Identification of uterine leiomyosarcoma-associated hub genes and immune cell infiltration pattern using weighted co-expression network analysis and CIBERSORT algorithm

Identification of disulfidptosis-associated genes and characterization of immune cell infiltration in thyroid carcinoma

OBJECTIVE

The primary objective of this study is to conduct a comprehensive screening and analysis of differentially expressed genes related to disulfidoptosis (DEDRGs) in thyroid carcinoma (THCA). This entails delving into the intricate characterization of immune cell infiltration within the THCA context and subsequently formulating and validating a novel prognostic model.

METHOD

To achieve our objectives, we first delineated two distinct subtypes of disulfidoptosis-related genes (DRGs) via consensus clustering methodology. Subsequently, employing the limma R package, we identified the DEDRGs critical for our investigation. These DEDRGs underwent meticulous validation across various databases, alongside an in-depth analysis of gene regulation. Employing functional enrichment techniques, we explored the potential molecular mechanisms underlying disulfidoptosis in THCA. Furthermore, we scrutinized the immune landscape within the two identified subtypes utilizing CIBERSORT and ESTIMATE algorithms. The construction of the prognostic model for THCA entailed intricate methodologies including univariate, multivariate Cox regression, and LASSO regression algorithms. The validity and efficacy of our prognostic model were corroborated through Kaplan-Meier survival curves and ROC curves. Additionally, a nomogram was meticulously formulated to facilitate the prediction of patient prognosis. To fortify our findings, we conducted a comprehensive Bayesian co-localization analysis coupled with rigorous in vitro experimentation, aimed at unequivocally establishing the validity of the identified DEDRGs.

RESULT

Our analyses unveiled Cluster C1, characterized by elevated expression levels of DEDRGs, as harboring a favorable prognosis accompanied by abundant immune cell infiltration. Correlation analyses underscored predominantly positive associations among the DEDRGs, further affirming their significance in THCA. Differential expression patterns of DEDRGs between tumor samples and normal tissues were evident across the GEPIA and HPA databases. Insights from the TIMER database underscored a robust correlation between DEDRGs and immune cell infiltration. KEGG analysis elucidated the enrichment of DEDRGs primarily in pivotal pathways including MAPK, PPAR signaling pathway, and Proteoglycans in cancer. Furthermore, analyses using CIBERSORT and ESTIMATE algorithms shed light on the crucial role played by DEDRGs in shaping the immune microenvironment. The prognostic model, anchored by five genes intricately associated with THCA prognosis, exhibited commendable predictive accuracy and was intricately linked to the tumor immune microenvironment. Notably, patients categorized with low-risk scores stood to potentially benefit more from immunotherapy. The validation of DEDRGs unequivocally underscores the protective role of INF2 in THCA.

CONCLUSION

In summary, our study delineates two discernible subtypes intricately associated with DRGs, revealing profound disparities in immune infiltration and survival prognosis within the THCA milieu. The implications of our findings extend to potential treatment strategies for THCA patients, which could entail targeted interventions directed towards DEDRGs and prognostic genes, thereby influencing disulfidptosis and the immune microenvironment. Moreover, the robust predictive capability demonstrated by our prognostic model, based on the five genes (ANGPTL7, FIRRE, ODAPH, PROKR1, SFRP5), underscores its potential clinical utility in guiding personalized therapeutic approaches for THCA patients.

Potential Markers to Differentiate Uterine Leiomyosarcomas from Leiomyomas

Uterine leiomyomas (ULM) are the most common benign tumors of the female genitalia, while uterine leiomyosarcomas (ULMS) are rare. The sarcoma is diffuse growth, prone to hematogenous metastasis, and has a poor prognosis. Due to their similar clinical symptoms and morphological features, it is sometimes difficult to distinguish them, and the final diagnosis depends on histological diagnosis. Misdiagnosis of ULM as ULMS will lead to more invasive and extensive surgery when it is not needed, while misdiagnosis of ULMS as ULM may lead to delayed treatment and poor prognosis. This review searched and studied the published articles on ULM and ULMS, and summarized the potential markers for the differential diagnosis of ULMS. These markers will facilitate differential diagnosis and personalized treatment, providing timely diagnosis and potentially better prognosis for patients.

