Comprehensive insights on pivotal prognostic signature involved in clear cell renal cell carcinoma microenvironment using the ESTIMATE algorithm

Luo, Jun; Xie, Yi; Zheng, Yuxiao; Wang, Chenji; Qi, Feng; Hu, Jiateng; Xu, Yixiang

doi:10.1002/cam4.2983

Cited by 43 publications

(51 citation statements)

References 52 publications

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“…CXCL12 binds primarily to CXCR4 which can cause a mass of signal routing including the immunity [41]. FREM1 has been authenticated as an immune-related gene which may be a potential target for immunotherapy in breast cancer and clear cell renal cell carcinoma [42,43]. The insulin-like growth factor 1(IGF1) pathway has been proven to contribute to the suppression of immune tumor microenvironment(TME) in gynecologic cancers [44].…”

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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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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“…Candidate ccRCC-related genes were selected based on previous published literature., [9][10][11][12][13][14][15][16][17][18][19] including our review article 19 and pathways described in the Cancer Genome Anatomy project. A total of 388 candidate genes were evaluated in the discovery sets.…”

Section: Gene Selectionmentioning

confidence: 99%

“…Therefore, the identification of reliable biomarkers for ccRCC progression is greatly needed. We and others reported candidate biomarkers, such as long non-coding RNAs, 9 gene expression signatures, [10][11][12][13][14][15] epigenetics 16 for ccRCC progression and/or survival. [17][18][19] However, there is currently no clinically accepted molecular biomarker for ccRCC progression.…”

Section: Introductionmentioning

confidence: 99%

Influence of gene expression on survival of clear cell renal cell carcinoma

et al. 2020

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“…This algorithm uses the unique properties of the transcription pro le of cancer samples to infer in ltrating stromal/immune cells. According to reports, the researchers have explored the tumor characteristics and prognosis assessment of lung cancer [13], breast cancer [14], clear cell renal cell carcinoma [15] based on the ESTIMATE algorithm. However, the value of immune and stromal scores for PCa has not been veri ed.…”

Section: Bioinformatics Analysis Uses a Combination Of Biological Stmentioning

confidence: 99%

A Risk Score Model Associated with Tumor Microenvironment Predicts Disease-Free Survival After Radical Prostatectomy

Zhao¹,

Zhang²,

Shi³

et al. 2020

Preprint

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Background Tumor microenvironment (TME) and immune checkpoint inhibitors has been shown to promote active immune responses through different mechanisms. We aimed to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa).Methods The gene transcriptome profiles and clinical information of PCa patients were obtained from the TCGA database, and the immune, stromal and estimate scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of risk score (RS) model based on univariate Cox and LASSO Cox regression models analysis, and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 data set was used for external validation. Finally, 22 subsets of tumor-infiltrating immune cells (Tiics) were analyzed using the Cibersort algorithm.Results In this study, the patients with higher immune, stromal, and estimate scores were associated with poorer DFS, higher Gleason score, and higher AJCC T stage. Based on the immune and stromal scores, the Venny diagram screened out 515 cross DEGs. The univariate COX and Lasso Cox regression models were used to select 18 DEGs from 515 DEGs, and constructed a RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (P<0.001). The AUC of 1-year, 3-year and 5-year DFS rates in RS model were 0.778, 0.754 and 0.750, respectively. In addition, the RS model constructed from 18 genes was found to be more sensitive than Gleason score (1, 3, 5 year AUC= 0.704, 0.677 and 0.682). The nomograms of DFS were established based on RS and Gleason scores. The AUC of the nomograms in the first, third, and fifth years were 0.802, 0.808, and 0.796, respectively. These results have been further validated in GEO. In addition, the proportion of Tregs was higher in high-RS patients (P<0.05), and the expression of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) was higher in high-RS patients (P<0.05).Conclusion We identified 18 TME-related genes from the TCGA database, which were significantly related to DFS in PCa patients.

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Comprehensive insights on pivotal prognostic signature involved in clear cell renal cell carcinoma microenvironment using the ESTIMATE algorithm

Cited by 43 publications

References 52 publications

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

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

Influence of gene expression on survival of clear cell renal cell carcinoma

A Risk Score Model Associated with Tumor Microenvironment Predicts Disease-Free Survival After Radical Prostatectomy

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