Evaluation of ITGA3 as a Biomarker of Progression and Recurrence in Papillary Thyroid Carcinoma

Characterization of Extracellular Vesicles Derived From Human Precision-Cut Liver Slices in Metabolic Dysfunction-Associated Steatotic Liver Disease

Extracellular vesicles (EVs) are cell-produced, membrane-surrounded vesicles that harbour the biological features of donor cells. In the current study, we are the first to isolate and characterize EVs isolated from human precision-cut liver slices (PCLS), obtained from both healthy and metabolic dysfunction-associated steatohepatitis (MASH) cirrhotic livers. PCLS derived from patients can faithfully represent disease conditions in humans. EVs were isolated from human PCLS after incubating in normal medium or modified medium that mimics the pathophysiological environment of metabolic dysfunction associated liver disease (MASLD). MASH PCLS produced higher amounts of EVs compared to healthy PCLS (p < 0.001). Mass spectrometry revealed that around 300 proteins were significantly different in EVs derived from MASH PCLS versus healthy PCLS (FDR < 0.05), irrespective of the type of medium. Significantly changed EV proteins were largely involved in signalling receptor binding function and showed potential in promoting fibrosis. In the liver, these ligand-associated receptors are highly expressed in hepatic stellate cells, and the MASH EVs functionally promoted the activation of hepatic stellate cells. Furthermore, the amounts of EpCAM and ITGA3 in EVs were positively associated with the progression of MASLD, which suggests the use of liver-derived EVs as potential biomarkers for MASLD. Characterization of EVs derived from human PCLS may assist future studies in investigating the pathogenesis and identifying liver-specific EVs as biomarkers of MASLD.

SPOCK2 promotes the invasion and migration of ovarian cancer cells through FAK signaling pathway

OBJECTIVE

Ovarian cancer is one of the most prevalent malignancies worldwide, with the highest mortality rate among gynecological cancers. This study aims to investigate the molecular mechanisms of SPOCK2 in ovarian cancer progression and metastasis and evaluate its potential as a therapeutic target.

METHODS

The expression levels of SPOCK2 in ovarian cancer tissues and normal tissues were analyzed using data from The Cancer Genome Atlas (TCGA) and immunohistochemistry experiments. Functional assays, including epithelial-mesenchymal transition (EMT), invasion, and migration assays, were performed in high-grade serous ovarian cancer (HGSOC) cells to explore the role of SPOCK2. The interaction between SPOCK2 and ITGA3 and the subsequent activation of focal adhesion kinase (FAK) signaling were investigated. In vivo experiments were conducted to validate the effects of SPOCK2 knockdown on tumor metastasis and invasiveness.

RESULTS

SPOCK2 expression was significantly upregulated in ovarian cancer tissues compared to normal tissues and was associated with poor prognosis. Functional assays demonstrated that SPOCK2 promotes EMT, invasion, and migration in HGSOC cells by interacting with ITGA3 and activating FAK signaling. In vivo experiments confirmed that SPOCK2 knockdown significantly suppressed tumor metastasis and invasiveness.

CONCLUSION

This study highlights the critical role of the SPOCK2/ITGA3 axis in driving ovarian cancer progression and provides evidence for SPOCK2 as a potential molecular marker and therapeutic target. These findings offer new insights into the early diagnosis and treatment of ovarian cancer, with significant clinical implications for improving patient outcomes.

ITGA3 promotes pancreatic cancer progression through HIF1α- and c-Myc-driven glycolysis in a collagen I-dependent autocrine manner