Integrative analysis from multi-center studies identifies a weighted gene co-expression network analysis-based Tregs signature in ovarian cancer

Ovarian cancer (OC) is a malignancy associated with poor prognosis and has been linked to regulatory T cells (Tregs) in the immune microenvironment. Nevertheless, the association between Tregs-related genes (TRGs) and OC prognosis remains incompletely understood. The xCell algorithm was used to analyze Tregs scores across multiple cohorts. Weighted gene co-expression network analysis (WGCNA) was utilized to identify potential TRGs and molecular subtypes. Furthermore, we used nine machine learning algorithms to create risk models with prognostic indicators for patients. Reverse transcription-quantitative polymerase chain reaction and immunofluorescence staining were used to demonstrate the immunosuppressive ability of Tregs and the expression of key TRGs in clinical samples. Our study found that higher Tregs scores were significantly correlated with poorer overall survival. Recurrent patients exhibited increased Tregs infiltration and reduced CD8+ T cell. Moreover, molecular subtyping using seven key TRGs revealed that subtype B exhibited higher enrichment of multiple oncogenic pathways and had a worse prognosis. Notably, subtype B exhibited high Tregs levels, suggesting immune suppression. In addition, we validated machine learning-derived prognostic models across multiple platform cohorts to better distinguish patient survival and predict immunotherapy efficacy. Finally, the differential expression of key TRGs was validated using clinical samples. Our study provides novel insights into the role of Tregs in the immune microenvironment of OC. We identified potential therapeutic targets derived from Tregs (CD24, FHL2, GPM6A, HOXD8, NAP1L5, REN, and TOX3) for personalized treatment and created a machining learning-based prognostic model for OC patients, which could be useful in clinical practice.

Post-Acute Myocardial Infarction Heart Failure Core Genes and Relevant Signaling Pathways

ABSTRACT

There is increasing concern about heart failure after myocardial infarction and the current clinical treatment measures for ventricular remodeling. Herein, we present the results of differential gene analysis, pathway enrichment analysis, and characteristic gene screening. Our study identifies 4 core genes ( KLRC2 , SNORD105 , SNORD45B , and RNU5A-1 ) associated with post-acute myocardial infarction (AMI) heart failure. The authors discuss the significance of the identified core genes, their potential implications in immune dysfunction and heart failure, and their relevance to disease regulatory genes. The study concludes by emphasizing the importance of clinical relevance in molecular research and suggests potential therapeutic targets for post-AMI heart failure.

Machine learning-based mRNA signature in early acute myocardial infarction patients: the perspective toward immunological, predictive, and personalized

Patients diagnosed with stable coronary artery disease (CAD) are at continued risk of experiencing acute myocardial infarction (AMI). This study aims to unravel the pivotal biomarkers and dynamic immune cell changes, from an immunological, predictive, and personalized viewpoint, by implementing a machine-learning approach and a composite bioinformatics strategy. Peripheral blood mRNA data from different datasets were analyzed, and CIBERSORT was used for deconvoluting human immune cell subtype expression matrices. Weighted gene co-expression network analysis (WGCNA) in single-cell and bulk transcriptome levels was conducted to explore possible biomarkers for AMI, with a particular emphasis on examining monocytes and their involvement in cell-cell communication. Unsupervised cluster analysis was performed to categorize AMI patients into different subtypes, and machine learning methods were employed to construct a comprehensive diagnostic model to predict the occurrence of early AMI. Finally, RT-qPCR on peripheral blood samples collected from patients validated the clinical utility of the machine learning-based mRNA signature and hub biomarkers. The study identified potential biomarkers for early AMI, including CLEC2D, TCN2, and CCR1, and found that monocytes may play a vital role in AMI samples. Differential analysis revealed that CCR1 and TCN2 exhibited elevated expression levels in early AMI compared to stable CAD. Machine learning methods showed that the glmBoost+Enet [alpha=0.9] model achieved high predictive accuracy in the training set, external validation sets, and clinical samples in our hospital. The study provided comprehensive insights into potential biomarkers and immune cell populations involved in the pathogenesis of early AMI. The identified biomarkers and the constructed comprehensive diagnostic model hold great promise for predicting the occurrence of early AMI and can serve as auxiliary diagnostic or predictive biomarkers.

