A Novel Ferroptosis-Related lncRNAs Signature Predicts Clinical Prognosis and Is Associated With Immune Landscape in Pancreatic Cancer

Ping, Haiqin; Jia, Xingqing; Ke, Hengning

doi:10.3389/fgene.2022.786689

Cited by 18 publications

(14 citation statements)

References 33 publications

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“…Among the seven lncRNAs used to construct the signature, AC092171.5 has previously been reported as an immune-and m6A-related lncRNA in pancreatic cancer and is significantly associated with patient prognosis (Wei et al, 2019;Cao et al, 2022). CASC8 has been suggested as a marker to predict the prognosis of PAAD and as a potential target for treatment (Wang et al, 2020;Ping et al, 2022), which is consistent with our findings. AC090114.2 was reported to be a pyroptosis-related lncRNA and was associated with PAAD prognosis and tumor immune microenvironment Frontiers in Genetics frontiersin.org (Zhao et al, 2022).…”

Section: Discussionsupporting

confidence: 90%

Construction of a cancer-associated fibroblasts-related long non-coding RNA signature to predict prognosis and immune landscape in pancreatic adenocarcinoma

Zhao

et al. 2022

Front. Genet.

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Background: Cancer-associated fibroblasts (CAFs) are an essential cell population in the pancreatic cancer tumor microenvironment and are extensively involved in drug resistance and immune evasion mechanisms. Long non-coding RNAs (lncRNAs) are involved in pancreatic cancer evolution and regulate the biological behavior mediated by CAFs. However, there is a lack of understanding of the prognostic signatures of CAFs-associated lncRNAs in pancreatic cancer patients.Methods: Transcriptomic and clinical data for pancreatic adenocarcinoma (PAAD) and the corresponding mutation data were obtained from The Cancer Genome Atlas database. lncRNAs associated with CAFs were obtained using co-expression analysis. lncRNAs were screened by Cox regression analysis using least absolute shrinkage and selection operator (LASSO) algorithm for constructing predictive signature. According to the prognostic model, PAAD patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis was used for survival validation of the model in the training and validation groups. Clinicopathological parameter correlation analysis, univariate and multivariate Cox regression, time-dependent receiver operating characteristic (ROC) curves, and nomogram were performed to evaluate the model. The gene set variation analysis (GSVA) and gene ontology (GO) analyses were used to explore differences in the biological behavior of the risk groups. Furthermore, single-sample gene set enrichment analysis (ssGSEA), tumor mutation burden (TMB), ESTIMATE algorithm, and a series of immune correlation analyses were performed to investigate the relationship between predictive signature and the tumor immune microenvironment and screen for potential responders to immune checkpoint inhibitors. Finally, drug sensitivity analyses were used to explore potentially effective drugs in high- and low-risk groups.Results: The signature was constructed with seven CAFs-related lncRNAs (AP005233.2, AC090114.2, DCST1-AS1, AC092171.5, AC002401.4, AC025048.4, and CASC8) that independently predicted the prognosis of PAAD patients. Additionally, the high-risk group of the model had higher TMB levels than the low-risk group. Immune correlation analysis showed that most immune cells, including CD8+ T cells, were negatively correlated with the model risk scores. ssGSEA and ESTIMATE analyses further indicated that the low-risk group had a higher status of immune cell infiltration. Meanwhile, the mRNA of most immune checkpoint genes, including PD1 and CTLA4, were highly expressed in the low-risk group, suggesting that this population may be “hot immune tumors” and have a higher sensitivity to immune checkpoint inhibitors (ICIs). Finally, the predicted half-maximal inhibitory concentrations of some chemical and targeted drugs differ between high- and low-risk groups, providing a basis for treatment selection.Conclusion: Our findings provide promising insights into lncRNAs associated with CAFs in PAAD and provide a personalized tool for predicting patient prognosis and immune microenvironmental landscape.

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Section: Discussionsupporting

confidence: 90%

Construction of a cancer-associated fibroblasts-related long non-coding RNA signature to predict prognosis and immune landscape in pancreatic adenocarcinoma

Zhao

et al. 2022

Front. Genet.

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“…In addition, LINC01281 is considered a protective factor for laryngeal cancer [ 37 ]. Ping et al identified a ferroptosis-related lncRNA prognostic signature that included PAN3-AS1 and found it associated with a favorable prognosis in cancer patients [ 38 ]. HEIH is highly expressed in several tumor types; can promote tumor cell proliferation, migration, invasion, and drug resistance; and is related to a poor prognosis [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Identification and Validation of a Novel Necroptosis-Related Long Noncoding RNA Prognostic Signature for Lung Adenocarcinoma