Pancreatic cancer is characterized by severe metabolic stress due to its prominent desmoplasia and poor vascularization. Integrin subunit alpha 3 (ITGA3) is a cell surface adhesion protein involved in tumor progression. However, the role of ITGA3 in pancreatic cancer progression, especially in metabolic reprogramming, remains largely unknown. In this study, we found that ITGA3 expression is elevated in pancreatic cancer tissues and predicts poor prognosis for patients with pancreatic cancer. Functional assays revealed that ITGA3 promotes the growth and liver metastasis of pancreatic cancer via boosting glycolysis. Mechanistically, Collagen I (Col1) derived from cancer cells acts as a ligand for ITGA3 to activate the FAK/PI3K/AKT/mTOR signaling pathway in an autocrine manner, thereby increasing the expression of HIF1α and c-Myc, two critical regulators of glycolysis. Blockade of Col1 by siRNA or of ITGA3 by a blocking antibody leads to specific inactivation of the FAK/PI3K/AKT/mTOR pathway and impairs malignant tumor behaviors induced by ITGA3. Thus, our data indicate that ITGA3 enhances glycolysis to promote pancreatic cancer growth and metastasis via increasing HIF1α and c-Myc expression in a Col1-dependent autocrine manner, making ITGA3 as a candidate diagnostic biomarker and a potential therapeutic target for pancreatic cancer.

Elevated ITGA3 expression serves as a novel prognostic biomarker and regulates tumor progression in cervical cancer

Patients with advanced and recurrent cervical cancer often lack satisfactory treatment outcomes. Thus, it is necessary to seek reliable biomarkers that provide the ability to identify the disease at an early stage and predict the patient prognosis, providing new strategies for the treatment of cervical cancer. The sequencing data of ITGA3 were retrieved from public datasets. Immune infiltration and sensitivity of potential immunotherapy and chemotherapy have been analyzed between two subgroups. Functional analysis was applied to excavate the related pathways of ITGA3 in cervical cancer. Furthermore, the impact of ITGA3 in tumor progression has been verified in vitro. The results revealed that the level of ITGA3 was upregulated in cervical cancer, and was positively correlated with worse prognosis. The tumor microenvironment of patients in the high-risk group was immunosuppressed. Patients in high-risk group may not benefit from immunotherapy, but be may be sensitive to several chemotherapy drugs. Notably, the angiogenesis, epithelial mesenchymal transition, and PI3K pathway were increased in high-risk group. Collectively, ITGA3 is a marker of poor prognosis and promotes tumor progression by regulating PI3K/AKT pathway in cervical cancer. Our results provide new insights for potential molecular targeted therapy and prognostic prediction of cervical cancer.

A prognostic model for anoikis-related genes in pancreatic cancer

Anoikis, a distinct form of programmed cell death, is crucial for both organismal development and maintaining tissue equilibrium. Its role extends to the proliferation and progression of cancer cells. This study aimed to establish an anoikis-related prognostic model to predict the prognosis of pancreatic cancer (PC) patients. Gene expression data and patient clinical profiles were sourced from The Cancer Genome Atlas (TCGA-PAAD: Pancreatic Adenocarcinoma) and the International Cancer Genome Consortium (ICGC-PACA: Pancreatic Ductal Adenocarcinoma). Non-cancerous pancreatic tissue gene expression data were obtained from the Genotype-Tissue Expression (GTEx) project. The R package was used to construct anoikis-related PC prognostic models, which were later validated with the ICGC-PACA database. Survival analyses demonstrated a poorer prognosis for patients in the high-risk group, consistent across both TCGA-PAAD and ICGC-PACA datasets. A nomogram was designed as a predictive tool to estimate patient mortality. The study also analyzed tumor mutations and immune infiltration across various risk groups, uncovering notable differences in tumor mutation patterns and immune landscapes between high- and low-risk groups. In conclusion, this research successfully developed a prognostic model centered on anoikis-related genes, offering a novel tool for predicting the clinical trajectory of PC patients.

Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning

Background

Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood.

Materials and methods

LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays.

Results

Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity.

Conclusion

In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.

