Identification of the novel therapeutic targets and biomarkers associated of prostate cancer with cancer-associated fibroblasts (CAFs)

Identification and Validation of Four Serum Biomarkers With Optimal Diagnostic and Prognostic Potential for Gastric Cancer Based on Machine Learning Algorithms

BACKGROUND

Gastric cancer (GC) is considered a highly heterogeneous disease, and currently, a comprehensive approach encompassing molecular data from various biological levels is lacking.

METHODS

This study conducted different analyses, including the identification of differentially expressed genes (DEGs), weighted correlation networks (WGCNA), single-cell RNA sequencing (scRNA-seq), mRNA expression-based stemness index (mRNAsi), and multiCox analysis, utilizing data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, the machine learning algorithms including least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), combined with multiCox analysis were exploited to identify hub genes. These findings were then validated through the receiver operating characteristic (ROC) curve and Kaplan-Meier analysis, and were experimentally confirmed in GC samples by reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA).

RESULTS

Integrated analysis of TCGA and GEO databases, coupled with LASSO regression and RF algorithms, allowed us to identify 18 hub genes encoding differentially expressed secreted proteins in GC. The results of RT-PCR and bioinformatics analysis revealed four promising biomarkers with optimal diagnostic and prognostic potential. ROC analysis and Kaplan-Meier curves highlighted CHI3L1, FCGBP, VSIG2, and TFF2 as promising biomarkers for GC, offering superior modeling accuracy. These findings were further confirmed by RT-PCR and ELISA, affirming the clinical utility of these four biomarkers. Additionally, CIBERSORT analysis indicated a potential correlation between the four biomarkers and the infiltration of B memory cells and Treg cells.

CONCLUSION

This study unveiled four promising biomarkers present in the serum of patients with GC, which could serve as powerful indicators of GC and provide valuable insights for further research into GC pathogenesis.

Gene Expression Dysregulation in Whole Blood of Patients with Clostridioides difficile Infection

Clostridioides difficile (C. difficile) is a global threat and has significant implications for individuals and health care systems. Little is known about host molecular mechanisms and transcriptional changes in peripheral immune cells. This is the first gene expression study in whole blood from patients with C. difficile infection. We took blood and stool samples from patients with toxigenic C. difficile infection (CDI), non-toxigenic C. difficile infection (GDH), inflammatory bowel disease (IBD), diarrhea from other causes (DC), and healthy controls (HC). We performed transcriptome-wide RNA profiling on peripheral blood to identify diarrhea common and CDI unique gene sets. Diarrhea groups upregulated innate immune responses with neutrophils at the epicenter. The common signature associated with diarrhea was non-specific and shared by various other inflammatory conditions. CDI had a unique 45 gene set reflecting the downregulation of humoral and T cell memory functions. Dysregulation of immunometabolic genes was also abundant and linked to immune cell fate during differentiation. Whole transcriptome analysis of white cells in blood from patients with toxigenic C. difficile infection showed that there is an impairment of adaptive immunity and immunometabolism.

ULK2 suppresses ovarian cancer cell migration and invasion by elevating IGFBP3

Background

Ovarian cancer is an aggressive malignancy with high mortality known for its considerable metastatic potential. This study aimed to explore the expression and functional role of Unc-51 like autophagy activating kinase 2 (ULK2) in the progression of ovarian cancer.

Methods

ULK2 expression patterns in ovarian cancer tissues as well as benign tumor control samples obtained from our institution were evaluated using immunohistochemistry. Cell counting kit 8 and Transwell assays were applied to assess the effects of ULK2 overexpression on cell proliferation, migration and invasion, respectively. RNA sequencing was performed to explore potential mechanisms of action of ULK2 beyond its classical autophagy modulation.

