Comprehensive Analysis to Identify SPP1 as a Prognostic Biomarker in Cervical Cancer

Background. Cervical cancer is the fourth common cancer among women. Its prognosis needs our more attention. Our purpose was to identity new prognostic gene sets to help other researchers develop more effective treatment for cervical cancer patients and improve the prognosis of patients. Methods. We used gene set variation analysis (GSVA) to calculate the enrichment scores of gene sets and identified three subtypes of cervical cancer through the Cox regression model, k-means clustering algorithm, and nonnegative matrix factorization method (NMF). Chi-square test was utilized to test whether a certain clinical characteristic is different among divided subtypes. We further screened the prognostic gene sets using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to analyze which pathways and function the genes from screened gene sets enriched. Search Tool for the Retrieval of Interacting Genes (STRING) was used to draw the protein-protein interaction network, and Cytoscape was used to visualize the hub genes of protein-protein interaction network. Results. We identified three novel subtypes of cervical cancer in The Cancer Genome Atlas (TCGA) samples and validated in Gene Expression Omnibus (GEO) samples. There were significant variations between the three subtypes in histological type, T stage, M stage, and N stage. T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP and N_HALLMARK_ANGIOGENESIS were screened prognostic gene sets. The prognostic model was as follows: riskScore = T _ GSE 36888 _ UNTREATED _ VS _ IL 2 _ TREATED _ STAT 5 _ AB _ KNOCKIN _ TCELL _ 2 H _ U P ∗ 2.617 + N _ HALLMARK _ ANGIOGENESI S ∗ 4.860 . Survival analysis presented that in these two gene sets, high enrichment scores were all significantly related to worse overall survival. The hub genes from T gene set included CXCL1, CXCL2, CXCL8, ALDOA, TALDO1, LDHA, CCL4, FCAR, FCER1G, SAMSN1, LILRB1, SH3PXD2B, PPM1N, PKM, and FKBP4. As for N gene sets, the hub genes included ITGAV, PTK2, SPP1, THBD, and APOH. Conclusions. Three novel subtypes and two prognostic gene sets were identified. 15 hub genes for T gene set and 5 hub genes for N gene set were discovered. Based on these findings, we can develop more and more effective treatments for cervical cancer patients. Based on the gene enriched pathways, we can development specific drugs targeting the pathways.

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“…These hub genes were mostly related to protein regulation. PTK2 [ 49 ] SPP1 [ 50 ] were also related to the progression of cervical cancer.…”

Section: Discussionmentioning

confidence: 99%

Identification of Specific Cervical Cancer Subtypes and Prognostic Gene Sets in Tumor and Nontumor Tissues Based on GSVA Analysis

Zhong

Wang

Yin

et al. 2022

Journal of Oncology

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“…PANK1 messenger ribonucleic acid (mRNA) levels in several cancer types were analyzed using the Tumor Immune Estimation Resource (TIMER) database, which is based on data from TCGA (17). The TCGA-KIRC cohort comprised a total of 539 tumor and 72 adjacent normal tissue samples from ccRCC patients, and all the clinical information.…”

Section: Gene Expression Profile Analysismentioning

confidence: 99%

PANK1 associates with cancer metabolism and immune infiltration in clear cell renal cell carcinoma: a retrospective prognostic study based on the TCGA database

