A novel online calculator based on inflammation-related endotypes and clinical features to predict postoperative pulmonary infection in patients with cervical spinal cord injury.

BACKGROUND: Postoperative pulmonary infection (POI) of patients with cervical spinal cord injury (CSCI) is highly heterogeneous, while the potential endotypes and related risk factors remain unclear. METHODS: A retrospective collection of 290 CSCI patients was conducted from January 2010 to July 2024 using 1:1 propensity score matching to compare POI (n = 145) and non-POI (n = 145) groups. We generated laboratory examination data from admission patients and identified endotypes using unsupervised consensus clustering and machine learning. CSCI patients were randomly assigned to the training set (n = 203) and internal validation set (n = 87). A separate cohort comprising 245 CSCI patients were used for external validation. Independent predictors for POI were identified using univariate and multivariate logistic regression. A nomogram and an online calculator were developed and validated, both internally and externally. RESULTS: Two inflammation-related endotypes were identified: high inflammation endotype (endotype C1) and low inflammation endotype (endotype C2). Eight predictors for POI were identified (including age, operation duration, number of surgical segments, time between injury and surgery, preoperative steroid pulse, American Spinal Injury Association (ASIA) grade, smoking history, and inflammation-related endotype). A nomogram integrating the risk factors showed excellent discrimination in the training set (AUC, 0.976; 95% CI 0.956-0.996), internal validation set (AUC, 0.993; 95% CI 0.981-1.000), and external validation set (AUC, 0.799; 95%CI 0.744-0.854). Calibration curves demonstrated excellent fit, and decision curves highlighted its favorable clinical value. An online calculator (https://tjspine.shinyapps.io/dynnomapp/) was constructed to improve the convenience and efficiency of our prediction model. CONCLUSIONS: We identified inflammation-related endotype and constructed a web-based calculator for predicting POI in patients with CSCI, exhibiting excellent clinical utility.

A 10-year mono-center study on patients with burns ≥70% TBSA: prediction model construction and multi-center validation: retrospective cohort.

BACKGROUND: Burn injuries with ≥70% total body surface area (TBSA) are especially acute and life-threatening, leading to severe complications and terrible prognosis, while a powerful model for prediction of overall survival (OS) is lacked. The objective of this study is to identify prognostic factors for the OS of patients with burn injury ≥70% TBSA, construct and validate a feasible predictive model. MATERIALS AND METHODS: Patients diagnosed with burns ≥70% TBSA admitted and treated between 2010 and 2020 in our hospital were included. A cohort of the patients from the Kunshan explosion were assigned as the validation set. The Chi-square test and K-M survival analysis were conducted to identify potential predictors for OS. Then, multi-variate Cox regression analysis was performed to identify the independent factors. Afterwards, we constructed a nomogram to predict OS probability. Finally, the Kunshan cohort was applied as an external validation set. RESULTS: Gender, the percentage of third- and fourth-degree burn as well as organ dysfunction were identified as significant independent factors. A nomogram only based on the factors of the individuals was built and evidenced to have promising predictive accuracy, accordance, and discrimination by both internal and external validation. CONCLUSIONS: This study recognized significant influencing factors for the OS of patients with burns ≥70% TBSA. Furthermore, our nomogram proved to be an effective tool for doctors to quickly evaluate patients' outcomes and make appropriate clinical decisions at an early stage of treatment.

A gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma.

BACKGROUND: Malignant mesothelioma is a type of infrequent tumor that is substantially related to asbestos exposure and has a terrible prognosis. We tried to produce a fibroblast differentiation-related gene set for creating a novel classification and prognostic prediction model of MESO. METHOD: Three databases, including NCBI-GEO, TCGA, and MET-500, separately provide single-cell RNA sequencing data, bulk RNA sequencing profiles of MESO, and RNA sequencing information on bone metastatic tumors. Dimensionality reduction and clustering analysis were leveraged to acquire fibroblast subtypes in the MESO microenvironment. The fibroblast differentiation-related genes (FDGs), which were associated with survival and subsequently utilized to generate the MESO categorization and prognostic prediction model, were selected in combination with pseudotime analysis and survival information from the TCGA database. Then, regulatory network was constructed for each MESO subtype, and candidate inhibitors were predicted. Clinical specimens were collected for further validation. RESULT: A total of six fibroblast subtypes, three differentiation states, and 39 FDGs were identified. Based on the expression level of FDGs, three MESO subtypes were distinguished in the fibroblast differentiation-based classification (FDBC). In the multivariate prognostic prediction model, the risk score that was dependent on the expression level of several important FDGs, was verified to be an independently effective prognostic factor and worked well in internal cohorts. Finally, we predicted 24 potential drugs for the treatment of MESO. Moreover, immunohistochemical staining and statistical analysis provided further validation. CONCLUSION: Fibroblast differentiation-related genes (FDGs), especially those in low-differentiation states, might participate in the proliferation and invasion of MESO. Hopefully, the raised clinical subtyping of MESO would provide references for clinical practitioners.

