Identification and validation of a novel necroptosis-related prognostic signature in cervical squamous cell carcinoma and endocervical adenocarcinoma

Cuproptosis-Associated lncRNA Gene Signature Establishes New Prognostic Profile and Predicts Immunotherapy Response in Endometrial Carcinoma

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.

Identification and validation of a novel anoikis-related signature for predicting prognosis and immune landscape in ovarian serous cystadenocarcinoma

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.

Identification of a Necroptosis-Related Prognostic Signature and Associated Regulatory Axis in Lung Adenocarcinoma

Background

Lung cancer is considered to be the second most aggressive and rapidly fatal cancer after breast cancer. Necroptosis, a novel discovered pattern of cell death, is mediated by Receptor-interacting serine/threonine-protein kinase 1 (RIPK1), Receptor-interacting serine/threonine-protein kinase 3 (RIPK3), and Mixed Lineage Kinase Domain Like Pseudokinase (MLKL).

Methods

For the purpose of developing a prognostic model, Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted. Using Pearson's correlation analysis, we evaluated the correlation between necroptosis-related markers and tumor immune infiltration. A bioinformatics analysis was conducted to construct a necroptosis-related regulatory axis.

Results

There was a downregulation of most of necroptosis-related genes in lung adenocarcinoma (LUAD) versus lung tissues but an increase in PGAM5, HMGB1, TRAF2, EZH2 levels. We also summarized the Single Nucleotide Variant (SNV) and copy number variation (CNV) of necroptosis-related genes in LUAD. Consensus clustering identified two clusters in LUAD with distinct immune cell infiltration and ESTIMATEScore. Genes related to necroptosis were associated with necroptosis, Tumor necrosis factor (TNF) signaling pathway, and apoptosis according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Four prognostic genes (ALDH2, HMGB1, NDRG2, TLR2) were combined to develop a prognostic gene signature for LUAD patients, which was highly accurate in predicting prognosis. Univariate and multivariate analysis identified HMGB1, pT stage, and pN stage as independent factors impacting on LUAD patients' prognosis. A significant correlation was found between the level of TLR2 and NDRG2 and clinical stage, immunity infiltration, and drug resistance. Additionally, the progression of LUAD might be regulated by lncRNA C5orf64/miR-582-5p/NDRG2/TLR2.

Conclusion

The current bioinformatics analysis identified a necroptosis-related prognostic signature for LUAD and their relation to immunity infiltration. This result requires further investigation.

Identification and Validation of the Anoikis-Related Gene Signature as a Novel Prognostic Model for Cervical Squamous Cell Carcinoma, Endocervical Adenocarcinoma, and Revelation Immune Infiltration

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.