Wang¹,

Liu²,

Luo³

et al. 2022

Transl Cancer Res TCR

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Background: Identify key biomarkers to improve the clinical prognosis of patients with advanced and metastatic clear cell renal cell carcinoma (ccRCC) remains an important research topic. Recently, ccRCC has been regarded as a metabolic disease. Pantothenate kinase-1 (PANK1) has been shown to play an important regulatory role in global metabolism and associates with the pathogenesis of hepatocellular carcinoma. Therefore, we aimed to investigate the role of PANK1 in the prognosis of ccRCC and in metabolism and immunity. Methods: PANK1 messenger ribonucleic acid (RNA) expression patterns in ccRCC using The Cancer Genome Atlas (TCGA) database. The clinical prognostic significance of PANK1 in ccRCC and a Cox regression was performed to evaluate the clinical factors associated with prognosis with confounding factors adjusted. The signaling pathways related to PANK1 expression were identified by Gene Ontology (GO) investigation and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The Tumor Immune Estimation Resource database was used to analyze the correlation between PANK1 and tumor-infiltrating immune cells.Results: A total of 539 ccRCC patients and corresponding clinical samples and data from TCGA were included in this analysis. Significant differences were observed in PANK1 expression levels between tumor tissues and adjacent normal tissues in both TCGA-Kidney Renal Clear Cell Carcinoma cohort (4.40 vs.2.94, P<0.001). PANK1 expression was found to be correlated with pathological stage, histological grade, age, sex, and clinical prognosis. Specifically, the low expression of PANK1 was found to be closely related to poor overall survival (OS), disease-specific survival (DSS), and the progression-free survival (PFS) in ccRCC patients. The receiver operating characteristic curve suggested that PANK1 could be a potential prognostic biomarker (area under the curve =0.880), and that the promoter methylation levels of PANK1 were correlated with clinical factors. Further, PANK1 expression was found to be associated with multiple immune cell types and correlated with the enrichment of these cells. Finally, we further investigated the role of PANK1 in tumor growth and mitochondrial metabolism using ccRCC cells.Conclusions: PANK1 correlates with ccRCC prognosis, tumor immune status and metabolism using the TGCA data. PANK1 might be a prognostic marker of clinical prognosis for ccRCC patients. the following article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/ article/view/10.21037/tcr-22-1488/rc). Methods Study design and research proceduresThis study is a retrospective prognostic study analyzing the expression difference of PANK1 between tumor and adjacent normal tissues, correlations with the clinical factors, overall

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“…A protein-protein interaction network (PPI) was constructed, and a module analysis was performed using STRING database (http://string-db.org/ (Last accessed on 15 November 2022)) with an interaction with a combined score of >0.4. To further clarify the function of PVR, GO and KEGG analysis using the top 100 genes most related with PVR were performed by the method described in a previous study [21].…”

Section: Ppi Network and Go/kegg Analysismentioning

confidence: 99%

PVR—A Prognostic Biomarker Correlated with Immune Cell Infiltration in Hepatocellular Carcinoma

Liu

Quan

et al. 2022

Diagnostics

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The poliovirus receptor (PVR) is a member of the immunoglobulin superfamily (Ig SF) and is essential for the promotion of cancer cell proliferation and invasion. However, the correlation between PVR expression and prognosis as well as immune infiltration in hepatocellular carcinoma (HCC) remains unclear. The expression level of PVR was quantified using the Tumor and Tumor Immunity Evaluation Resource (TIMER) and Sangerbox. The Gene Expression Omnibus (GEO) database was used to validate the PVR expression. The receiver operating characteristic (ROC) curve was used to evaluate the feasibility of using PVR as a differentiating factor according to the area under curve (AUC) score. A PVR binding protein network was built using the STRING tool. An enrichment analysis using the R package clusterProfiler was used to explore the potential function of PVR. Immune infiltration analysis was calculated with ESTIMATE algorithms. We also assessed the correlation between PVR expression and immune infiltration by the single-sample Gene Set Enrichment Analysis (ssGSEA) method from the R package GSVA and TIMER database. The results showed that PVR was commonly overexpressed in multiple types of tumors including HCC. The data of GSE64041 confirmed the same result. The ROC curve suggested that PVR could be a potential diagnostic biomarker. Additionally, high mRNA expression of PVR in HCC was significantly correlated with poor overall survival (OS) and relapse free survival (RFS). Results also indicated correlations between PVR mRNA expression with the level of infiltration immune cells including B cells, CD8+ T cells, cytotoxic cells, DCs, CD56dim NK cells, pDCs, and Th2 cells. Furthermore, the PVR level was significantly correlated with immune markers for immunosuppressive cells in HCC. In conclusion, PVR might be an important regulator of tumor immune cell infiltration and a valuable prognostic biomarker in HCC. However, additional work is needed to fully elucidate the underlying mechanisms.

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Comprehensive Analysis to Identify SPP1 as a Prognostic Biomarker in Cervical Cancer

Cited by 39 publications

References 29 publications

Identification of Specific Cervical Cancer Subtypes and Prognostic Gene Sets in Tumor and Nontumor Tissues Based on GSVA Analysis

Identification of Specific Cervical Cancer Subtypes and Prognostic Gene Sets in Tumor and Nontumor Tissues Based on GSVA Analysis

PANK1 associates with cancer metabolism and immune infiltration in clear cell renal cell carcinoma: a retrospective prognostic study based on the TCGA database

PVR—A Prognostic Biomarker Correlated with Immune Cell Infiltration in Hepatocellular Carcinoma

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