Tracing the evolving dynamics and research hotspots of microbiota and immune microenvironment from the past to the new era.

Gut microbiota can regulate many physiological processes within gastrointestinal tract and other distal sites. Dysbiosis may not only influence chronic diseases like the inflammatory bowel disease (IBD), metabolic disease, tumor and its therapeutic efficacy, but also deteriorate acute injuries. This article aims to review the documents in this field and summarize the research hotspots as well as developing processes. Gut microbiota and immune microenvironment-related documents from 1976 to 2022 were obtained from the Web of Science Core Collection database. Bibliometrics was used to assess the core authors and journals, most contributive countries and affiliations together with hotspots in this field and keyword co-occurrence analysis. Data were visualized to help comprehension. Nine hundred and twelve documents about gut microbiota and immune microenvironment were retrieved, and the annual publications increased gradually. The most productive author, country, and affiliation were "Zitvogel L," USA and "UNIV TEXAS MD ANDERSON CANC CTR," respectively. FRONTIERS IN IMMUNOLOGY, CANCERS, and INTERNATIONAL JOURNAL OF MOLECULAR SCIENCE were the periodicals with most publications. Keyword co-occurrence analysis identified three clusters, including gut microbiota, inflammation, and IBD. Combined with the visualized analysis of documents and keyword co-occurrence as well as literature reading, we recognized three key topics of gut microbiota: cancer and therapy; immunity, inflammation and IBD; acute injuries and metabolic diseases. This article revealed researches on gut microbiota and immune microenvironment were growing. More attention should be given to the latest hotspots like gut microbiota, inflammation, IBD, cancer and immunotherapy, acute traumas, and metabolic diseases.IMPORTANCEGut microbiota can regulate many physiological processes within gastrointestinal tract and other distal sites. Dysbiosis may not only influence chronic diseases like inflammatory bowel disease (IBD), metabolic disease, tumor and its therapeutic efficacy, but also deteriorate acute injuries. While the application of bibliometrics in the field of gut microbiota and immune microenvironment still remains blank, which focused more on the regulation of the gut microbiota on the immune microenvironment of different kinds of diseases. Here, we intended to review and summarize the presented documents in gut microbiota and immune microenvironment field by bibliometrics. And we revealed researches on gut microbiota and immune microenvironment were growing. More attention should be given to the latest hotspots like gut microbiota, inflammation, IBD, cancer and immunotherapy, acute traumas, and metabolic diseases.

Identification of angiogenesis-related genes signature for predicting survival and its regulatory network in glioblastoma.

Glioblastoma (GBM) is notorious for malignant neovascularization that contributes to undesirable outcome. However, its mechanisms remain unclear. This study aimed to identify prognostic angiogenesis-related genes and the potential regulatory mechanisms in GBM. RNA-sequencing data of 173 GBM patients were obtained from the Cancer Genome Atlas (TCGA) database for screening differentially expressed genes (DEGs), differentially transcription factors (DETFs), and reverse phase protein array (RPPA) chips. Differentially expressed genes from angiogenesis-related gene set were extracted for univariate Cox regression analysis to identify prognostic differentially expressed angiogenesis-related genes (PDEARGs). A risk predicting model was constructed based on 9 PDEARGs, namely MARK1, ITGA5, NMD3, HEY1, COL6A1, DKK3, SERPINA5, NRP1, PLK2, ANXA1, SLIT2, and PDPN. Glioblastoma patients were stratified into high-risk and low-risk groups according to their risk scores. GSEA and GSVA were applied to explore the possible underlying GBM angiogenesis-related pathways. CIBERSORT was employed to identify immune infiltrates in GBM. The Pearson's correlation analysis was performed to evaluate the correlations among DETFs, PDEARGs, immune cells/functions, RPPA chips, and pathways. A regulatory network centered by three PDEARGs (ANXA1, COL6A1, and PDPN) was constructed to show the potential regulatory mechanisms. External cohort of 95 GBM patients by immunohistochemistry (IHC) assay demonstrated that ANXA1, COL6A1, and PDPN were significantly upregulated in tumor tissues of high-risk GBM patients. Single-cell RNA sequencing also validated malignant cells expressed high levels of the ANXA1, COL6A1, PDPN, and key DETF (WWTR1). Our PDEARG-based risk prediction model and regulatory network identified prognostic biomarkers and provided valuable insight into future studies on angiogenesis in GBM.

