Bioinformatics profiling utilized a nine immune‐related long noncoding RNA signature as a prognostic target for pancreatic cancer

Wei, Chunmi; Liang, Qingyu; Li, Xue; Li, Hongyu; Liu, Yi; Xiao-hong, Huang; Chen, Xiujie; Guo, Yongxin; Li, Jianjun

doi:10.1002/jcb.28754

Cited by 107 publications

(86 citation statements)

References 52 publications

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“…More recently, Khadirnaikar [26] and Wang [27] et al proposed the prognostic model based on the lncR-SNHG8 and UCA1 in anaplastic gliomas and ccRCC. Chunmi et al highlighted the predicted value of nine IRlncRs (AL138966.2, AL133520.1, AC142472.1, AC127024.5, AC116913.1, AC083880.1, AC124016.1, AC008443.5, and AC092171.5) in pancreatic cancer [28]. Meng et al developed a risk evaluating model based on six lncRNAs (AC005013.5, UBE2R2-AS1, ENTPD1-AS1, RP11-89C21.2, AC073115.6, and XLOC_004803) to assess the prognosis of glioblastoma patients [29].…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

et al. 2020

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Background: Renal cell carcinoma (RCC) is one of the most common aggressive malignant tumors in urogenital system, and the clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma. Immune related long non-coding RNAs (IRlncRs) plentiful in immune cells and immune microenvironment (IME) are potential in evaluating prognosis and assessing the effects of immunotherapy. A completed and meaningful IRlncRs analysis based on abundant ccRCC gene samples from The Cancer Genome Atlas (TCGA) will provide insight in this field. Methods: Based on the TCGA dataset, we integrated the expression profiles of IRlncRs and overall survival (OS) in the 611 ccRCC patients. The immune score of each sample was calculated based on the expression level of immunerelated genes and used to identify the most meaningful IRlncRs. Survival-related IRlncRs (sIRlncRs) was estimated by calculating the algorithm of difference and COX regression analysis in ccRCC patients. Based on the median immune-related risk score (IRRS) developed from the screened sIRlncRs, the high-risk and low-risk components were distinguished. Functional annotation was detected by gene set enrichment analysis (GSEA) and principal component analysis (PCA), and the immune composition and purity of the tumor was evaluated by microenvironment cell population records. The expression levels of three sIRlncRs were verified in various tissues and cell lines. Results: A total of 39 IRlncRs were collected by Pearson correlation analyses among immune score and the lncRNA expression. A total of 7 sIRlncRs were significantly associated with the clinical outcomes of ccRCC patients. Three sIRl-ncRs (ATP1A1-AS1, IL10RB-DT and MELTF-AS1) with the most significant prognostic values were enrolled to build the IRRS model in which the OS of in the high-risk group was shorter than that in the low-risk group. The IRRS was identified as an independent prognosis factor and correlated with the OS. The high-risk group and low-risk group illustrated different distributions in PCA and different immune status in GSEA. Besides, we found the more significant expression in certain ccRCC cell lines and tumor tissues of ccRCC patients compared with the HK-2 and adjacent tissues respectively. Additionally, the expression levels of lncR-MELTF-AS1 and IL10RB-DT were remarkably enhanced along the more advanced T-stages, but the lncR-ATP1A1-AS1 showed the inverse gradient.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

et al. 2020

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“…In a pan‐cancer analysis, Chen et al discovered 4 immune types across 14 solid cancer types, including COAD, LIHC, STAD, and other system cancers. In addition, studies of the tumor immune microenvironment on colorectal cancer, esophageal cancer, pancreatic cancer, and gastric cancer have been reported. We focused on DSCs that have many similar characteristics and risk factors, systematically analyzing the immune environment and providing predictors of DSCs.…”

Section: Discussionmentioning

confidence: 99%

Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer

et al. 2019

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The digestive system cancers are aggressive cancers with the highest mortality worldwide. In this study, we undertook a systematic investigation of the tumor immune microenvironment to identify diagnostic and prognostic biomarkers. The fraction of 22 immune cell types of patients were estimated using CIBERSORT. The least absolute shrinkage and selection operator (LASSO) analysis was carried out to identify important immune predictors. By comparing immune cell compositions in 801 tumor samples and 46 normal samples, we constructed the diagnostic immune score (DIS), showing high specificity and sensitivity in the training (area under the receiver operating characteristic curve [AUC] = 0.929), validation (AUC = 0.935), and different cancer type cohorts (AUC > 0.70 for all).We also established the prognostic immune score (PIS), which was an effective prognostic factor for relapse-free survival in training, validation, and entire cohorts (P < .05). In addition, PIS provided a higher net benefit than TNM stage. A composite nomogram was built based on PIS and patients' clinical information with well-fitted calibration curves (c-index = 0.84). We further used other cohorts from Gene Expression Omnibus databases and obtained similar results, confirming the reliability and validity of the DIS and PIS. In addition, the unsupervised clustering analysis using immune cell proportions revealed 6 immune subtypes, suggesting that the immune types defined as having relatively high levels of M0 or/and M1 macrophages were the high-risk subtypes of relapse. In conclusion, this study comprehensively analyzed the tumor immune microenvironment and identified DIS and PIS for digestive system cancers. K E Y W O R D Sdiagnosis, digestive system cancer, immune cell, prognosis, TCGA

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“…Compared to a certain number of studies carried out to explore IRL signatures in several human malignancies [48][49][50][51][52][53][54], this was the rst study based on a comprehensive analysis that focused on IRL signature in CC. However, our study has some limitations.…”

Section: Discussionmentioning

confidence: 99%

Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

Zheng

Tong

Cao

et al. 2020

Preprint

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Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

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Bioinformatics profiling utilized a nine immune‐related long noncoding RNA signature as a prognostic target for pancreatic cancer

Cited by 107 publications

References 52 publications

Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer

Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

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