A Comprehensive Pan-Cancer Analysis for Pituitary Tumor-Transforming Gene 1

Gong, Siming; Wu, Changwu; Duan, Yingjuan; Tang, Juyu

doi:10.3389/fgene.2022.843579

Cited by 5 publications

(5 citation statements)

References 63 publications

(78 reference statements)

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“…PTTG1-induced senescence was p53-dependent and telomerase-independent [ 49 ]. However, the high expression of PTTG1 in tumor cells can inhibit senescence, which has also been confirmed by many studies [ 50 , 51 ]. For example, a study by Tong et al showed that knocking down PTTG1 strengthened drug-induced senescence in colon carcinoma cells, confirming that PTTG1 functions to suppress drug-induced senescence [ 52 ].…”

Section: Discussionmentioning

confidence: 64%

Cell senescence-associated genes predict the malignant characteristics of glioblastoma

et al. 2022

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Background Glioblastoma (GBM) is the most malignant, aggressive and recurrent primary brain tumor. Cell senescence can cause irreversible cessation of cell division in normally proliferating cells. According to studies, senescence is a primary anti-tumor mechanism that may be seen in a variety of tumor types. It halts the growth and spread of tumors. Tumor suppressive functions held by cellular senescence provide new directions and pathways to promote cancer therapy. Methods We comprehensively analyzed the cell senescence-associated genes expression patterns. The potential molecular subtypes were acquired based on unsupervised cluster analysis. The tumor immune microenvironment (TME) variations, immune cell infiltration, and stemness index between 3 subtypes were analyzed. To identify genes linked with GBM prognosis and build a risk score model, we used weighted gene co-expression network analysis (WGCNA), univariate Cox regression, Least absolute shrinkage and selection operator regression (LASSO), and multivariate Cox regression analysis. And the correlation between risk scores and clinical traits, TME, GBM subtypes, as well as immunotherapy responses were estimated. Immunohistochemistry (IHC) and cellular experiments were performed to evaluate the expression and function of representative genes. Then the 2 risk scoring models were constructed based on the same method of calculation whose samples were acquired from the CGGA dataset and TCGA datasets to verify the rationality and the reliability of the risk scoring model. Finally, we conducted a pan-cancer analysis of the risk score, assessed drug sensitivity based on risk scores, and analyzed the pathways of sensitive drug action. Results The 3 potential molecular subtypes were acquired based on cell senescence-associated genes expression. The Log-rank test showed the difference in GBM patient survival between 3 potential molecular subtypes (P = 0.0027). Then, 11 cell senescence-associated genes were obtained to construct a risk-scoring model, which was systematically randomized to distinguish the train set (n = 293) and the test set (n = 292). The Kaplan-Meier (K-M) analyses indicated that the high-risk score in the train set (P < 0.0001), as well as the test set (P = 0.0053), corresponded with poorer survival. In addition, the high-risk score group showed a poor response to immunotherapy. The reliability and credibility of the risk scoring model were confirmed according to the CGGA dataset, TCGA datasets, and Pan-cancer analysis. According to drug sensitivity analysis, it was discovered that LJI308, a potent selective inhibitor of RSK pathways, has the highest drug sensitivity. Moreover, the GBM patients with higher risk scores may potentially be more beneficial from drugs that target cell cycle, mitosis, microtubule, DNA replication and apoptosis regulation signaling. Conclusion We identified potential associations between clinical characteristics, TME, stemness, subtypes, and immunotherapy, and we clarified the therapeutic usefulness of cell senescence-associated genes.

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Section: Discussionmentioning

confidence: 64%

Cell senescence-associated genes predict the malignant characteristics of glioblastoma

et al. 2022

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“…In this work, RRP1B and SERBP1 oncogenes were obtained via intersection analysis of RCC2-interacted and RCC2-correlated genes. These oncogenes may be linked to EMT in some tumors [ [38] , [39] , [40] , [41] ]. This might reveal the potential mechanism by which RCC2 leads to cancer formation and metastasis.…”

Section: Discussionmentioning

confidence: 99%

A human pan-cancer system analysis of regulator of chromatin condensation 2

Gong¹,

Wu²,

Wu³

et al. 2023

Heliyon

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“…Notably, tissue levels of PTTG1 gene were able to stratify patients into two groups based on their overall survival, with patients with high expression levels resulting in poor prognostic outcome (i.e., overall survival). This gene encodes for PTTG1, which is an oncogenic transcription factor known to be upregulated in number of malignancies 38 . PTTG1 is involved in promotion of angiogenesis via FGF2 and VEGF and cellular proliferation 39,40 …”

Section: Discussionmentioning

confidence: 99%

“…This gene encodes for PTTG1, which is an oncogenic transcription factor known to be upregulated in number of malignancies. 38 PTTG1 is involved in promotion of angiogenesis via FGF2 and VEGF and cellular proliferation. 39,40 F I G U R E 5 Pathway analysis.…”

Section: Discussionmentioning

confidence: 99%

Gene expression profiling of pancreatic ductal adenocarcinomas in response to neoadjuvant chemotherapy

et al. 2023

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AimPancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate of all major cancers. Chemotherapy is the mainstay systemic therapy for PDAC, and chemoresistance is a major clinical problem leading to therapeutic failure. This study aimed to identify key differences in gene expression profile in tumors from chemoresponsive and chemoresistant patients.MethodsArchived formalin‐fixed paraffin‐embedded tumor tissue samples from patients treated with neoadjuvant chemotherapy were obtained during surgical resection. Specimens were macrodissected and gene expression analysis was performed. Multi‐ and univariate statistical analysis was performed to identify differential gene expression profile of tumors from good (0%–30% residual viable tumor [RVT]) and poor (>30% RVT) chemotherapy‐responders.ResultsInitially, unsupervised multivariate modeling was performed by principal component analysis, which demonstrated a distinct gene expression profile between good‐ and poor‐chemotherapy responders. There were 396 genes that were significantly (p < 0.05) downregulated (200 genes) or upregulated (196 genes) in tumors from good responders compared to poor responders. Further supervised multivariate analysis of significant genes by partial least square (PLS) demonstrated a highly distinct gene expression profile between good‐ and poor responders. A gene biomarker of panel (IL18, SPA17, CD58, PTTG1, MTBP, ABL1, SFRP1, CHRDL1, IGF1, and CFD) was selected based on PLS model, and univariate regression analysis of individual genes was performed. The identified biomarker panel demonstrated a very high ability to diagnose good‐responding PDAC patients (AUROC: 0.977, sensitivity: 82.4%; specificity: 87.0%).ConclusionA distinct tumor biological profile between PDAC patients who either respond or not respond to chemotherapy was identified.

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A Comprehensive Pan-Cancer Analysis for Pituitary Tumor-Transforming Gene 1

Cited by 5 publications

References 63 publications

Cell senescence-associated genes predict the malignant characteristics of glioblastoma

Cell senescence-associated genes predict the malignant characteristics of glioblastoma

A human pan-cancer system analysis of regulator of chromatin condensation 2

Gene expression profiling of pancreatic ductal adenocarcinomas in response to neoadjuvant chemotherapy

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