Bibliometric analysis and mini-review of global research on pyroptosis in the field of cancer.

Pyroptosis is one of the mechanisms of programmed cell death (PCD) activated by inflammasomes and involved by the caspase family and the gasdermin family. During the oncogenesis and progression of tumors, pyroptosis is crucial, and complex withal. Currently, pyroptosis is the focus topic in the research field of oncology, but there is no single bibliometric analysis systematically studying 'pyroptosis and cancer'. Our study aimed to visualize the research status of pyroptosis in oncology and excavate the hotspots and prospects in this field. Furthermore, in consideration of the professional direction of researchers, we particularly emphasized articles on pyroptosis in gynecology and formed a mini systematic review. This bibliometric work integrated and analyzed all articles from ISI Web of Science: Science Citation Index Expanded (SCI-Expanded) (dated April 25th, 2022), based on quantitative and visual mapping approaches. Systematically reviewing articles on pyroptosis in gynecology helped us complement our analysis of research advancements in this field. Including 634 articles, our study found that the number of articles on pyroptosis in cancer increased exponentially in recent years. These publications came from 45 countries and regions headed by China and the US mainly aiming at the mechanism of pyroptosis in cell biology and biochemistry molecular biology, as well as the role of pyroptosis in the development and therapeutic application of various cancers. The top 20 most cited studies on this topic mostly came from the US, followed by China and England, and half of the articles cited more than 100 times in total were published in Nature. Moreover, as for gynecologic cancer, in vitro and bioinformatics analysis were the main methodology conducting to explore roles of pyroptosis-related genes (PRGs) and formation of inflammasomes in cancer progression and prognosis. Pyroptosis has evolved into a burgeoning research field in oncology. The cellular and molecular pathway mechanism of pyroptosis, as well as the effect of pyroptosis in oncogenesis, progression, and treatment have been the hot topic of the current study and provided us the future direction as the potential opportunities and challenges. We advocate more active cooperation to improve therapeutic strategies for cancer.

The Recurrent-Specific Regulation Network of Prognostic Stemness-Related Signatures in Low-Grade Glioma.

Gliomas including astrocytomas, oligodendrogliomas, mixed oligoastrocytic, and mixed glioneuronal tumors are an important group of brain tumors. Based on the 2016 WHO classification for tumors in the central nervous system, gliomas were classified into four grades, from I to IV, and brain lower grade glioma (LGG) consists of grade II and grade III. Patients with LGG may undergo recurrence, which makes clinical treatment tough. Stem cell-like features of cancer cells play a key role in tumor's biological behaviors, including tumorigenesis, development, and clinical prognosis. In this article, we quantified the stemness feature of cancer cells using the mRNA stemness index (mRNAsi) and identified stemness-related key genes based on correlation with mRNAsi. Besides, hallmark gene sets and translate factors (TFs) which were highly related to stemness-related key genes were identified. Therefore, a recurrency-specific network was constructed and a potential regulation pathway was identified. Several online databases, assay for transposase-accessible chromatin using sequencing (ATAC-seq), single-cell sequencing analysis, and immunohistochemistry were utilized to validate the scientific hypothesis. Finally, we proposed that aurora kinase A (AURKA), positively regulated by Non-SMC Condensin I Complex Subunit G (NCAPG), promoted E2F target pathway in LGG, which played an important role in LGG recurrence.

Efficacy and safety of external tissue expansion technique in the treatment of soft tissue defects: a systematic review and meta-analysis of outcomes and complication rates.

