Integrated analysis of scRNA-seq and bulk RNA-seq identifies FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC.

Background: High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice. Methods: We jointly analyzed the transcriptomic profiles of single-cell and bulk datasets of HGSOC to identify cell types associated with chemoresistance. Copy number variation (CNV) inference was performed to identify malignant cells. We subsequently analyzed the expression of candidate biomarkers and their relationship with patients' prognosis. The enrichment analysis and potential biological function of candidate biomarkers were explored. Then, we validated the candidate biomarker using in vitro experiments. Results: We identified 8871 malignant epithelial cells in a single-cell RNA sequencing dataset, of which 861 cells were associated with chemoresistance. Among these malignant epithelial cells, FBXO2 (F-box protein 2) is highly expressed in cells related to chemoresistance. Moreover, FBXO2 expression was found to be higher in epithelial cells from chemoresistance samples compared to those from chemosensitivity samples in a separate single-cell RNA sequencing dataset. Patients exhibiting elevated levels of FBXO2 experienced poorer outcomes in terms of both overall survival (OS) and progression-free survival (PFS). FBXO2 could impact chemoresistance by influencing the PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interactions and regulating tumorigenesis. The 50% maximum inhibitory concentration (IC50) of cisplatin decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2 during an in vitro experiment. Conclusions: We determined that FBXO2 is a potential biomarker linked to chemoresistance in HGSOC by combining single-cell RNA-seq and bulk RNA-seq dataset. Our results suggest that FBXO2 could serve as a valuable prognostic marker and potential target for drug development in HGSOC.

Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer.

BACKGROUND: Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors. Accordingly, our study aims to investigate the correlation between the image features of intra-tumor heterogeneity and drug resistance of ovarian cancer based on artificial intelligence. METHODS: We obtained hematoxylin and eosin staining frozen histopathological images of ovarian cancer and paracarcinoma tissues from the Cancer Genome Atlas. We extracted quantitative image features of whole-slide images based on the automatic image nuclear segmentation processing technology. After that, we used bioinformatics analysis to find the relationship between image features of intra-tumor heterogeneity and drug resistance. RESULTS: Our results show that our automatic image processing process based on computer artificial intelligence can extract image features effectively, and the key image features extracted are closely related to ITH. Among them, the Perimeter.sd image feature with the most prominent ITH feature can accurately predict the risk of platinum-based chemotherapy drug resistance in ovarian cancer patients. CONCLUSION: Automatic image processing and feature extraction based on artificial intelligence have excellent results. Perimeter.sd can be used as a useful image feature indicator for evaluating ITH. ITH is associated with drug resistance of ovarian cancer, so ITH characteristics can be used as an effective indicator to evaluate drug resistance in patients with ovarian cancer.

TRIM65 knockout inhibits the development of HCC by polarization tumor-associated macrophages towards M1 phenotype via JAK1/STAT1 signaling pathway.

BACKGROUND & AIMS: Tumor-associated macrophages (TAMs) are main components of immune cells in tumor microenvironment (TME), and play a crucial role in tumor progression. Tripartite motif-containing protein 65 (TRIM65) has been associated with tumor progression. However, whether TRIM65 regulate the interaction of tumor cell and TAMs in HCC and the underlying mechanisms remain unknown. In this study, we investigated the role of TRIM65 in TME of HCC and explored its underlying mechanisms. METHODS: The relation of TRIM65 expression level with tumor grades, TNM stages, and worse prognosis of HCC patients was evaluated by bioinformatics analysis, as well as immune infiltration level of macrophages. TRIM65 shRNA was transfected into HepG2 cells, and TRIM65 overexpression plasmid was transfected into Huh7 cells, and the effect of TRIM65 on cell growth was examined by EdU assay. The mouse subcutaneous Hep1-6 tumor-bearing model with WT and TRIM65-/- mice was established to study the role of TRIM65 in HCC. Immunohistochemistry staining, Immunofluorescence staining, qRT-PCR and western blot were performed to evaluate the effect of TRIM65 on TAM infiltration, TAM polarization and JAK1/STAT1 signaling pathway. RESULTS: Bioinformatics analysis revealed that TRIM65 was upregulated in 16 types of cancer especially in HCC, and high level of TRIM65 was strongly correlated with higher tumor grades, TNM stages, and worse prognosis of patients with HCC as well as immune infiltration level of macrophages (M0, M1, and M2). Moreover, we observed that TRIM65 shRNA-mediated TRIM65 knockdown significantly inhibited the HepG2 cells growth while TRIM65 overexpression highly increased the Huh7 cells growth in vitro. TRIM65 knockout significantly inhibited the tumor growth as well as macrophages polarization towards M2 but promoted macrophages polarization towards M1 in vivo. Mechanistically, the results demonstrate that TRIM65 knockout promoted macrophage M1 polarization in conditioned medium-stimulated peritoneal macrophages and in tumor tissues by activating JAK1/STAT1 signaling pathway. CONCLUSIONS: Taken together, our study suggests that tumor cells utilize TRIM65-JAK1/STAT1 axis to inhibit macrophage M1 polarization and promote tumor growth, reveals the role of TRIM65 in TAM-targeting tumor immunotherapy.

