Enhanced Angiogenesis in Salivary Duct Carcinoma Ex-Pleomorphic Adenoma

Molecular Factors in Carcinoma Ex Pleomorphic Adenoma: Systematic Review and Meta-Analysis

OBJECTIVE

Carcinoma ex pleomorphic adenoma (CXPA) is a rare malignant salivary gland tumor. Although multiple reviews have been published on salivary gland malignancies, it has been a decade since the last dedicated systematic review pertaining to CXPA alone was published. This study examines molecular factors in CXPA diagnosis.

DATA SOURCES

MEDLINE, CINAHL, Embase, Scopus, Web of Science (BIOSIS), Cochrane CENTRAL, Health Collection (Informit), OpenDOAR, and GreyNet International.

REVIEW METHODS

Systematic review and meta-analysis from inception to October 31, 2022 for all English language studies pertaining to "carcinoma ex pleomorphic adenoma." Predicted incidence of each biomarker was calculated with meta-analysis. Comparison against pleomorphic adenoma (PA) and salivary duct carcinoma (SDC) when reported within the same study are performed. Risk of bias performed with JBI tool for prevalence studies.

RESULTS

Of 19151 unique studies undergoing abstract screening, 55 studies (n = 1322 patients) underwent data analysis. Biomarkers with >3 studies were p53, HER2, AR, EGFR, PLAG1, ERBB, ER, PR, HMGA2, p16, p63, a-SMA, RAS, PTEN, PDL1, BRAF, PIK3CA, and c-kit. Highest incidence was seen in AR, EGFR, p16, and p53. Significant differences were demonstrated compared with PA and SDC. There was high heterogeneity and overall high risk of bias within studies.

CONCLUSION

Molecular factors are an area of interest in the diagnosis of CXPA. Our study results support examining CXPA as a discrete cohort in future targeted therapy trials. Laryngoscope, 134:1042-1053, 2024.

Extracellular matrix remodelling and stiffening contributes to tumorigenesis of salivary carcinoma ex pleomorphic adenoma--A study based on patient-derived organoids

BACKGROUND

Salivary carcinoma ex pleomorphic adenoma (CXPA) is defined as a carcinoma that develops from benign pleomorphic adenoma (PA). Abnormally activated Androgen signaling pathway and amplification of HER-2/neu(ERBB-2) gene are known to be involved in CXPA tumorigenesis. Recent progress in tumour microenvironment research has led to identification that extracellular matrix (ECM) remodelling and increased stiffness act as critical contributing role in tumour carcinogenesis. This study examined ECM modifications to elucidate the mechanism underlying CXPA tumorigenesis.

RESULTS

PA and CXPA organoids were successfully established. Histological observation, immunohistochemistry (IHC), and whole-exome sequencing demonstrated that organoids recapitulated phenotypic and molecular characteristics of their parental tumours. RNA-sequencing and bioinformatic analysis of organoids showed that differentially expressed genes are highly enriched in ECM-associated terms, implying that ECM alternations may be involved in carcinogenesis. Microscopical examination for surgical samples revealed that excessive hyalinized tissues were deposited in tumour during CXPA tumorigenesis. Transmission electron microscopy confirmed that these hyalinized tissues were tumour ECM in nature. Subsequently, examination by picrosirius red staining, liquid chromatography with tandem mass spectrometry, and cross-linking analysis indicated that tumour ECM was predominantly composed of type I collagen fibers, with dense collagen alignment and an increased level of collagen cross-linking. IHC revealed the overexpression of COL1A1 protein and collagen-synthesis-related genes, DCN and IGFBP5 (p < 0.05). Higher stiffness of CXPA than PA was demonstrated by atomic force microscopy and elastic imaging analysis. We utilized hydrogels to mimic ECM with varying stiffness degrees in vitro. Compared with softer matrices (5Kpa), CXPA cell line and PA primary cells exhibited more proliferative and invasive phenotypes in stiffer matrices (50Kpa, p < 0.01). Protein-protein interaction (PPI) analysis of RNA-sequencing data revealed that AR and ERBB-2 expression was associated with TWIST1. Moreover, surgical specimens demonstrated a higher TWIST1 expression in CXPA over PA. After knocking down TWIST1 in CXPA cells, cell proliferation, migration, and invasiveness were significantly inhibited (p < 0.01).

CONCLUSION

Developing CXPA organoids provides a useful model for cancer biology research and drug screening. ECM remodelling, attributed to overproduction of collagen, alternation of collagen alignment, and increased cross-linking, leads to increased ECM stiffness. ECM modification is an important contributor in CXPA tumorigenesis.