DPP6 and MFAP5 are associated with immune infiltration as diagnostic biomarkers in distinguishing uterine leiomyosarcoma from leiomyoma

Objective

Uterine leiomyosarcoma (ULMS) is the most common subtype of uterine sarcoma and is difficult to discern from uterine leiomyoma (ULM) preoperatively. The aim of the study was to determine the potential and significance of immune-related diagnostic biomarkers in distinguishing ULMS from ULM.

Methods

Two public gene expression profiles (GSE36610 and GSE64763) from the GEO datasets containing ULMS and ULM samples were downloaded. Differentially expressed genes (DEGs) were selected and determined among 37 ULMS and 25 ULM control samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Disease Ontology (DO) enrichment analyses as well as gene set enrichment analysis (GSEA). The candidate biomarkers were identified by least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analyses. The receiver operating characteristic curve (ROC) was applied to evaluate diagnostic ability. For further confirmation, the biomarker expression levels and diagnostic value in ULMS were verified in the GSE9511 and GSE68295 datasets (12 ULMS and 10 ULM), and validated by immunohistochemistry (IHC). The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in ULMS.

Result

In total, 55 DEGs were recognized via GO analysis, and KEGG analyses revealed that the DEGs were enriched in nuclear division, and cell cycle. The recognized DEGs were primarily implicated in non-small cell lung carcinoma and breast carcinoma. Gene sets related to the cell cycle and DNA replication were activated in ULMS. DPP6 and MFAP5 were distinguished as diagnostic biomarkers of ULMS (AUC = 0.957, AUC = 0.899, respectively), and they were verified in the GSE9511 and GSE68295 datasets (AUC = 0.983, AUC = 0.942, respectively). The low expression of DPP6 and MFAP5 were associated with ULMS. In addition, the analysis of the immune microenvironment indicated that resting mast cells were positively correlated with DPP6 and MFAP5 expression and that eosinophils and M0 macrophages were negatively correlated with DPP6 expression (P<0.05).

Conclusion

These findings indicated that DPP6 and MFAP5 are diagnostic biomarkers of ULMS, thereby offering a novel perspective for future studies on the occurrence, function and molecular mechanisms of ULMS.

m6A-Related lncRNA Signature Is Involved in Immunosuppression and Predicts the Patient Prognosis of the Age-Associated Ovarian Cancer

Background

Epithelial ovarian cancers are age-associated diseases, usually diagnosed at an advanced stage. lncRNA has been discovered to interplay with N6-methyladenosine (m6A), working in tandem to promote cancer progression and worsening patient outcomes. This study is aimed at investigating the roles and mechanism of m6A-related lncRNA signature on ovarian cancers.

Methods

We retrieved TCGA and CGGA sequencing data to identify m6A-related lncRNA signature and constructed an m6A score (MS) using the LASSO algorithm. A clinical nomogram was then established to predict the overall survival of patients. Subsequently, GSEA analyses were conducted to obtain pathways involved. Expression of HLA genes, 28 tumor-infiltrating lymphocyte infiltration, and anticancer cycle were analyzed the immunological differences between high-MS and low-MS groups. Finally, immune checkpoint gene expressions and IC₅₀ of chemotherapeutic drugs were calculated, and CMap was run to identify the potential compounds and their corresponding mechanisms.

Results

We identified 16 m6A-related lncRNAs and constructed an MS model. The high-MS group showed a poor prognosis. A clinical nomogram consists of MS, and age was constructed and predicted the 1-, 3-, and 5-year survival with high accuracy. GSEA analyses presented downregulated antigen processing and presentation pathways. Immunocyte infiltrating analyses demonstrated that high-MS was associated with high infiltration of Treg cells, macrophages, and low Th1/Th2 rate. Also, high expression of immune checkpoint genes NRP1, TNFSF9, and VSIR was observed in the high-MS group. Finally, the high-MS group also predicted low IC₅₀ of vinorelbine and vorinostat.