Osteopontin Promotes Macrophage M1 Polarization by Activation of the JAK1/STAT1/HMGB1 Signaling Pathway in Nonalcoholic Fatty Liver Disease.

Background and Aims: Osteopontin (OPN) is reported to be associated with the pathogenesis of nonalcoholic fatty liver disease (NAFLD). However, the function of OPN in NAFLD is still inconclusive. Therefore, our aim in this study was to evaluate the role of OPN in NAFLD and clarify the involved mechanisms. Methods: We analyzed the expression change of OPN in NAFLD by bioinformatic analysis, qRT-PCR, western blotting and immunofluorescence staining. To clarify the role of OPN in NAFLD, the effect of OPN from HepG2 cells on macrophage polarization and the involved mechanisms were examined by FACS and western blotting. Results: OPN was significantly upregulated in NAFLD patients compared with normal volunteers by microarray data, and the high expression of OPN was related with disease stage and progression. OPN level was also significantly increased in liver tissue samples of NAFLD from human and mouse, and in HepG2 cells treated with oleic acid (OA). Furthermore, the supernatants of OPN-treated HepG2 cells promoted the macrophage M1 polarization. Mechanistically, OPN activated the janus kinase 1(JAK1)/signal transducers and activators of transcription 1 (STAT1) signaling pathway in HepG2 cells, and consequently HepG2 cells secreted more high-mobility group box 1 (HMGB1), thereby promoting macrophage M1 polarization. Conclusions: OPN promoted macrophage M1 polarization by increasing JAK1/STAT1-induced HMGB1 secretion in hepatocytes.

Comprehensive analysis of HOX family genes in endometrial cancer.

Background: Endometrial cancer (EC) is one of the most prevalent malignancies in the female population. Homeoboxes (HOXs) are a large family of transcription factors that have a variety of functions in biological processes (BPs), including developmental differentiation, and their dysregulated expression has been implicated in tumorigenesis. However, the involvement of HOXs in EC has received little attention. Thus, we aimed to identify the potential role of HOXs in EC from a multi-omics perspective through bioinformatics analysis. Methods: We obtained transcriptome, mutation, and methylation data and the corresponding clinical data for normal and tumor tissues from The Cancer Genome Atlas (TCGA) database. Abnormal expression of HOXs in EC was identified via differential analysis, and the diagnostic value of HOXs in EC was assessed with the receiver operating characteristic (ROC) method. Univariate and multivariate Cox regression models were employed to evaluate the predictive efficacy of HOXs in EC. Methylation and mutation analyses revealed epigenetic and genetic sequence alterations in HOXs. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the altered immune microenvironment in EC. Moreover, the gene activity and pathway enrichment of downstream key HOX genes were revealed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis in EC. Results: HOXs were found to be linked to the growth of EC and potentially playing a role in establishing the tumor immune microenvironment in patients with EC. HOXB9 was found to be a vital prognostic molecule in patients with EC and is expected to contribute to a novel treatment approach. Conclusions: We used bioinformatics techniques to clarify the potential role of HOXs from a multi-omics perspective, and our findings provide a foundation for future investigations into the molecular mechanisms of HOXs in EC.