Background: Currently, various external tissue expansion devices are becoming widely used. Considering the scarcity of relevant application standards, this systematic review was performed to explore the effectiveness and safety of external tissue expansion techniques for the reconstruction of soft tissue defects. Method: A systematic review and meta-analysis on the efficacy and safety of external tissue expansion technique was conducted. A comprehensive search was performed in the following electronic databases: PubMed/Medline, Embase, Cochrane Library (Wiley Online Library), and Web of Science. Studies reporting patients with soft tissue defects under the treatment of external tissue expansion technique were included. Results: A total of 66 studies with 22 different types of external tissue expansion devices met the inclusion criteria. We performed a descriptive analysis of different kinds of devices. A single-arm meta-analysis was performed to evaluate the efficacy and safety of the external tissue expansion technique for different aetiologies. The pooled mean wound healing time among patients with defects after fasciotomy was 10.548 days [95% confidence interval (CI) = 5.796-15.299]. The pooled median wound healing times of patients with defects after excisional surgery, trauma, chronic ulcers and abdominal defects were 11.218 days (95% CI = 6.183-16.253), 11.561 days (95% CI = 7.062-16.060), 15.956 days (95% CI = 11.916-19.996) and 12.853 days (95% CI=9.444-16.227), respectively. The pooled wound healing rates of patients with defects after fasciotomy, excisional surgery, trauma, chronic ulcers and abdominal defects were 93.8% (95% CI=87.1-98.2%), 97.2% (95%CI=92.2-99.7%), 97.0% (95%CI=91.2-99.8%), 99.5% (95%CI=97.6-100%), and 96.8% (95%CI=79.2-100%), respectively. We performed a subgroup analysis in patients with diabetic ulcers and open abdominal wounds. The pooled median wound healing time of patients with diabetic ulcers was 11.730 days (95% CI = 10.334-13.125). The pooled median wound healing time of patients with open abdomen defects was 48.810 days (95% CI = 35.557-62.063) and the pooled successful healing rate was 68.8% (95% CI = 45.9-88.1%). A total of 1686 patients were included, 265 (15.7%) of whom experienced complications. The most common complication was dehiscence (n = 53, 3.14%). Conclusions: Our systematic review is the first to demonstrate the efficacy and safety of external tissue expansion in the management of soft tissue defects. However, we must interpret the meta-analysis results with caution considering the limitations of this review. Large-scale randomized controlled trials and long-term follow-up studies are still needed to confirm the effectiveness and evaluate the quality of healing.

Repression of enhancer RNA PHLDA1 promotes tumorigenesis and progression of Ewing sarcoma via decreasing infiltrating T-lymphocytes: A bioinformatic analysis.

Background: The molecular mechanisms of EWS-FLI-mediating target genes and downstream pathways may provide a new way in the targeted therapy of Ewing sarcoma. Meanwhile, enhancers transcript non-coding RNAs, known as enhancer RNAs (eRNAs), which may serve as potential diagnosis markers and therapeutic targets in Ewing sarcoma. Materials and methods: Differentially expressed genes (DEGs) were identified between 85 Ewing sarcoma samples downloaded from the Treehouse database and 3 normal bone samples downloaded from the Sequence Read Archive database. Included in DEGs, differentially expressed eRNAs (DEeRNAs) and target genes corresponding to DEeRNAs (DETGs), as well as the differentially expressed TFs, were annotated. Then, cell type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) was used to infer portions of infiltrating immune cells in Ewing sarcoma and normal bone samples. To evaluate the prognostic value of DEeRNAs and immune function, cross validation, independent prognosis analysis, and Kaplan-Meier survival analysis were implemented using sarcoma samples from the Cancer Genome Atlas database. Next, hallmarks of cancer by gene set variation analysis (GSVA) and immune gene sets by single-sample gene set enrichment analysis (ssGSEA) were identified to be significantly associated with Ewing sarcoma. After screening by co-expression analysis, most significant DEeRNAs, DETGs and DETFs, immune cells, immune gene sets, and hallmarks of cancer were merged to construct a co-expression regulatory network to eventually identify the key DEeRNAs in tumorigenesis of Ewing sarcoma. Moreover, Connectivity Map Analysis was utilized to identify small molecules targeting Ewing sarcoma. External validation based on multidimensional online databases and scRNA-seq analysis were used to verify our key findings. Results: A six-different-dimension regulatory network was constructed based on 17 DEeRNAs, 29 DETFs, 9 DETGs, 5 immune cells, 24 immune gene sets, and 8 hallmarks of cancer. Four key DEeRNAs (CCR1, CD3D, PHLDA1, and RASD1) showed significant co-expression relationships in the network. Connectivity Map Analysis screened two candidate compounds, MS-275 and pyrvinium, that might target Ewing sarcoma. PHLDA1 (key DEeRNA) was extensively expressed in cancer stem cells of Ewing sarcoma, which might play a critical role in the tumorigenesis of Ewing sarcoma. Conclusion: PHLDA1 is a key regulator in the tumorigenesis and progression of Ewing sarcoma. PHLDA1 is directly repressed by EWS/FLI1 protein and low expression of FOSL2, resulting in the deregulation of FOX proteins and CC chemokine receptors. The decrease of infiltrating T-lymphocytes and TNFA signaling may promote tumorigenesis and progression of Ewing sarcoma.

