Establishment of a prognosis prediction model based on pyroptosis-related signatures associated with the immune microenvironment and molecular heterogeneity in clear cell renal carcinoma

Background: Pytoptosis is essential for tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC). However, the heterogeneity of pyroposis and its relationship with the tumor microenvironment (TME) remain unclear. The aim of the present study was to identify proptosis-related subtypes and construct a prognosis prediction model based on pyroptosis signatures. Methods: First, heterogenous pyroptosis subgroups were explored based on 33 pyroptosis-related genes and ccRCC samples from TCGA, and the model establsihed by LASSO regression was verified by ICGC database. Then, the clinical significance, functional status, immune infiltration, cell-cell communication, genomic alteration and drug sensitivity of different subgroups were further analyzed. Finally, the LASSO-Cox algorithm was applied to narrow down the candidate genes to develop a robust and concise prognostic model. Results: Two heterogenous pyroptosis subgroups were identified: pyroptosis-low immunity-low C1 subtype, and pyroptosis-high immunity-high C2 subtype. Compared with C1, C2 was associated with a higher clinical stage or grade and a worse prognosis. More immune cell infiltration was observed in C2 than that in C1, while the response rate in C2 subgroup was lower than that in C1 subgroup. Pyroptosis related genes were mainly expressed in myeloid cells, and T cells and epithelial cells might influence other cell clusters via Pyroptosis related pathway. In addition, C1 was characterized by MTOR and ATM mutation, while C2 was characterized by more significant alterations in SPEN and ROS1 mutation. Finally, we constructed and validated a robust and promising signature based on the pyroptosis-related risk score for assessing the prognosis in ccRCC. Conclusion: We identified two heterogeneous pyroptosis subtypes and 5 reliable risk signatures to establish a prognosis prediction model. Our findings may help better understand the role of pyroptosis in ccRCC progression and provide a new perspective in the management of ccRCC patients.

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Establishment of a prognosis Prediction Model Based on Pyroptosis-Related Signatures Associated With the Immune Microenvironment and Molecular Heterogeneity in Clear Cell Renal Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.755212 ◽

2021 ◽

Vol 11 ◽

Author(s):

Aimin Jiang ◽

Jialin Meng ◽

Yewei Bao ◽

Anbang Wang ◽

Wenliang Gong ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Prediction Model ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Molecular Heterogeneity ◽

Cell Renal Cell Carcinoma ◽

Prognosis Prediction ◽

Model Based ◽

Prognosis Prediction Model

BackgroundPyroptosis is essential for tumorigenesis and progression of neoplasm. However, the heterogeneity of pyroptosis and its relationship with the tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC) remain unclear. The purpose of the present study was to identify pyroptosis-related subtypes and construct a prognosis prediction model based on pyroptosis signatures.MethodsFirst, heterogenous pyroptosis subgroups were explored based on 33 pyroptosis-related genes and ccRCC samples from TCGA, and the model established by LASSO regression was verified by the ICGC database. Then, the clinical significance, functional status, immune infiltration, cell–cell communication, genomic alteration, and drug sensitivity of different subgroups were further analyzed. Finally, the LASSO-Cox algorithm was applied to narrow down the candidate genes to develop a robust and concise prognostic model.ResultsTwo heterogenous pyroptosis subgroups were identified: pyroptosis-low immunity-low C1 subtype and pyroptosis-high immunity-high C2 subtype. Compared with C1, C2 was associated with a higher clinical stage or grade and a worse prognosis. More immune cell infiltration was observed in C2 than that in C1, while the response rate in the C2 subgroup was lower than that in the C1 subgroup. Pyroptosis-related genes were mainly expressed in myeloid cells, and T cells and epithelial cells might influence other cell clusters via the pyroptosis-related pathway. In addition, C1 was characterized by MTOR and ATM mutation, while the characteristics of C2 were alterations in SPEN and ROS1 mutation. Finally, a robust and promising pyroptosis-related prediction model for ccRCC was constructed and validated.ConclusionTwo heterogeneous pyroptosis subtypes were identified and compared in multiple omics levels, and five pyroptosis-related signatures were applied to establish a prognosis prediction model. Our findings may help better understand the role of pyroptosis in ccRCC progression and provide a new perspective in the management of ccRCC patients.