Assessing electrocardiogram changes after ischemic stroke with artificial intelligence.

OBJECTIVE: Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI). METHODS: We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs. Next, we trained the convolutional neural network (CNN), random forest (RF) and support vector machine (SVM) models to automatically detect the changes in the ECGs; Additionally, We compared the CNN scores of good prognosis (mRS ≤ 2) and poor prognosis (mRS > 2) to assess the prognostic value of CNN model. Finally, we used gradient class activation map (Grad-CAM) to localize the key abnormalities. RESULTS: Among the 3506 ECGs of the IS patients, 2764 ECGs (78.84%) led to an abnormal diagnosis. Then we divided ECGs in the primary cohort into three groups, normal ECGs (N-Ns), abnormal ECGs after the first ischemic stroke (A-ISs), and normal ECGs after the first ischemic stroke (N-ISs). Basic demographic and ECG parameter analyses showed that heart rate, QT interval, and P-R interval were significantly different between 673 N-ISs and 3546 N-Ns (p < 0.05). The CNN has the best performance among the three models in distinguishing A-ISs and N-Ns (AUC: 0.88, 95%CI = 0.86-0.90). The prediction scores of the A-ISs and N-ISs obtained from the all three models are statistically different from the N-Ns (p < 0.001). Futhermore, the CNN scores of the two groups (mRS > 2 and mRS ≤ 2) were significantly different (p < 0.05). Finally, Grad-CAM revealed that the V4 lead may harbor the highest probability of abnormality. CONCLUSION: Our study showed that a high proportion of post-IS ECGs harbored abnormal changes. Our CNN model can systematically assess anomalies in and prognosticate post-IS ECGs.

Prognostic signature for hepatocellular carcinoma based on 4 pyroptosis-related genes.

BACKGROUND: Hepatocellular carcinoma (HCC) is a cancer with a poor prognosis. Many recent studies have suggested that pyroptosis is important in tumour progression. However, the role of pyroptosis-related genes (PRGs) in HCC remains unclear. MATERIALS AND METHODS: We identified differentially expressed PRGs in tumours versus normal tissues. Through univariate, LASSO, and multivariate Cox regression analyses, a prognostic PRG signature was established. The signature effectiveness was evaluated by time-dependent receiver operating characteristic (t-ROC) curve and Kaplan-Meier (KM) survival analysis. The signature was validated in the ICGC (LIRI-JP) cohort. In addition, single-sample gene enrichment analysis (ssGSEA) showed the infiltration of major immune cell types and the activity of common immune pathways in different subgroups. RESULTS: Twenty-nine pyroptosis-related DEGs from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset were detected, and four genes (CTSV, CXCL8, MKI67 and PRF1) among them were selected to construct a prognostic signature. Then, the patients were divided into high- and low-risk groups. The pyroptosis-related signature was significantly associated with overall survival (OS). In addition, the patients in the high-risk group had lower levels of immune infiltration. CONCLUSION: The prognostic signature for HCC based on 4 pyroptosis-related genes has reliable prognostic and predictive value for HCC patients.

Abnormal spindle-like microcephaly-associated protein promotes proliferation by regulating cell cycle in epithelial ovarian cancer.