The role of tetraspanins pan-cancer.

Tetraspanins (TSPANs) are a class of four-transmembrane segmented proteins. The precise functions of TSPANs and their roles in pan-cancer are unclear. In this work, we analyzed TSPAN1, TSPAN10, TSPAN11, TSPAN12, TSPAN13, TSPAN14, TSPAN15, TSPAN16, TSPAN17, TSPAN18, TSPAN18-AS1, TSPAN19, TSPAN2, TSPAN3, TSPAN31, TSPAN32, TSPAN33, TSPAN4, TSPAN5, TSPAN6, TSPAN7, TSPAN8, TSPAN9, TSPAN9-IT1 (24 TSPAN family genes) in relation to tumor characteristics from 11,057 TCGA samples across 33 cancer types. On 24 TSPAN family genes, multidimensional studies were conducted, including gene differential expression, immunological subtype analysis, clinical analysis, stemness indices analysis, drug sensitivity analysis, alteration analysis, and multi-omics validation (including ATAC-seq validation, single-cell sequencing validation, and other external validation). Genes were differentially expressed in 33 cancers, and several of them showed high consistency in terms of tumor characteristics. In particular, the potential roles of TSPAN15 and TSPAN1 in cancer deserve further attention.

Construction of the prognostic enhancer RNA regulatory network in osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is a common malignant tumor in osteoarticular system, the 5-year overall survival of which is poor. Enhancer RNAs (eRNAs) have been implicated in the tumorigenesis of various cancer types, whereas their roles in OS tumorigenesis remains largely unclear. METHODS: Differentially expressed eRNAs (DEEs), transcription factors (DETFs), target genes (DETGs) were identified using limma (Linear Models for Microarray Analysis) package. Prognosis-related DEEs were accessed by univariate Cox regression analysis. A multivariate model was constructed to evaluate the prognosis of OS samples. Prognosis-related DEEs, DETFs, DETGs, immune cells, and hallmark gene sets were co-analyzed to construct an regulatory network. Specific inhibitors were also filtered by connectivity Map analysis. External validation and scRNA-seq analysis were performed to verify our key findings. RESULTS: 3,981 DETGs, 468 DEEs, 51 DETFs, and 27 differentially expressed hallmark gene sets were identified. A total of Multivariate risk predicting model based on 18 prognosis-related DEEs showed a high accuracy (area under curve (AUC) = 0.896). GW-8510 was the candidate inhibitor targeting prognosis-related DEEs (mean = 0.670, p < 0.001). Based on the OS tumorigenesis-related regulation network, we identified that CCAAT enhancer binding protein alpha (CEBPA, DETF) may regulate CD8A molecule (CD8A, DEE), thereby promoting the transcription of CD3E molecule (CD3E, DETG), which may affect allograft rejection based on CD8+ T cells. CONCLUSION: We constructed an eRNA-based prognostic model for predicting the OS patients' prognosis and explored the potential regulation network for OS tumorigenesis by an integrated bioinformatics analysis, providing promising therapeutic targets for OS patients.

Prognostic Ability of Enhancer RNAs in Metastasis of Non-Small Cell Lung Cancer.

