The novel transcriptomic signature of angiogenesis predicts clinical outcome, tumor microenvironment and treatment response for prostate adenocarcinoma

Gu, Chengyuan; Dai, Bo; Zhu, Yao; Lin, Guo-Wen; Wang, Hongkai; Ye, Ding-Wei; Qin, Xiaojian

doi:10.1186/s10020-022-00504-6

Cited by 5 publications

(3 citation statements)

References 80 publications

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“…In model construction, univariate proportional hazards model (Cox) analysis was performed to screen out the survival-related lncRNAs with ‘survival’ R package ( 20 ). Then, least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation was employed to minimize the errors for the construction of an ideal risk model ( 21 ). The risk score calculation formula was the sum of coefficient × x (x is the FPKM value of each cuproptosis-related lncRNA) ( 22 ).…”

Section: Methodsmentioning

confidence: 99%

Development and verification of a newly established cuproptosis-associated lncRNA model for predicting overall survival in uterine corpus endometrial carcinoma

Hu,

Wang,

Chen

et al. 2023

Transl Cancer Res

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Background Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignant tumor with high recurrence and mortality rates. This study aimed to develop and validate a prognostic model for patients with UCEC based on cuproptosis-related long non-coding RNA (lncRNA) signature. Methods Transcriptome and clinical UCEC data were obtained from The Cancer Genome Atlas (TCGA) database. Correlation analysis was conducted to screen out the cuproptosis-related lncRNAs, and univariate regression analysis was performed to determine prognostic factors associated with overall survival (OS). A cuproptosis-related lncRNA risk model was constructed through least absolute shrinkage and selection operator (LASSO) regression and cross-validation. The accuracy and reliability of the model were verified through Kaplan-Meier (KM), proportional hazards model (Cox) regression, nomogram, principal component analysis (PCA), and stage analysis. Gene Ontology (GO) enrichment, immune function, and tumor mutation burden (TMB) analyses were conducted between low-risk and high-risk groups, and antineoplastic drugs were predicted. Results By correlation analysis, 155 cuproptosis-related lncRNAs were acquired, and 9 lncRNAs were identified as independent prognostic factors. A 6-cuproptosis-related lncRNA model was established. The results revealed that patients in the high-risk group were more inclined to have a poor OS than those in the low-risk group. Risk score was an independent prognostic factor and had a high accuracy and predictive value. The extracellular structure and anchored components of membrane-related GO terms were significantly enriched. Immune function and TMB results were assumed to be different from each other, which might explain a better outcome in the low-risk group than that in the high-risk group. Eighteen compounds were predicted as chemotherapy drugs with high half maximal inhibitory concentration (IC50) in the high-risk group. Conclusions We successfully developed a cuproptosis-related lncRNA risk model for the prediction of prognosis, while simultaneously providing insights on new approaches for immunotherapy and chemotherapy for patients with UCEC.

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Section: Methodsmentioning

confidence: 99%

Development and verification of a newly established cuproptosis-associated lncRNA model for predicting overall survival in uterine corpus endometrial carcinoma

Hu,

Wang,

Chen

et al. 2023

Transl Cancer Res

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“…In recent year, algorithms have been developed to predict tumor purity using gene expression data based on the TCGA database. For example, (36,37), breast cancer (38,39), colorectal cancer (40)(41)(42) and hepatocellular carcinoma (43), which demonstrated that such big-data-based algorithms are effective, although its utility on immune and/or stromal scores in glioblastoma multiforme (GBM) has not been explored in detail.…”

Section: What Is the Implication And What Should Change Now?mentioning

confidence: 99%

Exploration of prognostic biomarkers in head and neck squamous cell carcinoma microenvironment from TCGA database