Zeng

Liu

et al. 2022

BioMed Research International

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Background. Several cancers, including lung adenocarcinoma (LUAD), are caused by genes related to necroptosis. However, it is still unknown how necroptosis-related long noncoding RNAs (lncRNAs) may be involved in LUAD. In order to predict the prognosis of LUAD patients and personalize immunotherapy, this study set out to construct a necroptosis-related lncRNA prognostic signature (NLPS). Methods. The Cancer Genome Atlas (TCGA) database was used to download the LUAD transcriptome data and the associated clinical data. lncRNAs associated with necroptosis were screened using coexpression analysis. An NLPS was built using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The Gene Expression Omnibus (GEO) database’s GSE30219 was used to validate the NLPS. The prognostic value of the risk score was assessed using Kaplan-Meier survival, receiver operating characteristic (ROC) Cox regression, multivariate Cox regression, and nomogram analyses. Then, we looked into the differences between the low- and high-risk groups in the tumor immune microenvironment, immunotherapy response, and half-maximal inhibitory concentration (IC50). Results. The 14 lncRNAs in a novel NLPS were created. With further validation in the GSE30219 dataset, the risk score according to the NLPS was an independent prognostic indicator for LUAD patients. Patients with better overall survival (OS) in the low-risk group, who were characterized by increased immune cell infiltration, tumor mutational burden (TMB), and immunophenoscore (IPS), may have hot tumors and higher immunotherapy response rates. In addition, the risk score was also closely linked to sensitivity to various anticancer medications. Conclusions. We constructed a novel NLPS that could predict OS and sensitivity to immunotherapy in LUAD patients.

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“…Since rst named by Dixon in 2012, ferroptosis has been demonstrated in the regulation of metabolism and redox biology, affecting the pathogenesis and treatment of cancers. The prognostic signatures based on ferroptosis related lncRNAs have been investigated to play essential roles in predicting survival and outcomes of patients with various cancers, including hepatocellular carcinoma (19), renal cancer (11), pancreatic cancer (20), oral squamous cell carcinoma ( 21) and so on. Cuproptosis a novel form of copper-induced cell death has been reported to be regulated by 16 mRNAs as described above.…”

Section: Discussionmentioning

confidence: 99%

Construction of a Cuproptosis-Related Signature for Predicting Prognosis and Immune Response in Clear Cell Renal Cell Carcinoma

Cui

Zhang²,

Shen

et al. 2022

Preprint

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Background Cuproptosis is a new form of programmed cell death which induced by copper. The signature of cuproptosis related lncRNAs and mRNAs (CRLM) has not been reported before in clear cell renal cell carcinoma. Methods The RNA-seq, clinical data, and single nucleotide variants (SNV) data of clear cell renal cell carcinoma (ccRCC) were obtained from the Cancer Genome Atlas (TCGA). Cuproptosis related long non-coding RNAs (lncRNAs) were identified via Pearson’s test. Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were used to further screen prognosis related lncRNAs and mRNAs for signature construction. Cases were then randomly divided into training and testing set groups at ratio 1:1 to validate the cuproptosis signature. Kaplan–Meier survival analysis, receiver operating characteristic (ROC) curve analysis, nomogram, principal components analysis (PCA), gene set enrichment analyses (GSEA), mutation landscape, and therapies response were subsequently conducted with R or perl algorithms. A Japanese ccRCC cohort (n = 98) was used to validate this signature. Results A total of 245 lncRNAs were identified as cuproptosis related. Three of them (AL731577.2, LINC00460, AL133215.2) and cuproptosis related mRNA DBT were finally incorporated into the signature construction. High-risk group patients suffered from lower survival rate. The area under curve (AUC) of the novel signature for 1-, 3-, 5-year survival rates was 0.731, 0.718, 0.745, respectively. The concordance index (C-index) was 0.692. The Japanese cohort showed a good consistence with the results derived from the TCGA dataset. Tumor mutation burden (TMB) was also found to be a risking factor for ccRCC patients. Immune related Gene ontology terms were significantly enriched in high-risk patients. Besides, low-risk patients may be more sensitive to immune check inhibitors (ICIs), Sunitinib, and Pazopanib. High-risk patients may be more sensitive to Sorafenib. Conclusion The cuproptosis related signature is a promising and potential prognostic tool in predicting the survival of patients with ccRCC. It could contribute to precise and individualized ccRCC treatment.

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A Novel Ferroptosis-Related lncRNAs Signature Predicts Clinical Prognosis and Is Associated With Immune Landscape in Pancreatic Cancer

Cited by 18 publications

References 33 publications

Construction of a cancer-associated fibroblasts-related long non-coding RNA signature to predict prognosis and immune landscape in pancreatic adenocarcinoma

Construction of a cancer-associated fibroblasts-related long non-coding RNA signature to predict prognosis and immune landscape in pancreatic adenocarcinoma

Identification and Validation of a Novel Necroptosis-Related Long Noncoding RNA Prognostic Signature for Lung Adenocarcinoma

Construction of a Cuproptosis-Related Signature for Predicting Prognosis and Immune Response in Clear Cell Renal Cell Carcinoma

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