(1) Background: Non-small cell lung cancer (NSCLC) is the most common lung cancer. Enhancer RNA (eRNA) has potential utility in the diagnosis, prognosis and treatment of cancer, but the role of eRNAs in NSCLC metastasis is not clear; (2) Methods: Differentially expressed transcription factors (DETFs), enhancer RNAs (DEEs), and target genes (DETGs) between primary NSCLC and metastatic NSCLC were identified. Prognostic DEEs (PDEEs) were screened by Cox regression analyses and a predicting model for metastatic NSCLC was constructed. We identified DEE interactions with DETFs, DETGs, reverse phase protein arrays (RPPA) protein chips, immunocytes, and pathways to construct a regulation network using Pearson correlation. Finally, the mechanisms and clinical significance were explained using multi-dimensional validation unambiguously; (3) Results: A total of 255 DEEs were identified, and 24 PDEEs were selected into the multivariate Cox regression model (AUC = 0.699). Additionally, the NSCLC metastasis-specific regulation network was constructed, and six key PDEEs were defined (ANXA8L1, CASTOR2, CYP4B1, GTF2H2C, PSMF1 and TNS4); (4) Conclusions: This study focused on the exploration of the prognostic value of eRNAs in the metastasis of NSCLC. Finally, six eRNAs were identified as potential markers for the prediction of metastasis of NSCLC.

Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Prostate Cancer.

Background: We planned to uncover the cancer stemness-related genes (SRGs) in prostate cancer (PCa) and its underlying mechanism in PCa metastasis. Methods: We acquired the RNA-seq data of 406 patients with PCa from the TCGA database. Based on the mRNA stemness index (mRNAsi) calculated by one-class logistic regression (OCLR) algorithm, SRGs in PCa were extracted by WGCNA. Univariate and multivariate regression analyses were applied to uncover OS-associated SRGs. Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Pearson's correlation analysis were performed to discover the possible mechanism of PCa metastasis. The significantly correlated transcription factors of OS-associated SRGs were also identified by Pearson's correlation analysis. ChIP-seq was applied to validate the binding relationship of TFs and OS-associated SRGs and spatial transcriptome and single-cell sequencing were performed to uncover the location of key biomarkers expression. Lastly, we explored the specific inhibitors for SRGs using CMap algorithm. Results: We identified 538 differentially expressed genes (DEGs) between non-metastatic and metastatic PCa. Furthermore, OS-associated SRGs were identified. The Pearson correlation analysis revealed that FOXM1 was significantly correlated with NEIL3 (correlation efficient =0.89, p < 0.001) and identified hallmark_E2F_targets as the potential pathway mechanism of NEIL3 promoting PCa metastasis (correlation efficient =0.58, p < 0.001). Single-cell sequencing results indicated that FOXM1 regulating NEIL3 may get involved in the antiandrogen resistance of PCa. Rottlerin was discovered to be a potential target drug for PCa. Conclusion: We constructed a regulatory network based on SRGs associated with PCa metastasis and explored possible mechanism.

Prognostic Bone Metastasis-Associated Immune-Related Genes Regulated by Transcription Factors in Mesothelioma.

Mesothelioma (MESO) is a mesothelial originate neoplasm with high morbidity and mortality. Despite advancement in technology, early diagnosis still lacks effectivity and is full of pitfalls. Approaches of cancer diagnosis and therapy utilizing immune biomarkers and transcription factors (TFs) have attracted more and more attention. But the molecular mechanism of these features in MESO bone metastasis has not been thoroughly studied. Utilizing high-throughput genome sequencing data and lists of specific gene subsets, we performed several data mining algorithm. Single-sample Gene Set Enrichment Analysis (ssGSEA) was applied to identify downstream immune cells. Potential pathways involved in MESO bone metastasis were identified using Gene Oncology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Cox regression analysis. Ultimately, a model to help early diagnosis and to predict prognosis was constructed based on differentially expressed immune-related genes between bone metastatic and nonmetastatic MESO groups. In conclusion, immune-related gene SDC2, regulated by TFs TCF7L1 and POLR3D, had an important role on immune cell function and infiltration, providing novel biomarkers and therapeutic targets for metastatic MESO.