Results

Our experiments showed significant downregulation of ULK2 in ovarian cancer tissues. Importantly, low expression of ULK2 was markedly correlated with decreased overall survival. In vitro functional studies further demonstrated that overexpression of ULK2 significantly suppressed tumor cell proliferation, migration, and invasion. RNA sequencing analysis revealed a potential regulatory role of ULK2 in the insulin signaling pathway through upregulation of insulin-like growth factor binding protein-3 (IGFBP3) in ovarian cancer cells.

Conclusions

In summary, the collective data indicated that ULK2 acted as a tumor suppressor in ovarian cancer by upregulating the expression of IGFBP3. Our study underscores the potential utility of ULK2 as a valuable prognostic marker for ovarian cancer.

Beyond Small Molecules: Antibodies and Peptides for Fibroblast Activation Protein Targeting Radiopharmaceuticals

Fibroblast activation protein (FAP) is a serine protease characterized by its high expression in cancer-associated fibroblasts (CAFs) and near absence in adult normal tissues and benign lesions. This unique expression pattern positions FAP as a prospective biomarker for targeted tumor radiodiagnosis and therapy. The advent of FAP-based radiotheranostics is anticipated to revolutionize cancer management. Among various types of FAP ligands, peptides and antibodies have shown advantages over small molecules, exemplifying prolonged tumor retention in human volunteers. Within its scope, this review summarizes the recent research progress of the FAP radiopharmaceuticals based on antibodies and peptides in tumor imaging and therapy. Additionally, it incorporates insights from recent studies, providing valuable perspectives on the clinical utility of FAP-targeted radiopharmaceuticals.

The secretory leukocyte protease inhibitor (SLPI) in pathophysiology of non-communicable diseases: Evidence from experimental studies to clinical applications

Non-communicable diseases (NCDs) are a worldwide health issue because of their prevalence, negative impacts on human welfare, and economic costs. Protease enzymes play important roles in viral and NCD diseases. Slowing disease progression by inhibiting proteases using small-molecule inhibitors or endogenous inhibitory peptides appears to be crucial. Secretory leukocyte protease inhibitor (SLPI), an inflammatory serine protease inhibitor, maintains protease/antiprotease balance. SLPI is produced by host defense effector cells during inflammation to prevent proteolytic enzyme-induced tissue damage. The etiology of noncommunicable illnesses is linked to SLPI's immunomodulatory and tissue regeneration roles. Disease phases are associated with SLPI levels and activity changes in regional tissue and circulation. SLPI has been extensively evaluated in inflammation, but rarely in NCDs. Unfortunately, the thorough evaluation of SLPI's pathophysiological functions in NCDs in multiple research models has not been published elsewhere. In this review, data from PubMed from 2014 to 2023 was collected, analysed, and categorized into in vitro, in vivo, and clinical studies. According to the review, serine protease inhibitor (SLPI) activity control is linked to non-communicable diseases (NCDs) and other illnesses. Overexpression of the SLPI gene and protein may be a viable diagnostic and therapeutic target for non-communicable diseases (NCDs). SLPI is also cytoprotective, making it a unique treatment. These findings suggest that future research should focus on these pathways using advanced methods, reliable biomarkers, and therapy approaches to assess susceptibility and illness progression. Implications from this review will help pave the way for a new therapeutic target and diagnosis marker for non-communicable diseases.

Define cancer-associated fibroblasts (CAFs) in the tumor microenvironment: new opportunities in cancer immunotherapy and advances in clinical trials

Despite centuries since the discovery and study of cancer, cancer is still a lethal and intractable health issue worldwide. Cancer-associated fibroblasts (CAFs) have gained much attention as a pivotal component of the tumor microenvironment. The versatility and sophisticated mechanisms of CAFs in facilitating cancer progression have been elucidated extensively, including promoting cancer angiogenesis and metastasis, inducing drug resistance, reshaping the extracellular matrix, and developing an immunosuppressive microenvironment. Owing to their robust tumor-promoting function, CAFs are considered a promising target for oncotherapy. However, CAFs are a highly heterogeneous group of cells. Some subpopulations exert an inhibitory role in tumor growth, which implies that CAF-targeting approaches must be more precise and individualized. This review comprehensively summarize the origin, phenotypical, and functional heterogeneity of CAFs. More importantly, we underscore advances in strategies and clinical trials to target CAF in various cancers, and we also summarize progressions of CAF in cancer immunotherapy.