Conclusion

This study constructed a robust prediction model for prognostic management and revealed the cross-talk between m6A and immunosuppression. Besides, the m6A lncRNA signature can predict the chemotherapeutic drug response. These will shed light on the development of novel therapeutic strategies and render survival benefits for ovarian patients.

Integrated Bioinformatics Analysis Identifies Robust Biomarkers and Its Correlation With Immune Microenvironment in Nonalcoholic Fatty Liver Disease

Objective: Nonalcoholic fatty liver disease (NAFLD) is a serious threat to human health worldwide. In this study, the aim is to analyze diagnosis biomarkers in NAFLD and its relationship with the immune microenvironment based on bioinformatics analysis.

Methods: We downloaded microarray datasets (GSE48452 and GSE63067) from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The hub genes were screened by a series of machine learning analyses, such as support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and weighted gene co-expression network analysis (WGCNA). It is worth mentioning that we used the gene enrichment analysis to explore the driver pathways of NAFLD occurrence. Subsequently, the aforementioned genes were validated by external datasets (GSE66676). Moreover, the CIBERSORT algorithm was used to estimate the proportion of different types of immune cells. Finally, the Spearman analysis was used to verify the relationship between hub genes and immune cells.

Results: Hub genes (CAMK1D, CENPV, and TRHDE) were identified. In addition, we found that the pathogenesis of NAFLD is mainly related to nutrient metabolism and the immune system. In correlation analysis, CENPV expression had a strong negative correlation with resting memory CD4 T cells, and TRHDE expression had a strong positive correlation with naive B cells.

Conclusion: CAMK1D, CENPV, and TRHDE play regulatory roles in NAFLD. In particular, CENPV and TRHDE may regulate the immune microenvironment by mediating resting memory CD4 T cells and naive B cells, respectively, and thus influence disease progression.

Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer

Cancer-associated fibroblasts (CAFs) are a major contributor to tumor stromal crosstalk in the tumor microenvironment (TME) and boost tumor progression by promoting angiogenesis and lymphangiogenesis. This study aimed to identify prognostic genes associated with CAFs that lead to high morbidity and mortality in ovarian cancer (OC) patients. We performed bioinformatics analysis in 16 multicenter studies (2,742 patients) and identified CAF-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify COL16A1, COL5A2, GREM1, LUM, SRPX, and TIMP3 and construct a prognostic signature. Subsequently, a series of bioinformatics algorithms indicated risk stratification based on the above signature, suggesting that high-risk patients have a worse prognosis, weaker immune response, and lower tumor mutational burden (TMB) status but may be more sensitive to routine chemotherapeutic agents. Finally, we characterized prognostic markers using cell lines, immunohistochemistry, and single-cell sequencing. In conclusion, these results suggest that the CAF-related signature may be a novel pretreatment guide for anti-CAFs, and prognostic markers in CAFs may be potential therapeutic targets to inhibit OC progression.

Comprehensive Analysis of LINC01615 in Head and Neck Squamous Cell Carcinoma: A Hub Biomarker Identified by Machine Learning and Experimental Validation

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers, but in clinical practice, the lack of precise biomarkers often results in an advanced diagnosis. Hence, it is crucial to explore novel biomarkers to improve the clinical outcome of HNSCC patients.