Ral GEF with the PH Domain and SH3 Binding Motif 1 Regulated by Splicing Factor Junction Plakoglobin and Pyrimidine Metabolism Are Prognostic in Uterine Carcinosarcoma.

Uterine carcinosarcoma (UCS) is a highly invasive malignant tumor that originated from the uterine epithelium. Many studies suggested that the abnormal changes of alternative splicing (AS) of pre-mRNA are related to the occurrence and metastasis of the tumor. This study investigates the mechanism of alternative splicing events (ASEs) in the tumorigenesis and metastasis of UCS. RNA-seq of UCS samples and alternative splicing event (ASE) data of UCS samples were downloaded from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases, several times. Firstly, we performed the Cox regression analysis to identify the overall survival-related alternative splicing events (OSRASEs). Secondly, a multivariate model was applied to approach the prognostic values of the risk score. Afterwards, a coexpressed network between splicing factors (SFs) and OSRASEs was constructed. In order to explore the relationship between the potential prognostic signaling pathways and OSRASEs, we fabricated a network between these pathways and OSRASEs. Finally, validations from multidimension platforms were used to explain the results unambiguously. 1,040 OSRASEs were identified by Cox regression. Then, 6 OSRASEs were incorporated in a multivariable model by Lasso regression. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve was 0.957. The risk score rendered from the multivariate model was corroborated to be an independent prognostic factor (P < 0.001). In the network of SFs and ASEs, junction plakoglobin (JUP) noteworthily regulated RALGPS1-87608-AT (P < 0.001, R = 0.455). Additionally, RALGPS1-87608-AT (P = 0.006) showed a prominent relationship with distant metastasis. KEGG pathways related to prognosis of UCS were selected by gene set variation analysis (GSVA). The pyrimidine metabolism (P < 0.001, R = -0.470) was the key pathway coexpressed with RALGPS1. We considered that aberrant JUP significantly regulated RALGPS1-87608-AT and the pyrimidine metabolism pathway might play a significant part in the metastasis and prognosis of UCS.

Identification of Key eRNAs for Spinal Cord Injury by Integrated Multinomial Bioinformatics Analysis.

Background: Spinal cord injury (SCI) is a severe neurological deficit affecting both young and older people worldwide. The potential role of key enhancer RNAs (eRNAs) in SCI remains elusive, which is a prominent challenge in the trauma repair process. This study aims to investigate the roles of key eRNAs, transcription factors (TFs), signaling pathways, and small-molecule inhibitors in SCI using multi-omics bioinformatics analysis. Methods: Microarray data of peripheral blood mononuclear cell (PBMC) samples from 27 healthy volunteers and 25 chronic-phase SCI patients were retrieved from the Gene Expression Omnibus database. Differentially expressed transcription factors (DETFs), differentially expressed enhancer RNAs (DEeRNAs), and differentially expressed target genes (DETGs) were identified using the Linear Models for Microarray Data (limma) package. Fraction of immune cells was estimated using CIBERSORT algorithm. Gene Set Variation Analysis (GSVA) was applied to identify the downstream signaling pathways. The eRNA regulatory network was constructed based on the correlation results. Connectivity Map (CMap) database was used to find potential drugs for SCI patients. The cellular communication analysis was performed to explore the molecular regulation mechanism of SCI based on single-cell RNA sequencing (scRNA-seq) data. Chromatin immunoprecipitation sequencing (ChIP-seq) and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data were used to validate the key regulatory mechanisms. scRNA-seq dataset was used to validate the cell subtype localization of the key eRNAs. Results: In total, 21 DETFs, 24 DEeRNAs, and 829 DETGs were identified. A regulatory network of 13 DETFs, six DEeRNAs, seven DETGs, two hallmark pathways, two immune cells, and six immune pathways was constructed. The link of Splicing factor proline and glutamine rich (SFPQ) (TF) and vesicular overexpressed in cancer prosurvival protein 1 (VOPP1) (eRNA) (R = 0.990, p < 0.001, positive), VOPP1 (eRNA) and epidermal growth factor receptor (EGFR) (target gene) (R = 0.974, p < 0.001, positive), VOPP1, and T helper (Th) cells (R = -0.987, p < 0.001, negative), and VOPP1 and hallmark coagulation (R = 0.937, p < 0.001, positive) was selected. Trichostatin A was considered the best compound target to SCI-related eRNAs (specificity = 0.471, p < 0.001). Conclusion: VOPP1, upregulated by SFPQ, strengthened the transient expression of EGFR. Th cells and coagulation were the potential downstream pathways of VOPP1. This regulatory network and potential inhibitors provide novel diagnostic biomarkers and therapeutic targets for SCI.