Maxillary Sinus Pleomorphic Adenoma: A Systematic Review

Background: Pleomorphic adenoma (PA), also known as a mixed tumor, is a neoplasm of the glandular tissue. The maxillary sinus (MS) is an atypical location of PA; however, as an empty cavity inside the bone, it predisposes tumors to develop unnoticed. The aim of the following systematic review was to identify and characterize the diagnostics and prognosis of maxillary sinus pleomorphic adenoma (MSPA). Methods: The cases of MSPA that underwent treatment and were observed for possible recurrence were qualified for the review. The medical databases were searched using the following engines: ACM, BASE, Google Scholar and PubMed. The risk of bias was assessed using the JBI Critical Appraisal Tool for Case Reports. The extracted data were tabulated and summarized in a descriptive manner. Results: To the best knowledge of the authors, only seven cases of MSPA were described in the last 20 years. Relapse occurred in three of these cases and malignant transformation occurred in two. The longest recorded time without recurrence was 4 years. The size of the detected MSPA in the largest dimension ranged in various cases from 40 to 60 mm, with an average of 48 mm. Discussion: All but one of the seven included reports showed flaws during the risk of bias assessment. Only in two of seven reported cases was there no reason to suspect that the tumor had penetrated the sinus from the oral or nasal cavity. The MS is disadvantageous as a location as tumors occupying the entire volume of the sinus are often diagnosed. Recurrences and malignant transformations seem to be frequent and often remain undetected for a prolonged period. Other information: This research received no external funding. OSF Registries number: 8KVGM.

Using a Machine Learning Approach to Identify Key Biomarkers for Renal Clear Cell Carcinoma

Background

The most common and deadly subtype of renal carcinoma is kidney renal clear cell carcinoma (KIRC), which accounts for approximately 75% of renal carcinoma. However, the main cause of death in KIRC patients is tumor metastasis. There are no obvious clinical features in the early stage of kidney cancer, and 25–30% of patients have already metastasized when they are first diagnosed. Moreover, KIRC patients whose local tumors have been removed by nephrectomy are still at high risk of metastasis and recurrence and are not sensitive to chemotherapy and radiotherapy, leading to poor prognosis. Therefore, early diagnosis and treatment of this disease are very important.

Methods

KIRC-related patient datasets were downloaded from the GEO database and TCGA database. DEG screening and GO, KEGG and GSEA enrichment analysis was firstly conducted and then the LASSO and support vector machine (SVM) RFE algorithms were adopted to identify KIRC-associated key genes in training sets and validate them in the test set. The clinical prognostic analysis including the association between the expression of key genes and the overall survival, stage, grade across KIRC, the immune infiltration difference between normal samples and cancer samples, the correlation between the key genes and immune cells, immunomodulator, immune subtypes of KIRC were investigated in this research.

Results

We finally screened out 4 key genes, including ACPP, ANGPTL4, SCNN1G, SLC22A7. The expression of key genes show difference among normal samples and tumor samples, SCNN1G and SLC22A7 could be predictor of prognosis of patients. The expression of key genes was related with the abundance of tumor infiltration immune cells and the gene expression of immune checkpoint.

Conclusion

This study screened the 4 key genes, which contributed to early diagnosis, prognosis assessment and immune target treatment of patients with KIRC.

Machine learning to identify immune-related biomarkers of rheumatoid arthritis based on WGCNA network

OBJECTIVES

This study was designed to identify the potential diagnostic biomarkers of rheumatoid arthritis (RA) and to explore the potential pathological relevance of immune cell infiltration in this disease.

METHODS

Three previously published datasets containing gene expression data from 35 RA patients and 29 controls (GSE55235, GSE55457, and GSE12021) were downloaded from the GEO database, after which a weighted correlation network analysis (WGCNA) approach was utilized to clarify differentially abundant genes. Candidate biomarkers of RA were then identified via the use of a LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. Data were validated based upon the area under the receiver operating characteristic curve (AUC) values, with hub genes being identified as those with an AUC > 85% and a P value < 0.05. Lastly, the CIBERSORT algorithm was used to assess immune cell infiltration of RA tissues, and correlations between immune cell infiltration and disease-related diagnostic biomarkers were assessed.

RESULTS

The green-yellow module containing 87 genes was found to be highly correlated with RA positivity. FADD, CXCL2, and CXCL8 were identified as potential RA diagnostic biomarkers (AUC > 0.85), and these results were validated using the GSE77298 dataset. Immune cell infiltration analyses revealed the expression of hub genes to be correlated with mast cells, monocytes, activated NK cells, CD8 T cells, resting dendritic cells, and plasma cells.

CONCLUSION

These data indicate that FADD, CXCL2, and CXCL8 are valuable diagnostic biomarkers of RA, offering new insight that can guide future studies of RA incidence and progression.