Cancer-associated fibroblasts refine the classifications of gastric cancer with distinct prognosis and tumor microenvironment characteristics

Background

Cancer-associated fibroblasts (CAFs) are essential tumoral components of gastric cancer (GC), contributing to the development, therapeutic resistance and immune-suppressive tumor microenvironment (TME) of GC. This study aimed to explore the factors related to matrix CAFs and establish a CAF model to evaluate the prognosis and therapeutic effect of GC.

Methods

Sample information from the multiply public databases were retrieved. Weighted gene co-expression network analysis was used to identify CAF-related genes. EPIC algorithm was used to construct and verify the model. Machine-learning methods characterized CAF risk. Gene set enrichment analysis was employed to elucidate the underlying mechanism of CAF in the development of GC.

Results

A three-gene (GLT8D2, SPARC and VCAN) prognostic CAF model was established, and patients were markedly divided according to the riskscore of CAF model. The high-risk CAF clusters had significantly worse prognoses and less significant responses to immunotherapy than the low-risk group. Additionally, the CAF risk score was positively associated with CAF infiltration in GC. Moreover, the expression of the three model biomarkers were significantly associated with the CAF infiltration. GSEA revealed significant enrichment of cell adhesion molecules, extracellular matrix receptors and focal adhesions in patients at a high risk of CAF.

Conclusion

The CAF signature refines the classifications of GC with distinct prognosis and clinicopathological indicators. The three-gene model could effectively aid in determining the prognosis, drug resistance and immunotherapy efficacy of GC. Thus, this model has promising clinical significance for guiding precise GC anti-CAF therapy combined with immunotherapy.

Development and Validation of the Promising PPAR Signaling Pathway-Based Prognostic Prediction Model in Uterine Cervical Cancer

A ligand-activated transcription factor, peroxisome proliferator-activated receptor (PPAR) regulates fatty acid uptake and transport. In several studies, upregulation of PPAR expression/activity by cancer cells has been associated with cancer progression. Worldwide, cancer of the cervix ranks fourth among women's cancers. Angiogenesis inhibitors have improved treatment for recurrent and advanced cervical cancer since their introduction 5 years ago. In spite of that, the median overall survival rate for advanced cervical cancer is 16.8 months, indicating that treatment effectiveness is still lacking. Thus, it is imperative that new therapeutic methods be developed. In this work, we first downloaded the PPAR signaling pathway-related genes from the previous study. In addition, the single-sample gene set enrichment analysis (ssGSEA) algorithm was applied to calculate the PPAR score of patients with cervical cancer. Furthermore, cervical cancer patients with different PPAR scores show different sensitivity to immune checkpoint therapy. In order to screen the genes to serve as the best biomarker for cervical cancer patients, we then construct the PPAR-based prognostic prediction model. The results revealed that PCK1, MT1A, AL096855.1, AC096711.2, FAR2P2, and AC099568.2 not only play a key role in the PPAR signaling pathway but also show good predictive value in cervical cancer patients. The gene set variation analysis (GSVA) enrichment analysis also proved that the PPAR signaling pathway is one of the most enriched pathways in the prognostic prediction model. Finally, further analysis revealed that AC099568.2 may be the most promising biomarker for the diagnosis, treatment, and prognosis in cervical cancer patients. Both the survival analysis and Receiver Operating Characteristic curve demonstrated that AC099568.2 plays a key role in cervical cancer patients. However, to our knowledge, this is the first time a study focused on the role of AC099568.2 in cervical cancer patients. Our work successfully revealed a new biomarker for cervical cancer patients, which also provides a new direction for future research.