Identification of Stemness-Related Genes for Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma by Integrated Bioinformatics Analysis.

BACKGROUND: Invasion and metastasis of cervical cancer are the main factors affecting the prognosis of patients with cervical squamous cell carcinoma (CESC). Therefore, it is of vital importance to find novel biomarkers that are associated with CESC invasion and metastasis, which will aid in the amelioration of individualized therapeutic methods for advanced patients. METHODS: The gene expression profiles of 10 metastatic and 116 non-metastatic samples were downloaded from The Cancer Genome Atlas (TCGA), where differentially expressed genes (DEGs) were defined. Weighted gene correlation network analysis (WGCNA) was employed to identify the stemness-related genes (SRGs). Univariate and multivariate regression analyses were used to identify the most significant prognostic key genes. Differential expression analysis of transcription factor (TF) and Gene Set Variation Analysis (GSVA) were utilized to explore the potential upstream regulation of TFs and downstream signaling pathways, respectively. Co-expression analysis was performed among significantly enriched TFs, key SRGs, and signaling pathways to construct a metastasis-specific regulation network in CESC. Connectivity Map (CMap) analysis was performed to identify bioactive small molecules which might be potential inhibitors for the network. Additionally, direct regulatory patterns of key genes were validated by ChIP-seq and ATAC-seq data. RESULTS: DEGs in yellow module acquired via WGCNA were defined as key genes which were most significantly related to mRNAsi. A multivariate Cox regression model was constructed and then utilized to explore the prognostic value of key SRGs by risk score. Area under curve (AUC) of the receiver operating characteristic (ROC) curve was 0.842. There was an obvious co expression pattern between the TF NR5A2 and the key gene VIM (R = 0.843, p < 0.001), while VIM was also significantly co-expressed with hallmark epithelial mesenchymal transition (EMT) signaling pathway (R = 0.318, p < 0.001). Naringenin was selected as the potential bioactive small molecule inhibitor for metastatic CESC based on CMap analysis. CONCLUSIONS: VIM positively regulated by NR5A2 affected EMT signaling pathways in metastatic CESC, and naringenin was the inhibitor for the treatment of metastatic CESC via suppressing cancer stemness. This hypothetical signaling axis and potential inhibitors provide biomarkers and novel therapeutic targets for metastatic CESC.

Identification of prognostic and metastasis-related alternative splicing signatures in hepatocellular carcinoma.

As the most common neoplasm in digestive system, hepatocellular carcinoma (HCC) is one of the most important leading cause of cancer deaths worldwide. Its high-frequency metastasis and relapse rate lead to the poor survival of HCC patients. However, the mechanism of HCC metastasis is still unclear. Alternative splicing events (ASEs) have a great effect in cancer development, progression and metastasis. We downloaded RNA sequencing and seven types of ASEs data of HCC samples, in order to explore the mechanism of ASEs underlying tumorigenesis and metastasis of HCC. The data were taken from the The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases. Univariate Cox regression analysis was used to determine a total of 3197 overall survival-related ASEs (OS-SEs). And based on five OS-SEs screened by Lasso regression, we constructed a prediction model with the Area Under Curve of 0.765. With a good reliability of the model, the risk score was also proved to be an independent predictor. Among identified 390 candidate SFs, Y-box protein 3 (YBX3) was significantly correlated with OS and metastasis. Among 177 ASEs, ATP-binding cassette subfamily A member 6 (ABCA6)-43162-AT and PLIN5-46808-AT were identified both associated with OS, bone metastasis and co-expressed with SFs. Then we identified primary bile acid biosynthesis as survival-related (KEGG) pathway by Gene Set Variation Analysis (GSVA) and univariate regression analysis, which was correlated with ABCA6-43162-AT and PLIN5-46808-AT. Finally, we proposed that ABCA6-43162-AT and PLIN5-46808-AT may contribute to HCC poor prognosis and metastasis under the regulation of aberrant YBX3 through the pathway of primary bile acid biosynthesis.