Background: Epithelial ovarian cancer (EOC) ranks first for female gynecological tumor-related deaths. Due to the limited efficacy of traditional chemotherapy strategies, potential therapeutic targets are urgently needed. Previous studies have reported a relationship between abnormal spindle-like microcephaly-associated protein (ASPM) and ovarian cancer based on immunohistochemistry (IHC) and bioinformatics analysis. However, the potential role of ASPM in the proliferation of ovarian cancer cells and its molecular mechanism remain to be elucidated. Therefore, we aimed to further investigate the potential role of ASPM and its underlying mechanism in EOC using integrated online databases, clinical samples, and cell models. Methods: We used online databases (Gene Expression Profiling Interactive Analysis, Cbioportal and Kaplan-Meier Plotter) to analyze differential ASPM expression in ovarian carcinoma and explore its prognostic value in ovarian cancer (OvCa) patients. Immunohistochemistry staining based on a clinical tissue microarray (TMA) comprised 75 cases of EOC tissue and 5 cases of adjacent normal ovary tissue was used to detect the ASPM expression and analyze the relationship between ASPM expression and EOC characteristics. Various cell function experiments related to tumorigenesis were performed including the CCK8 assay, 5-ethynyl-2'-deoxyuridine (EdU), colony formation assay and Transwell assay in EOC cell models (A2780 and OVCAR3) with knocked down ASPM by small interfering RNA (siRNA) to observe its role. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was conducted to determine the signaling pathways in which ASPM was involved in the pathogenesis of ovarian cancer. Analysis of cell cycle distribution using flow cytometry was further performed to verify the pathways. Results: The expression profile based on data from The Cancer Genome Atlas (TCGA) database confirmed ASPM expression in EOC was higher compared with normal tissue, and further analysis suggested that higher expression was correlated with worse patient prognosis. Immunohistochemical analysis further indicated that ASPM was highly expressed in OvCa tissues and associated with a higher pathological stage, grade, and positive lymphatic metastasis. Cell models with knocked down ASPM by small interfering RNA (siRNA) significantly inhibited proliferation and migration. KEGG pathway enrichment and cell cycle analysis showed that ASPM silencing could inhibit ovarian cancer cell proliferation via synthesis (S) phase arrest. Conclusions: Our study confirmed that ASPM promoted proliferation and caused S phase arrest in EOC cells. ASPM may become a potential molecular marker for early screening and a valuable therapeutic target in EOC. Keywords: Abnormal spindle-like microcephaly-associated protein (ASPM); epithelial ovarian cancer (EOC); prognosis; proliferation.

Fast path planning algorithm for large-aperture aspheric optical elements based on minimum object depth and a self-optimized overlap coefficient.

In the procedure of surface defects detection of large-aperture aspheric optical elements, it is necessary to scan the surface of the element to achieve full coverage inspection. Since the curvature of the aspherical element is constantly changing from the center to the edge, it is of great difficulty to carry out efficient path planning. In addition, the machine vision system is a microscopic system with limited depth of field, and the sub-aperture imaging of aspherical elements has a visual depth along the object side. When the object depth is greater than the depth of field, out-of-focus blur will generate, so the object depth needs to be as small as possible. In response to these problems, this paper proposes a fast path planning algorithm based on the minimum object depth of a sub-aperture. To ensure minimum object depth, the machine vision system collects images along the normal direction of the sub-aperture plane. To address the problem of the surface curvatures of aspheric elements being different and the overlap coefficient difficult to determine, this paper proposes an image processing based overlap coefficient self-optimization algorithm. When scanning with full coverage of elements, there is only one connected domain in the horizontal projection image of all sub-apertures. According to this premise, the overlap coefficient is optimized through an image processing method to obtain a local optimal path planning strategy. According to the obtained path planning strategy, combining the component parameters and mechanical structure, the mapping matrix of the path planning algorithm transplanted to the detection system is calculated. Through computer programming, automatic sub-aperture acquisition is realized, and the self-edited sub-aperture stitching program is applied to reconstruct the collected sub-apertures. Our algorithm can complete path planning within 5 s, and the experimental results show that the maximum stitching misalignment error of the collected sub-apertures is no more than four pixels, and the average is one pixel. The reconstruction accuracy satisfies the needs of subsequent image processing and digital quantization.

Genome-wide screening for the G-protein-coupled receptor (GPCR) pathway-related therapeutic gene RGS19 (regulator of G protein signaling 19) in bladder cancer.

Bladder cancer is one of the most severe genitourinary cancers, causing high morbidity worldwide. However, the underlying molecular mechanism is not clear, and it is urgent to find target genes for treatment. G-protein-coupled receptors are currently a target of high interest for drug design. Thus, we aimed to identify a target gene-related to G-protein-coupled receptors for therapy. We used The Cancer Genome Atlas (TCGA) and DepMap databases to obtain the expression and clinical data of RGS19. The results showed that RGS19 was overexpressed in a wide range of tumor, especially bladder cancer. We also explored its effect on various types of cancer. High expression of RGS19 was also shown to be significantly associated with poor prognosis. Cell models were constructed for cell cycle detection. shRGS19 can halt the cell cycle at a polyploid point. RGS19 is a G-protein-coupled receptor signaling pathway-related gene with a significant effect on survival. We chose RGS19 as a therapeutic target gene in bladder cancer. The drug GSK1070916 was found to inhibit the effect of RGS19 via cell rescue experiments in vitro.