Liu¹,

Bi²,

Pi³

et al. 2023

Ann Transl Med

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Background: Immune checkpoint blockade (ICB) therapies have redefined human cancer treatment, including for head and neck squamous cell carcinoma (HNSCC). However, clinical responses to various immune checkpoint inhibitors are often accompanied by immune-related adverse events (irAEs). Therefore, it is crucial to obtain a comprehensive understanding of the association between different immune tumor microenvironments (TMEs) and the immunotherapeutic response.Methods: The research data were obtained from The Cancer Genome Atlas (TCGA) database. We applied RNA-seq genomic data from tumor biopsies to assess the immune TME in HNSCC. As the TME is a heterogeneous system that is highly associated with HNSCC progression and clinical outcome, we relied on the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores that were evaluated based on the immune or stromal components in the TME. Then, the Tumor Immune Dysfunction and Exclusion algorithm (TIDE) was used to predict the benefits of ICB to each patient. Finally, we identified specific prognostic tumor-infiltrating immune cells (TIICs) by quantifying the cellular composition of the immune response in HNSCC and its association to survival outcome, using the CIBERSORT algorithm.Results: Utilizing the HNSCC cohort of the TCGA database and TIDE and ESTIMATE algorithmderived immune scores, we obtained a list of microenvironment-associated lncRNAs that predicted different clinical outcomes in HNSCC patients. We validated these correlations in a different HNSCC cohort available from the TCGA database and provided insight into the prediction of response to ICB therapies in HNSCC.Conclusions: This study confirmed that CD8 + T cells were significantly associated with better survival in HNSCC and verified that the top five significantly mutated genes (SMGs) in the TCGA HNSCC cohort were TP53, TTN, FAT1, CDKN2A, and MUC16. A high level of CD8 + T cells and high immune and stroma scores corresponded to a better survival probability in HNSCC.Keywords: Head and neck squamous cell carcinoma (HNSCC); The Cancer Genome Atlas (TCGA); tumor microenvironment (TME); tumor-infiltrating immune cell (TIIC); immune checkpoint blockade (ICB)

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“…Recent evidence suggests that PRAD prognosis is heavily dependent on the tumor microenvironment [ 4 ]. The tumor immune microenvironment (TIME), comprised of extracellular matrix, stromal cells, and other tumor associated cells, can modulate the tumor’s response to therapy and can influence the progression of the tumor [ 5 ]. TIME has been reported to profoundly influence the growth and metastasis of cancer.…”

Section: Introductionmentioning

confidence: 99%

Identifying the tumor immune microenvironment-associated prognostic genes for prostate cancer

Zong,

Gao

2024

Discov Onc

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Purpose This study aimed to explore novel tumor immune microenvironment (TIME)-associated biomarkers in prostate adenocarcinoma (PRAD). Methods PRAD RNA-sequencing data were obtained from UCSC Xena database as the training dataset. The ESTIMATE package was used to evaluate stromal, immune, and tumor purity scores. Differentially expressed genes (DEGs) related to TIME were screened using the immune and stromal scores. Gene functions were analyzed using DAVID. The LASSO method was performed to screen prognostic TIME-related genes. Kaplan–Meier curves were used to evaluate the prognosis of samples. The correlation between the screened genes and immune cell infiltration was explored using Tumor IMmune Estimation Resource. The GSE70768 dataset from the Gene Expression Omnibus was used to validate the expression of the screened genes. Results The ESTIMATE results revealed that high immune, stromal, and ESTIMATE scores and low tumor purity had better prognoses. Function analysis indicated that DEGs are involved in the cytokine–cytokine receptor interaction signaling pathway. In TIME-related DEGs, METTL7B, HOXB8, and TREM1 were closely related to the prognosis. Samples with low expression levels of METTL7B, HOXB8, and TREM1 had better survival times. Similarly, both the validation dataset and qRT-PCR suggested that METTL7B, HOXB8, and TREM1 were significantly decreased. The three genes showed a positive correlation with immune infiltration. Conclusions This study identified three TIME-related genes, namely, METTL7B, HOXB8, and TREM1, which correlated with the prognosis of patients with PRAD. Targeting the TIME-related genes might have important clinical implications when making decisions for immunotherapy in PRAD.

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The novel transcriptomic signature of angiogenesis predicts clinical outcome, tumor microenvironment and treatment response for prostate adenocarcinoma

Cited by 5 publications

References 80 publications

Development and verification of a newly established cuproptosis-associated lncRNA model for predicting overall survival in uterine corpus endometrial carcinoma

Development and verification of a newly established cuproptosis-associated lncRNA model for predicting overall survival in uterine corpus endometrial carcinoma

Exploration of prognostic biomarkers in head and neck squamous cell carcinoma microenvironment from TCGA database

Identifying the tumor immune microenvironment-associated prognostic genes for prostate cancer

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