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Downregulation of ACE2 is associated with advanced pathological features and poor prognosis in clear cell renal cell carcinoma

Future Oncology ◽

10.2217/fon-2020-1164 ◽

2021 ◽

Author(s):

Tianjiao Wang ◽

Fang Xie ◽

Yun-Hui Li ◽

Bin Liang

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Immune Cell ◽

Clear Cell ◽

Expression Profiles ◽

Histological Grade ◽

Clinical Stage ◽

The Cancer Genome Atlas ◽

Cell Renal Cell Carcinoma

Aims: The aim of this study was to explore the alteration in ACE2 expression and correlation between ACE2 expression and immune infiltration in clear cell renal cell carcinoma (ccRCC). Methods: The authors first analyzed the expression profiles and prognostic value of ACE2 in ccRCC patients using The Cancer Genome Atlas public database. The authors used ESTIMATE and CIBERSORT algorithms to analyze the correlation between ACE2 expression and tumor microenvironment in ccRCC samples. Results: ACE2 was correlated with sex, distant metastasis, clinical stage, tumor T stage and histological grade. Moreover, downregulation of ACE2 was correlated with unfavorable prognosis. In addition, ACE2 expression was associated with different immune cell subtypes. Conclusion: The authors' analyses suggest that ACE2 plays an important role in the development and progression of ccRCC and may serve as a potential prognostic biomarker in ccRCC patients.

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Systematic Pan-Cancer Analysis of KIF23 and a Prediction Model Based on KIF23 in Clear Cell Renal Cell Carcinoma (ccRCC)

Pharmacogenomics and Personalized Medicine ◽

10.2147/pgpm.s337695 ◽

2021 ◽

Vol Volume 14 ◽

pp. 1717-1729

Author(s):

Xiaojie Bai ◽

Yuanfei Cao ◽

Xin Yan ◽

Kurerban Tuoheti ◽

Guowei Du ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Prediction Model ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Cell Renal Cell Carcinoma ◽

Model Based ◽

Pan Cancer

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An improved clear cell renal cell carcinoma stage prediction model based on gene sets

BMC Bioinformatics ◽

10.1186/s12859-020-03543-0 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Fangjun Li ◽

Mu Yang ◽

Yunhe Li ◽

Mingqiang Zhang ◽

Wenjuan Wang ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Prediction Model ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Cell Renal Cell Carcinoma ◽

Model Based ◽

Gene Sets

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Prognosis prediction model based on competing endogenous RNAs for recurrence of colon adenocarcinoma

BMC Cancer ◽

10.1186/s12885-020-07163-y ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Li Peng Jin ◽

Tao Liu ◽

Fan Qi Meng ◽

Jian Dong Tai

Keyword(s):

Prediction Model ◽

Regulatory Network ◽

Colon Adenocarcinoma ◽

Training Dataset ◽

Validation Dataset ◽

Prognostic Signature ◽

Prognosis Prediction ◽

Model Based ◽

Competing Endogenous Rnas ◽

Prognosis Prediction Model

Abstract Background Colon adenocarcinoma (COAD) patients who develop recurrence have poor prognosis. Our study aimed to establish effective prognosis prediction model based on competing endogenous RNAs (ceRNAs) for recurrence of COAD. Methods COAD expression profilings downloaded from The Cancer Genome Atlas (TCGA) were used as training dataset, and expression profilings of GSE29623 retrieved from Gene Expression Omnibus (GEO) were set as validation dataset. Differentially expressed RNAs (DERs) between non-recurrent and recurrent specimens in training dataset were screened, and optimum prognostic signature DERs were revealed to establish prognostic score (PS) model. Kaplan-Meier survival analysis was conducted for PS model, and GEO dataset was used for validation. Prognosis prediction efficiencies were evaluated by area under curve (AUC) and C-index. Meanwhile, ceRNA regulatory network was constructed by using signature mRNAs, lncRNAs and miRNAs. Results We identified 562 DERs including 42 lncRNAs, 36 miRNAs, and 484 mRNAs. PS prediction model, consisting of 17 optimum prognostic signature DERs, showed that high risk group had significantly poorer prognosis (5-year AUC = 0.951, C-index = 0.788), which also validated in GSE29623. Prognosis prediction model incorporating multi-RNAs with pathologic distant metastasis (M) and pathologic primary tumor (T) (5-year AUC = 0.969, C-index = 0.812) had better efficiency than clinical prognosis prediction model (5-year AUC = 0.712, C-index = 0.680). In the constructed ceRNA regulatory network, lncRNA NCBP2-AS1 could interact with hsa-miR-34c and hsa-miR-363, and lncRNA LINC00115 could interact with hsa-miR-363 and hsa-miR-4709. SIX4, GRAP, NKAIN4, MMAA, and ERVMER34–1 are regulated by hsa-miR-4709. Conclusion Prognosis prediction model incorporating multi-RNAs with pathologic M and pathologic T may have great value in COAD prognosis prediction.

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