Zhongtang Xiong

Objective Ovarian cancer (OV) is considered the most lethal gynecological cancer in women. Despite significant advancements in treatment and risk-stratification methods, these approaches remain far from ideal. This study aims to leverage large-scale public cohorts to identify differentially expressed prognostic genes between OV and normal ovarian tissue and evaluate their impact on patient survival. Methods Utilizing data from extensive public cohorts and machine learning methods, we conducted a comprehensive screening to identify genes differentially expressed in ovarian cancer compared to normal ovarian tissue. We also developed a risk score for each patient based on these genes. Subsequent analyses explored the immunological profiles and genomic alterations associated with different risk scores. Results Our analysis revealed that a high risk score is positively correlated with poor survival in OV patients. The risk score is associated with key oncological pathways, immune-related processes, and genomic changes. Notably, patients with higher risk scores exhibited increased levels of immune cell infiltration and significant remodeling of the immune microenvironment. Furthermore, there is a strong correlation between the risk score and immune checkpoint molecules, suggesting potential benefits from immune checkpoint blockade strategies. The risk score also proved to be a stable and sensitive indicator for predicting sensitivity to various chemotherapeutic drugs. Conclusions Through an integrative approach, our study deciphers the prognostic, immune, and therapeutic value of the risk score in OV. This analysis highlights the importance of the risk score in predicting survival, modulating immune response, and guiding chemotherapy sensitivity, thus supporting its utility in improving personalized treatment strategies for ovarian cancer patients.

Objective. Gliomas are the most common and life‐threatening intracranial tumors. Immune infiltration of the tumor microenvironment significantly affects tumor prognosis in glioma. Recently, PLEKHA4 was reported to be upregulated in melanoma and closely associated with tumor genesis and development, but its role in glioma is poorly understood. Our aim was to investigate the expression, functional role, and prognostic value of PLEKHA4 in glioma. Methods. The expression levels of PLEKHA4 in 33 types of cancer in the TCGA (The Cancer Genome Atlas) database were collected via the UCSC Xena browser. The clinical samples of glioma patients were downloaded from the TCGA database. Immunohistochemistry was used to verify PLEKHA4 expression in tumor tissues. We assessed the influence of PLEKHA4 on survival of glioma patients by survival module and GEPIA. Then, we downloaded datasets of glioma from TCGA and investigated the correlations between the clinical characteristics and PLEKHA4 expression using logistic regression. Moreover, we used TIMER to explore the collection of PLEKHA4 expression and immune infiltration level in glioma and to analyze cumulative survival in glioma. Gene Set Enrichment Analysis (GSEA) was performed using the TCGA dataset. Results. PLEKHA4 transcript levels were significantly upregulated in multiple cancer types, including gliomas. Moreover, immunohistochemical analysis verified that PLEKHA4 was overexpressed in gliomas compare to the corresponding normal tissues. Univariable survival and multivariate cox analysis show that increased PLEKHA4 expression significantly correlated with age, tumor grade, IDH mutation status, and 1p/19q codel status, and higher PLEKHA4 had shorter OS, DSS, and PFI. Specifically, PLEKHA4 expression level had significant positive correlations with infiltrating levels of B cell, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and DCs in glioma, and upregulation of PLEKHA4 expression was significantly related to immune cell biomarkers and immune checkpoint expression in glioma. In addition, several GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) items associated with immune response, JAK STAT signal pathway, and cell cycle were significantly enriched in the high PLEKHA4 expression phenotype pathway. Conclusions. Our findings proposed that PLEKHA4 was an independent prognostic biomarker and correlated with immune infiltrates in glioma, and targeting PLEKHA4 might improve immunotherapy in glioma. Of course, these findings also need basic experiments and further clinical trials to confirm in the future.

Prevalent copy number alteration is the most prominent genetic characteristic associated with ovarian cancer (OV) development, but its role in immune evasion has not been fully elucidated. In this study, we identified RAD21, a key component of the cohesin complex, as a frequently amplified oncogene that could modulate immune response in OV. Through interrogating the RAD21-regulated transcriptional program, we found that RAD21 directly interacts with YAP/TEAD4 transcriptional corepressors and recruits the NuRD complex to suppress interferon (IFN) signaling. In multiple clinical cohorts, RAD21 overexpression is inversely correlated with IFN signature gene expression in OV. We further demonstrated in murine syngeneic tumor models that RAD21 ablation potentiated anti–PD-1 efficacy with increased intratumoral CD8⁺ T cell effector activity. Our study identifies a RAD21–YAP/TEAD4–NuRD corepressor complex in immune modulation, and thus provides a potential target and biomarker for precision immunotherapy in OV.

Tissue microarrays (TMAs), also called tissue chips, contain hundreds to thousands of tissue cores obtained from different tissue donor blocks. By using TMA technology, a molecular marker, such as protein, RAN or DNA, can be simultaneously examined in hundreds of different specimens under the same experimental conditions. A growing number of previous studies have introduced different methods for constructing TMAs. Many authors tried to use various methods to implant more tissue cores in a single recipient block, and most of these methods involved reducing the diameter of the tissue cores and/or the spacing between adjacent tissue cores. However, when creating TMAs, it is difficult to reduce the distance between tissue cores to zero except with extremely expensive automatic TMA arrayers. Here, we introduce a novel method to construct a high-density TMA that does not have spacing between the tissue cores. We also introduce a method for preparing a self-made tissue‑arraying instrument. With this method and the tissue‑arraying instrument, we successfully created a TMA containing 126 tissue cores that were 2 mm in diameter. H&E staining, immunohistochemical staining and fluorescence in situ hybridization were performed on the sections cut from the TMA without any tissue spot loss. This method is easy to operate, and the materials for creating the tissue‑arraying instrument are inexpensive and can be purchased anywhere. Therefore, this high-density method can be applied in all laboratories.

Objective: Many methods for tissue microarray (TMA) construction were described in previous reports. Because TMA-based methods are expensive and complicated, their widespread application may be restricted. This study aimed to develop a simple method for TMA construction. Methods: High-density TMAs were constructed using simple equipment, and hematoxylin and eosin and immunohistochemical staining were performed to analyze the effect on the TMA block. Results: A recipient block with 162 holes of 0.9 mm in diameter was prepared using a mini-drill and plastic mold. Tissue cores of 1.0 mm in diameter were obtained from multiple donor blocks with stainless-steel capillary tubes driven by the mini-drill. Under the fixation and guidance of the plastic mold, tissue cores could be easily injected into the holes in the recipient block by inserting a stainless-steel wire into the stainless-steel tube with the tissue core and then pressing using the stainless-steel wire. Conclusion: A high-density TMA block with 162 1.0-mm cores was created. This new modified technique could be a good alternative in many laboratories.