on BC cell proliferation, migration, and apoptosis. The model consisting of 7 IRGs was an independent prognostic factor. Patients with lower risk scores exhibited longer survival times. Moreover, the expression of NPR3 was increased but the expression of PD-1, PD-L1, and CTLA-4 was decreased in the high-risk group compared to the low-risk group. In addition, compared with si-NC, si-NPR3 suppressed proliferation and migration but promoted apoptosis in both MDA-MB-231 and MCF-7 cells. This study presents a model for predicting survival outcomes and provides a strategy to guide effective personalized immunotherapy in BC patients.

A Novel Prognostic Chemokine-Related lncRNAs Signature Associated with Immune Landscape in Colon Adenocarcinoma

Chemokines have been reported to be involved in tumorigenesis and progression and can also modulate the tumor microenvironment. However, it is still unclear whether chemokine-related long noncoding RNAs (lncRNAs) can affect the prognosis of colon adenocarcinoma (COAD). We summarized chemokine-related genes and downloaded RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) database. A total of 52 prognostic chemokine-related lncRNAs were screened by univariate Cox regression analysis; patients were grouped according to cluster analysis results. Lasso regression analysis was applied to determine chemokine-related lncRNAs to construct a risk model for further research. This study first investigated the differences between the prognosis and immune status of two chemokine-related lncRNAs clusters by consensus clustering. Then, using various algorithms, we obtained ten chemokine-related lncRNAs to construct a new prognostic chemokine-related lncRNAs risk model. The risk model's predictive efficiency, validity, and accuracy were further validated and determined in the test and training cohorts. Furthermore, this risk model played a vital role in predicting immune cell infiltration, immune checkpoint gene expression, tumor mutational burden (TMB), immunotherapy score, and drug sensitivity in COAD patients. These findings elucidated the critical role of novel prognostic chemokine-related lncRNAs in prognosis, immune landscape, and drug therapy, thereby providing valuable insights for prognosis assessment and personalized treatment strategies for COAD patients.

Advances in Research on the Effects and Mechanisms of Chemokines and Their Receptors in Cancer

Cancer is a common and intractable disease that seriously affects quality of life of patients and imposes heavy economic burden on families and the entire society. Current medications and intervention strategies for cancer have respective shortcomings. In recent years, it has been increasingly spotlighted that chemokines and their receptors play vital roles in the pathophysiology of cancer. Chemokines are a class of structurally similar short-chain secreted proteins that initiate intracellular signaling pathways through the activation of corresponding G protein-coupled receptors and participate in physiological and pathological processes such as cell migration and proliferation. Studies have shown that chemokines and their receptors have close relationships with cancer epigenetic regulation, growth, progression, invasion, metastasis, and angiogenesis. Chemokines and their receptors may also serve as potential targets for cancer treatment. We herein summarize recent research progresses on anti-tumor effects and mechanisms of chemokines and their receptors, suggesting avenues for future studies. Perspectives for upcoming explorations, such as development of multi-targeted chemokine-based anti-tumor drugs, are also discussed in the present review.

Systemic Evaluation of the Effect of Diabetes Mellitus on Breast Cancer in a Mouse Model

Breast cancer complicated with diabetes mellitus (DM) is a common disease. To evaluate the effect of preexisting DM on breast cancer progression without drug interference, we used a streptozotocin (STZ)-induced type 2 diabetes mellitus BALB/c mouse model. We found that 4T1 breast cancer complicated with DM decreased the mouse survival time compared with 4T1-bearing mice. The diversity of gut microbiome was affected by DM. The infiltration of mucosal-associated invariant T cell (MAIT), CD8+ T cell, and CD4+ T cell in the tumor was significantly decreased in the DM-4T1 group compared with the 4T1 group. The transcriptome data of tumor tissues indicated that the expressions of inflammatory C–C chemokine- and metabolism-related genes were greatly changed. The abnormal expression of these genes may be related with the decreased T-cell infiltration in DM-4T1. In conclusion, the gut microbiome and tumor microenvironment of diabetic breast cancer patients have unique features. The effect of diabetes on breast cancer should be considered in the treatment for diabetic breast cancer patients.