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Uterine corpus endometrial carcinoma (UCEC), a prevalent kind of cancerous tumor in female reproductive system that has a dismal prognosis in women worldwide. Given the very limited studies of cuproptosis-related lncRNAs (CRLs) in UCEC. Our purpose was to construct a prognostic profile based on CRLs and explore its assess prognostic value in UCEC victims and its correlation with the immunological microenvironment. METHODS: 554 UCEC tumor samples and 23 normal samples' RNA-seq statistics and clinical details were compiled from data in the TCGA database. CRLs were obtained using Pearson correlation analysis. Using LASSO Cox regression, multivariate Cox regression, and univariate Cox regression analysis, six CRLs are confirmed to develop a risk prediction model at last.We identified two main molecular subtypes and observed that multilayer CRLs modifications were related to patient clinicopathological features, prognosis, and tumor microenvironment (TME) cell infiltration characteristics, and then we verified the prognostic hallmark of UCEC and examined its immunological landscape.Finally, using qRT-PCR, model hub genes' expression patterns were confirmed. RESULTS: A unique CRL signature was established by the combination of six differently expressed CRLs that were highly linked with the prognosis of UCEC patients. According to their CRLs signatures, the patients were divided into two groups: the low-risk and the high-risk groups. Compared to individuals at high risk, patients at low risk had higher survival rates (p < 0.001). Additionally, Cox regression reveals that the profiles of lncRNAs linked to cuproptosis may independently predict prognosis in UCEC patients. The 1-, 3-, and 5-year risks' respective receiver operating characteristics (ROC) exhibited AUC values of 0.778, 0.810, and 0.854. Likewise, the signature could predict survival in different groups based on factors like stage, age, and grade, among others. Further investigation revealed differences between the different risk score groups in terms of drug sensitivity,immune cell infiltration,tumor mutation burden (TMB) score and microsatellite instability (MSI) score. Compared to the group of high risk, the low-risk group had greater rates of TMB and MSI. Results from qRT-PCR revealed that in UCEC vs normal tissues, AC026202.2, NRAV, AC079466.2, and AC090617.5 were upregulated,while LINC01545 and AL450384.1 were downregulated. CONCLUSIONS: Our research clarified the relationship between CRLs signature and the immunological profile and prognosis of UCEC.This signature will establish the framework for future investigations into the endometrial cancer CRLs mechanism as well as the exploitation of new diagnostic tools and new therapeutic.
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Background: Ovarian serous cystadenocarcinoma (OSC) is the most prevalent histological subtype of ovarian cancer (OV) and presents a serious threat to women's health. Anoikis is an essential component of metastasis, and tumor cells can get beyond it to become viable. The impact of anoikis on OSC, however, has only been the topic of a few studies. Methods: The mRNA sequencing and clinical information of OSC came from The Cancer Genome Atlas Target Genotype-Tissue Expression (TCGA TARGET GTEx) dataset. Anoikis-related genes (ARGs) were collected by Harmonizome and GeneCards websites. Centered on these ARGs, we used unsupervised consensus clustering to explore potential tumor typing and filtered hub ARGs to create a model of predictive signature for OSC patients. Furthermore, we presented clinical specialists with a novel nomogram based on ARGs, revealing the underlying clinical relevance of this signature. Finally, we explored the immune microenvironment among various risk groups. Results: We identified 24 ARGs associated with the prognosis of OSC and classified OSC patients into three subtypes, and the subtype with the best prognosis was more enriched in immune-related pathways. Seven ARGs (ARHGEF7, NOTCH4, CASP2, SKP2, PAK4, LCK, CCDC80) were chosen to establish a risk model and a nomogram that can provide practical clinical decision support. Risk scores were found to be an independent and significant prognostic factor in OSC patients. The CIBERSORTx result revealed an inflammatory microenvironment is different for risk groups, and the proportion of immune infiltrates of Macrophages M1 is negatively correlated with risk score (rs = -0.21, P < 0.05). Ultimately, quantitative reverse transcription polymerase chain reaction (RT-PCR) was utilized to validate the expression of the seven pivotal ARGs. Conclusion: In this study, based on seven ARGs, a risk model and nomogram established can be used for risk stratification and prediction of survival outcomes in patients with OSC, providing a reliable reference for individualized therapy of OSC patients.
