Dataset Information

Integrating iron metabolism-related gene signature to evaluate prognosis and immune infiltration in nasopharyngeal carcinoma.

ABSTRACT:

Background

Dysregulation of iron metabolism has been shown to have significant implications for cancer development. We aimed to investigate the prognostic and immunological significance of iron metabolism-related genes (IMRGs) in nasopharyngeal carcinoma (NPC).

Methods

Multiple Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets were analyzed to identify key IMRGs associated with prognosis. Additionally, the immunological significance of IMRGs was explored.

Results

A novel risk model was established using the LASSO regression algorithm, incorporating three genes (TFRC, SLC39A14, and ATP6V0D1).This model categorized patients into low and high-risk groups, and Kaplan-Meier analysis revealed significantly shorter progression-free survival for the high-risk group (P < 0.0001). The prognostic model's accuracy was additionally confirmed by employing time-dependent Receiver Operating Characteristic (ROC) curves and conducting Decision Curve Analysis (DCA). High-risk patients were found to correlate with advanced clinical stages, specific tumor microenvironment subtypes, and distinct morphologies. ESTIMATE analysis demonstrated a significant inverse relationship between increased immune, stromal, and ESTIMATE scores and lowered risk score. Immune analysis indicated a negative correlation between high-risk score and the abundance of most tumor-infiltrating immune cells, including dendritic cells, CD8⁺ T cells, CD4⁺ T cells, and B cells. This correlation extended to immune checkpoint genes such as PDCD1, CTLA4, TIGIT, LAG3, and BTLA. The protein expression patterns of selected genes in clinical NPC samples were validated through immunohistochemistry.

Conclusion

This study presents a prognostic model utilizing IMRGs in NPC, which could assist in assessing patient prognosis and provide insights into new therapeutic targets for NPC.

SUBMITTER: Su J

PROVIDER: S-EPMC11009181 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Publications

Integrating iron metabolism-related gene signature to evaluate prognosis and immune infiltration in nasopharyngeal carcinoma.

Su Jiaming J Zhong Guanlin G Qin Weiling W Zhou Lu L Ye Jiemei J Ye Yinxing Y Chen Chang C Liang Pan P Zhao Weilin W Xiao Xue X Wen Wensheng W Luo Wenqi W Zhou Xiaoying X Zhang Zhe Z Cai Yonglin Y Li Cheng C

Discover oncology 20240411 1

<h4>Background</h4>Dysregulation of iron metabolism has been shown to have significant implications for cancer development. We aimed to investigate the prognostic and immunological significance of iron metabolism-related genes (IMRGs) in nasopharyngeal carcinoma (NPC).<h4>Methods</h4>Multiple Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets were analyzed to identify key IMRGs associated with prognosis. Additionally, the immunological significance of IMRGs was explored.<h ...[more]

PMID: 38602575

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Project description:BackgroundPrevious studies have confirmed the phenomenon of anoikis resistance in nasopharyngeal carcinoma (NPC). Nevertheless, the prognostic significance of anoikis-related genes (ARGs) in NPC remains incompletely understood. This study aimed to create a predictive risk score using an ARGs signature for NPC patients and to investigate how this score relates to clinicopathologic features and immune infiltration in the tumor microenvironment.MethodsBy using data from the Gene Expression Omnibus (GEO) database, we employed machine learning methods to discover prognostic ARGs and create a risk score. Key gene expression levels were validated through real-time PCR and immunohistochemical staining.ResultsThree differentially expressed ARGs (CDC25C, E2F1 and RBL2) with prognostic value were identified by the intersection of multiple machine learning algorithms. A risk score based on t 3-ARG feature was developed to stratify NPC patients into two distinct risk groups using the optimal model, Random Survival Forest. NPC patients with high-risk scores experienced notably shorter progression-free survival in comparison to those with low-risk scores. Multivariate Cox regression analysis indicated that the risk score served as an independent prognostic factor. The time-dependent ROC and decision curve analyses demonstrated the risk model's strong predictive accuracy and clinical utility. The low-risk score group exhibited features indicative of early clinical stage, immune activation, high immune checkpoint gene's expression, and low Epstein-Barr virus gene's expression. Functional analysis revealed enrichment of immune-related pathways in the low-risk group. Patients with high-risk scores were discovered to be unlikely to benefit from immune checkpoint inhibitor treatment. Moreover, the expression of RBL2, E2F1, and CDC25C were significantly correlated with the expression of caspase family genes. Finally, the lower mRNA and protein expression of RBL2 were validated in NPC cell lines and tissues.ConclusionsOur ARGs-based signature model shows promising results in predicting the prognosis of NPC patients and might be associated with immune cell infiltration.

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Project description:Background: With a high incidence and dismal survival rate, hepatocellular carcinoma (HCC) tops the list of the world's most frequent malignant tumors. Immunotherapy is a new approach to cancer treatment, and its effect on prolonging overall survival (OS) varies from patient to patient. For a more effective prognosis and treatment of HCC, we are committed to identifying immune infiltration-related long non-coding RNAs (IIRLs) with prognostic value in hepatocellular carcinoma. Methods: In our study, we calculated immune scores of 369 hepatocellular carcinoma samples from the Cancer Genome Atlas (TCGA) database by using an estimation algorithm, and obtained long non-coding RNAs (lncRNAs) associated with immune infiltration by using Weighted Gene Co-expression Network analysis (WGCNA). For training cohort, univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis were used to determine prognostic IIRLs, we established a prognostic IIRLs signature. By testing cohort and entire cohort, we confirmed that the signature is practical. The prognosis of people with different clinicopathological stages and risk scores were predicted by the nomogram we constructed. In addition, Immune cell infiltration analysis and prediction of therapeutic drugs were performed. Results: 93 IIRLs were obtained by WGCNA. Furthermore, the prognostic value of these IIRLs were evaluated by using univariate Cox, Lasso and multivariate Cox analysis. Four IIRLs were used to create a signature with a prognosis. Time-related receiver operating characteristic (ROC) curve revealed that this model had an acceptable prognostic value for HCC patients. By using univariate and multivariate Cox regression analysis, this risk score has been shown to be an independent prognostic factor for HCC. The nomogram we made showed good predictions. Except for that, the treatment with immune checkpoint inhibitors (ICI) was likely to be more effective for low-risk patients. Conclusion: Based on four IIRLs, a prognostic signature was created in this research showed good accuracy in predicting OS. This study also provided valuable references for Immunotherapy of hepatocellular carcinoma.

Dataset Information

Integrating iron metabolism-related gene signature to evaluate prognosis and immune infiltration in nasopharyngeal carcinoma.

Background

Methods

Results

Conclusion

Publications

Integrating iron metabolism-related gene signature to evaluate prognosis and immune infiltration in nasopharyngeal carcinoma.

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