A prognostic biomarker NRG1 promotes U-87 MG glioblastoma cell malignancy by inhibiting autophagy via ERBB2/AKT/mTOR pathway

Glioblastoma (GBM) is the most common and aggressive primary brain malignancy. Studies have shown that autophagy-related (ATG) genes play important roles in regulating GBM malignancy. However, the mechanism still needs to be fully elucidated. Based on clinical and gene expression information of GBM patients downloaded from The The Cancer Genome Atlas database, Kaplan-Meier, univariate Cox regression, least absolute shrinkage and selection operator regression and multivariate Cox regression were applied to construct a risk signature for GBM prognosis, followed by validation using receiver operating characteristic analysis. Next, Cell Counting Kit-8, wound healing assay, flow cytometry, monodansyl cadaverine autophagy staining assay, immunofluorescence staining and western blot, either in the absence or presence of ERBB2/AKT/mTOR inhibitors, were carried out in GBM U87 cell line to explore molecular pathway underlying GBM malignancy. A three-ATG-gene signature (HIF1A, ITGA3, and NGR1) was constructed for GBM prognosis with the greatest contribution from NRG1. In vitro experiments showed that NRG1 promoted U87 cell migration and proliferation by inhibiting autophagy, and ERBB2/AKT/mTOR is a downstream pathway that mediates the autophagy-inhibitory effects of NRG1. We constructed an ATG gene prognostic model for GBM and demonstrated that NRG1 inhibited autophagy by activating ERBB2/AKT/mTOR, promoting GBM malignancy, thus providing new insights into the molecular contribution of autophagy in GBM malignancy.

The role of BHLHE40 in clinical features and prognosis value of PDAC by comprehensive analysis and in vitro validation

Pancreatic ductal adenocarcinoma (PDAC) is the leading cause of cancer-related mortality, primarily due to the abundance of cancer-associated fibroblasts (CAFs), depleted effector T cells, and increased tumor cell stemness; hence, there is an urgent need for efficient biomarkers with prognostic and therapeutic potential. Here, we identified BHLHE40 as a promising target for PDAC through comprehensive analysis and weighted gene coexpression network analysis of RNA sequencing data and public databases, taking into account the unique characteristics of PDAC such as cancer-associated fibroblasts, infiltration of effector T cells, and tumor cell stemness. Additionally, we developed a prognostic risk model based on BHLHE40 and three other candidate genes (ITGA2, ITGA3, and ADAM9) to predict outcomes in PDAC patients. Furthermore, we found that the overexpression of BHLHE40 was significantly associated with T stage, lymph node metastasis, and American Joint Committee on Cancer (AJCC) stage in a cohort of 61 PDAC patients. Moreover, elevated expression levels of BHLHE40 were validated to promote epithelial-mesenchymal transition (EMT) and stemness-related proteins in BXPC3 cell lines. Compared to the parent cells, BXPC3 cells with BHLHE40 overexpression showed resistance to anti-tumor immunity when co-cultured with CD8⁺ T cells. In summary, these findings suggest that BHLHE40 is a highly effective biomarker for predicting prognosis in PDAC and holds great promise as a target for cancer therapy.

A diagnostic model for distinguishing between active tuberculosis and latent tuberculosis infection based on the blood expression profiles of autophagy-related genes

BACKGROUND

Autophagy is closely involved in the control of mycobacterial infection.

OBJECTIVES

Here, a diagnostic model was developed using the levels of autophagy-related genes (ARGs) in the blood to differentiate active tuberculosis (ATB) and latent tuberculosis infection (LTBI).

DESIGN

Secondary data analysis of three prospective cohorts.

METHODS

The expression of ARGs in patients with ATB and LTBI were analyzed using the GSE37250, GSE19491, and GSE28623 datasets from the GEO database.

RESULTS

Twenty-two differentially expressed ARGs were identified in the training dataset GSE37250. Using least absolute shrinkage and selection operator and multivariate logistic regression, three ARGs (FOXO1, CCL2, and ITGA3) were found that were positively associated with adaptive immune-related lymphocytes and negatively associated with myeloid and inflammatory cells. A nomogram was constructed using the three ARGs. The accuracy, consistency, and clinical relevance of the nomogram were evaluated using receiver operating characteristic curves, the C-index, calibration curves, and validation in the datasets GSE19491 and GSE28623. The nomogram showed good predictive performance.

CONCLUSION

The nomogram was able to accurately differentiate between ATB and LTBI patients. These findings provide evidence for future study on the pathology of autophagy in tuberculosis infection.