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Background: Endometriosis, a common gynecological condition, can cause symptoms such as dysmenorrhea, infertility, and abnormal bleeding, which can negatively affect a woman's quality of life. In the current study, the pathophysiological mechanisms of endometriosis are unknown, but this study suggests that endometriosis is associated with dysregulation of the autoimmune system. This study identify hub genes involved in the prevalence, identification and diagnostic value of endometriosis and autoimmune diseases, and explore the central genes and immune infiltrates, the diagnosis of endometriosis provides a new sight of thinking about diagnosis and treatment. Methods and Results: The relevant datasets for endometriosis GSE141549, GSE7305 and autoimmune disease-related genes (AIDGs) were downloaded from online database. Using the "limma" package and WGCNA to screen out the autoimmune disease related genes and endometriosis related genes, the autoimmune disease gene-related differential genes (AID-DEGs) progressive GO, KEGG enrichment analysis, and then using the protein interaction network and Cytoscape software to select hub genes (CXCL12, PECAM1, NGF, CTGF, WNT5A), using the "pROC" package to analyze the hub genes for the diagnostic value of endometriosis. The difference in the importance of hub genes for the diagnosis of endometriosis was analyzed by machine learning random forest, and the combined diagnostic value of hub genes was analyzed by using the Support Vector Machine (SVM) algorithm. The eutopic (EU) and ectopic endometrium (EC) immune microenvironment of endometriosis was evaluated using CIBERSORT, the correlation of hub genes to the immune microenvironment was analyzed. Conclusion: The hub genes associated with AIDGs are differentially expressed in EC and EU of endometriosis and possess important value for the diagnosis of endometriosis. The hub genes have a very important impact on the immune microenvironment of endometriosis, which is important for exploring the connection between endometriosis and autoimmune diseases and provides a new insight for the subsequent study of immunotherapy and diagnosis of endometriosis.
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Background and Objectives: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are malignant disorders with adverse prognoses for advanced patients. Anoikis, which is involved in tumor metastasis, facilitates the survival and separation of tumor cells from their initial site. Unfortunately, it is rarely studied, and in the literature, studies have only addressed the prognosis character of anoikis for patients with CESC. Materials and Methods: We utilized anoikis-related genes (ANRGs) to construct a prognostic signature in CESC patients that were selected from the Genecards and Harmonizome portals. Furthermore, we revealed the underlying clinical value of this signature for clinical maneuvers by providing clinical specialists with an innovative nomogram on the basis of ANRGs. Finally, we investigated the immune microenvironment and drug sensitivity in different risk groups. Results: We screened six genes from fifty-eight anoikis-related differentially expressed genes in the TCGA-CESC cohort, and we constructed a prognostic signature. Then, we built a nomogram combined with CESC clinicopathological traits and risk scores, which demonstrated that this model may improve the prognosis of CESC patients in clinical therapy. Next, the prognostic risk scores were confirmed to be an independent prognostic indicator. Additionally, we programmed a series of analyses, which included immune infiltration analysis, therapy-related analysis, and GSVA enrichment analysis, to identify the functions and mechanisms of the prognostic models during the progression of cancer in CESC patients. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the six ANRGs. Conclusions: The present discovery verified that the predictive 6-anoikis-related gene (6-ANRG) signature and nomogram serve as imperative factors that might notably impact a CESC patient's prognosis, and they may be able to provide new clinical evidence to assume the role of underlying biological biomarkers and thus become indispensable indicators for prospective diagnoses and advancing therapy.