Methods

We downloaded RNA-seq data consisting of 502 HNSCC tissues and 44 normal tissues from the TCGA database, and lncRNA genomic sequence information was downloaded from the GENECODE database for annotating lncRNA expression profiles. We used Cox regression analysis to screen prognostic lncRNAs, the threshold as HR >1 and p value <0.05. Subsequently, three survival outcomes (overall survival, progress-free interval, and disease-specific survival)-related lncRNAs overlapped to get the common lncRNAs. The hub biomarker was identified using LASSO and random forest models. Subsequently, we used a variety of statistical methods to validate the prognostic ability of the hub marker. In addition, Spearman correlation analysis between the hub marker expression and genomic heterogeneity was conducted, such as instability (MSI), homologous recombination deficiency (HRD), and tumor mutational burden (TMB). Finally, we used enrichment analysis, ssGSEA, and ESTIMATE algorithms to explore the changes in the underlying immune-related pathway and function. Finally, the MTT assay and transwell assay were performed to determine the effect of LINC01615 silencing on tumor cell proliferation, invasion, and migration.

Results

Cox regression analysis revealed 133 lncRNAs with multiple prognostic significance. The machine learning algorithm screened out the hub lncRNA with the highest importance in the RF model: LINC01615. Clinical correlation analysis revealed that the LINC01615 increased with increasing the T stage, N stage, pathology grade, and clinical stage. LINC01615 could be used as a predictor of HNSCC prognosis validating by a variety of statistical methods. Subsequently, when clinical indicators were combined with the LINC01615 expression, the visualization model (nomogram) was more applicable to clinical practice. Finally, immune algorithms indicated that LINC01615 may be involved in the regulation of lymphocyte recruitment and immunological infiltration in HNSCC, and the LINC01615 expression represented genomic heterogeneity in pan-cancer. Functionally, silencing of LINC01615 suppresses cell proliferation, invasion, and migration in HEP-2 and TU212 cells.

Conclusion

LINC01615 may play an important role in the prostromal cell enrichment and immunosuppressive state and serve as a prognostic biomarker in HNSCC.

Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling

Background: Multiple myeloma (MM) is characterized by abnormal proliferation of bone marrow clonal plasma cells. Tumor immunotherapy, a new therapy that has emerged in recent years, offers hope to patients, and studying the expression characteristics of immune-related genes (IRGs) based on whole bone marrow gene expression profiling (GEP) in MM patients can help guide personalized immunotherapy.

Methods: In this study, we explored the potential prognostic value of IRGs in MM by combining GEP and clinical data from the GEO database. We identified hub IRGs and transcription factors (TFs) associated with disease progression by Weighted Gene Co-expression Network Analysis (WGCNA), and modeled immune-related prognostic signature by univariate and multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, the prognostic ability of signature was verified by multiple statistical methods. Moreover, ssGSEA and GSEA algorithm reveled different immunological characteristics and biological function variation in different risk groups. We mapped the hub IRGs by protein-protein interaction network (PPI) and extracted the top 10 ranked genes. Finally, we conducted vitro assays on two alternative IRGs.

Results: Our study identified a total of 14 TFs and 88 IRGs associated with International Staging System (ISS). Ten IRGs were identified by Cox -LASSO regression analysis, and used to develop optimal prognostic signature for overall survival (OS) in MM patients. The 10-IRGs were BDNF, CETP, CD70, LMBR, LTBP1, NENF, NR1D1, NR1H2, PTK2B and SEMA4. In different groups, risk signatures showed excellent survival prediction ability, and MM patients also could be stratified at survival risk. In addition, IRF7 and SHC1 were hub IRGs in PPI network, and the vitro assays proved that they could promote tumor progression. Notably, ssGSEA and GSEA results confirmed that different risk groups could accurately indicate the status of tumor microenvironment (TME) and activation of biological pathways.

Conclusion: Our study suggested that immune-related signature could be used as prognostic markers in MM patients.

Integrated Bioinformatics Algorithms and Experimental Validation to Explore Robust Biomarkers and Landscape of Immune Cell Infiltration in Dilated Cardiomyopathy

Background

The etiology of dilated cardiomyopathy (DCM) is unclear. Bioinformatics algorithms may help to explore the underlying mechanisms. Therefore, we aimed to screen diagnostic biomarkers and identify the landscape of immune infiltration in DCM.