Identification of a novel pyroptosis-related gene signature correlated with the prognosis of diffuse glioma patients.

BACKGROUND: Diffuse glioma is the most common primary tumor of the central nervous system and has a poor prognosis. Recently, a new type of programmed cell death (PCD), pyroptosis, has been found to be widely involved in the process of tumor diseases. However, the expression of pyroptosis-related genes (PRGs) in diffuse gliomas and their relationship with prognosis have rarely been evaluated. METHODS: In this study, we obtained RNA sequencing and clinical data from the Cancer Genome Atlas (TCGA) database and the Chinese Glioma Genome Atlas (CGGA) of diffuse glioma patients. Simultaneously, differentially expressed PRGs between TCGA-Glioma tumor samples and the normal brain samples from the Genome Tissue Expression (GTEx) were investigated. Besides, univariate and multivariate Cox regression analysis were performed to identify and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve and principal component analysis (PCA) was undertaken to assess the prognostic capacity of the signature. Gene set enrichment analyses (GSEA) and single sample GSEA (ssGSEA) were used to further understand the molecular mechanisms and the difference of immune microenvironment. External validation of two separate cohorts from the CGGA database was then performed. RESULTS: Caspase 3 (CASP3) and interleukin-18 (IL18) were identified as potential prognostic biomarkers. A novel prognostic model was constructed to predict diffuse glioma patients' overall survival (OS) time. Patients in high-risk subgroup had shorter survival than those with high-risk with P<0.0001. GSEA and ssGSEA showed the activation of immune-related pathways and the extensive infiltration of immune cells [such as cytotoxic T cells, dendritic cells (DC), natural killer T cell (NKT), induced regulatory T cells (iTreg), naturally occurring regulatory T cells (nTreg)] in high-risk subgroup. CONCLUSIONS: A novel two-PRGs prognostic signature based on gene expression was identified, which could predict diffuse glioma patients' OS time. Pyroptosis may be involved in the establishment of immune microenvironment in diffuse glioma.

Identification of core genes in ovarian cancer by an integrative meta-analysis.

BACKGROUND: Epithelial ovarian cancer is one of the most severe public health threats in women. Since it is still challenging to screen in early stages, identification of core genes that play an essential role in epithelial ovarian cancer initiation and progression is of vital importance. RESULTS: Seven gene expression datasets (GSE6008, GSE18520, GSE26712, GSE27651, GSE29450, GSE36668, and GSE52037) containing 396 ovarian cancer samples and 54 healthy control samples were analyzed to identify the significant differentially expressed genes (DEGs). We identified 563 DEGs, including 245 upregulated and 318 downregulated genes. Enrichment analysis based on the gene ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways showed that the upregulated genes were significantly enriched in cell division, cell cycle, tight junction, and oocyte meiosis, while the downregulated genes were associated with response to endogenous stimuli, complement and coagulation cascades, the cGMP-PKG signaling pathway, and serotonergic synapse. Two significant modules were identified from a protein-protein interaction network by using the Molecular Complex Detection (MCODE) software. Moreover, 12 hub genes with degree centrality more than 29 were selected from the protein-protein interaction network, and module analysis illustrated that these 12 hub genes belong to module 1. Furthermore, Kaplan-Meier analysis for overall survival indicated that 9 of these hub genes was correlated with poor prognosis of epithelial ovarian cancer patients. CONCLUSION: The present study systematically validates the results of previous studies and fills the gap regarding a large-scale meta-analysis in the field of epithelial ovarian cancer. Furthermore, hub genes that could be used as a novel biomarkers to facilitate early diagnosis and therapeutic approaches are evaluated, providing compelling evidence for future genomic-based individualized treatment of epithelial ovarian cancer.