The novel hormone INSL3 is expressed in human testicular Leydig cell tumors: a clinical and immunohistochemical study

The Application and Pitfalls of Immunohistochemical Markers in Challenging Diagnosis of Genitourinary Pathology

CONTEXT.—

The morphologic features of different entities in genitourinary pathology overlap, presenting a diagnostic challenge, especially when diagnostic materials are limited. Immunohistochemical markers are valuable when morphologic features alone are insufficient for definitive diagnosis. The World Health Organization classification of urinary and male genital tumors has been updated for 2022. An updated review of immunohistochemical markers for newly classified genitourinary neoplasms and their differential diagnosis is needed.

OBJECTIVE.—

To review immunohistochemical markers used in the diagnosis of genitourinary lesions in the kidney, bladder, prostate, and testis. We particularly emphasized difficult differential diagnosis and pitfalls in immunohistochemistry application and interpretation. New markers and new entities in the 2022 World Health Organization classifications of genitourinary tumors are reviewed. Recommended staining panels for commonly encountered difficult differential diagnoses and potential pitfalls are discussed.

DATA SOURCES.—

Review of current literature and our own experience.

CONCLUSIONS.—

Immunohistochemistry is a valuable tool in the diagnosis of problematic lesions of the genitourinary tract. However, the immunostains must be carefully interpreted in the context of morphologic findings with a thorough knowledge of pitfalls and limitations.

Identification and Validation of a Hypoxia-Immune-Based Prognostic mRNA Signature for Oral Squamous Cell Carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is a commonly encountered head and neck malignancy. Increasing evidence shows that there are abnormal immune response and chronic cell hypoxia in the development of OSCC. However, there is a lack of a reliable hypoxia-immune-based gene signature that may serve to accurately prognosticate OSCC.

METHODS

The mRNA expression data of OSCC patients were extracted from the TCGA and GEO databases. Hypoxia status was identified using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. Both ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) were used for further evaluation of immune status. The DEGs in different hypoxia and immune status were determined, and univariate Cox regression was used to identify significantly prognostic genes. A machine learning method, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, allowed us to construct prognostic gene signature to predict the overall survival (OS) of OSCC patients.

RESULTS

A total of 773 DEGs were identified between hypoxia high and low groups. According to immune cell infiltration, patients were divided into immune high, medium, and low groups and immune-associated DEGs were identified. A total of 193 overlapped DEGs in both immune and hypoxia status were identified. With the univariate and LASSO Cox regression model, eight signature mRNAs (FAM122C, RNF157, RANBP17, SOWAHA, KIAA1211, RIPPLY2, INSL3, and DNAH1) were selected for further calculation of their respective risk scores. The risk score showed a significant association with age and perineural and lymphovascular invasion. In the GEO validation cohort, a better OS was observed in patients from the low-risk group in comparison with those in the high-risk group. High-risk patients also demonstrated different immune infiltration characteristics from the low-risk group and the low-risk group showed potentially better immunotherapy efficacy in contrast to high-risk ones.

CONCLUSION

The hypoxia-immune-based gene signature has prognostic potential in OSCC.

Integration and gene co-expression network analysis of scRNA-seq transcriptomes reveal heterogeneity and key functional genes in human spermatogenesis

Spermatogenesis is a complex process of cellular division and differentiation that begins with spermatogonia stem cells and leads to functional spermatozoa production. However, many of the molecular mechanisms underlying this process remain unclear. Single-cell RNA sequencing (scRNA-seq) is used to sequence the entire transcriptome at the single-cell level to assess cell-to-cell variability. In this study, more than 33,000 testicular cells from different scRNA-seq datasets with normal spermatogenesis were integrated to identify single-cell heterogeneity on a more comprehensive scale. Clustering, cell type assignments, differential expressed genes and pseudotime analysis characterized 5 spermatogonia, 4 spermatocyte, and 4 spermatid cell types during the spermatogenesis process. The UTF1 and ID4 genes were introduced as the most specific markers that can differentiate two undifferentiated spermatogonia stem cell sub-cellules. The C7orf61 and TNP can differentiate two round spermatid sub-cellules. The topological analysis of the weighted gene co-expression network along with the integrated scRNA-seq data revealed some bridge genes between spermatogenesis's main stages such as DNAJC5B, C1orf194, HSP90AB1, BST2, EEF1A1, CRISP2, PTMS, NFKBIA, CDKN3, and HLA-DRA. The importance of these key genes is confirmed by their role in male infertility in previous studies. It can be stated that, this integrated scRNA-seq of spermatogenic cells offers novel insights into cell-to-cell heterogeneity and suggests a list of key players with a pivotal role in male infertility from the fertile spermatogenesis datasets. These key functional genes can be introduced as candidates for filtering and prioritizing genotype-to-phenotype association in male infertility.