Methods

First, the CIBERSORT algorithm was used to excavate the proportion of immune-infiltration cells in DCM and normal myocardial tissues. Meanwhile, the Pearson analysis and principal component analysis (PCA) were used to identify immune heterogeneity in different tissues. The Wilcoxon test, LASSO regression, and machine learning method were conducted to identify the hub immune cells. In addition, the differentially expressed genes (DEGs) were screened by the limma package, and DEGs were analyzed for functional enrichment. In the protein–protein interaction (PPI) network, multiple algorithms were used to calculate the score of each DEG for screening the hub genes. Subsequently, external datasets were used to further validate the expression of hub genes, and the receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficacy. Finally, we examined the expression of hub biomarkers in animal models.

Results

A total of 108 DEGs were screened, and these genes may be related to biological processes such as cytolysis, positive regulation of cytokine secretion, etc. Two types of hub immune cells [activated natural killer (NK) cells and eosinophils] and four hub genes (ASPN, CD163, IL10, and LUM) were identified in DCM myocardial tissues. CD163 was verified to have the capability to diagnose DCM with the most excellent specificity and sensitivity. It is worth mentioning that the combined CD163 and eosinophils may have better diagnostic efficacy. Moreover, the correlation analysis showed CD163 was negatively correlated with activated NK cells. Finally, the results of the mice model also indicated that CD163 might be involved in the occurrence of DCM.

Conclusion

ASPN, CD163, IL10, and LUM may have a potential predictive ability for DCM, and especially CD163 showed the most robust efficacy. Furthermore, activated NK cells and eosinophils may relate to the occurrence of DCM.

Primary leiomyosarcoma of the adrenal; a case report

Introduction and importance

Primary adrenal leiomyosarcoma (PAL) is an extremely rare neoplasm that usually arises from the smooth muscle cells of the adrenal or adjacent vascular structures. The tumor is asymptomatic until it grows up and develops a mass effect in the retroperitoneal region. Although there are about 50 reported valid cases, surgical intervention is mandatory in the majority of patients.

Case presentation

Herein, we report the case of a 32-year-old healthy woman with a chief complaint of vague abdominal pain. After initial clinical and radiological examinations, we found a large retroperitoneal mass located around the right adrenal gland. Due to the patient's pain, a laparotomy was performed, and a large mass was resected with free margins. Immunohistochemical examination was positive for vimentin, smooth muscle actin (SMA), and desmin. Therefore, the diagnosis of PAL was confirmed.

Conclusion

Although PAL is an uncommon malignancy, its diagnostic and therapeutic approaches are almost straightforward. A computed tomography scan can show the characteristics of the tumor and direct the management. Surgical resection is the mainstay of treatment, and the effects of adjuvant therapies have not been apparent yet.

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A 32-year-old woman presented to us complaining of vague abdominal pain without any specific signs and symptoms.

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After imaging studies, a large retroperitoneal mass was found around the right adrenal gland.

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The patient was operated with a midline laparotomy and the mass was resected with free margins.

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Immunohistochemical examination was positive for pathological markers of leiomyosarcoma.

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The surgery was performed successfully, and the patient was discharged in a good condition.

Prognostic value of immune-related lncRNA pairs in patients with bladder cancer

BACKGROUND

The characteristics of immune-related long non-coding ribonucleic acids (ir-lncRNAs), regardless of their specific levels, have important implications for the prognosis of patients with bladder cancer.

METHODS

Based on The Cancer Genome Atlas database, original transcript data were analyzed. The ir-lncRNAs were obtained using a coexpression method, and their differentially expressed pairs (DE-ir-lncRNAs) were identified by univariate analysis. The lncRNA pairs were verified using a Lasso regression test. Thereafter, receiver operating characteristic curves were generated, and an optimal risk model was established. The clinical value of the model was verified through the analysis of patient survival rates, clinicopathological characteristics, presence of tumor-infiltrating immune cells, and chemotherapy efficacy evaluation.

RESULTS

In total, 49 pairs of DE-ir-lncRNAs were identified, of which 21 were included in the Cox regression model. A risk regression model was established on the premise of not involving the specific expression value of the transcripts.

CONCLUSIONS

The method and model used in this study have important clinical predictive value for bladder cancer and other malignant tumors.