Identification of a Seven-lncRNA-mRNA Signature for Recurrence and Prognostic Prediction in Relapsed Acute Lymphoblastic Leukemia Based on WGCNA and LASSO Analyses

Abnormal expressions of long noncoding RNAs (lncRNAs) and protein-encoding messenger RNAs (mRNAs) are important for the development of childhood acute lymphoblastic leukemia (ALL). This study developed an lncRNA-mRNA integrated classifier for the prediction of recurrence and prognosis in relapsed childhood ALL by using several transcriptome data. Weighted gene coexpression network analysis revealed that green, turquoise, yellow, and brown modules were preserved across the TARGET, GSE60926, GSE28460, and GSE17703 datasets, and they were associated with clinical relapse and death status. A total of 184 genes in these four modules were differentially expressed between recurrence and nonrecurrence samples. Least absolute shrinkage and selection operator analysis showed that seven genes constructed a prognostic signature (including one lncRNA: LINC00652 and six mRNAs: INSL3, NIPAL2, REN, RIMS2, RPRM, and SNAP91). Kaplan-Meier curve analysis observed that patients in the high-risk group had a significantly shorter overall survival than those of the low-risk group. Receiver operating characteristic curve analysis demonstrated that this signature had high accuracy in predicting the 5-year overall survival and recurrence outcomes, respectively. LINC00652 may function by coexpressing with the above prognostic genes (INSL3, SNAP91, and REN) and lipid metabolism-related genes (MIA2, APOA1). Accordingly, this lncRNA-mRNA-based classifier may be clinically useful to predict the recurrence and prognosis for childhood ALL. These genes represent new targets to explain the mechanisms for ALL.

A Two-Gene Signature for Tuberculosis Diagnosis in Persons With Advanced HIV

Background: Transcriptomic signatures for tuberculosis (TB) have been proposed and represent a promising diagnostic tool. Data remain limited in persons with advanced HIV.

Methods: We enrolled 30 patients with advanced HIV (CD4 <100 cells/mm³) in India; 16 with active TB and 14 without. Whole-blood RNA sequencing was performed; these data were merged with a publicly available dataset from Uganda (n = 33; 18 with TB and 15 without). Transcriptomic profiling and machine learning algorithms identified an optimal gene signature for TB classification. Receiver operating characteristic analysis was used to assess performance.

Results: Among 565 differentially expressed genes identified for TB, 40 were shared across India and Uganda cohorts. Common upregulated pathways reflect Toll-like receptor cascades and neutrophil degranulation. The machine-learning decision-tree algorithm selected gene expression values from RAB20 and INSL3 as most informative for TB classification. The signature accurately classified TB in discovery cohorts (India AUC 0.95 and Uganda AUC 1.0; p < 0.001); accuracy was fair in external validation cohorts.

Conclusions: Expression values of RAB20 and INSL3 genes in peripheral blood compose a biosignature that accurately classified TB status among patients with advanced HIV in two geographically distinct cohorts. The functional analysis suggests pathways previously reported in TB pathogenesis.

Leydig cell tumorigenesis - implication of G-protein coupled membrane estrogen receptor, peroxisome proliferator-activated receptor and xenoestrogen exposure. In vivo and in vitro appraisal

The etiology and molecular characteristics of Leydig cell tumor (LCT) are scarcely known. From the research data stems that estrogen can be implicated in LCT induction and development, however it is not investigated in detail. Considering the above, herein we analyzed the relation between G-protein coupled membrane estrogen receptor, peroxisome proliferator-activated receptor and insulin-like family peptides (insulin-like 3 peptide; INSL3 and relaxin; RLN) expressions as well as estrogen level with impact of xenoestrogen (bisphenol A; BPA, tetrabromobisphenol A; TBBPA, and tetrachlorobisphenol A; TCBPA). While in our previous studies altered GPER-PPAR partnership was found in human LCT being a possible cause and/or additionally effecting on LCT development, here mouse testes with experimentally induced LCT and mouse tumor Leydig cell (MA-10) treated with BPA chemicals were examined. We revealed either diverse changes in expression or co-expression of GPER and PPAR in mouse LCT as well as in MA-10 cells after BPA analogues when compared to human LCT. Relationships between expression of INSL3, RLN, including co-expression, and estrogen level in human LCT, mouse LCT and MA-10 cells xenoestrogen-treated were found. Moreover, involvement of PI3K-Akt-mTOR pathway or only mTOR in the interactions of examined receptors and hormones was showed. Taken together, species, cell of origin, experimental system used and type of used chemical differences may result in diverse molecular characteristics of LCT. Estrogen/xenoestrogen may play a role in tumor Leydig cell proliferation and biochemical nature but this issue requires further studies. Experimentally-induced LCT in mouse testis and MA-10 cells after BPA exposure seem to be additional models for understanding some aspects of human LCT biology.