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Establishment and validation of an individualized macrophage-related gene signature to predict overall survival in patients with triple negative breast cancer.

ABSTRACT: Background:Recently, researchers have classified highly heterogeneous triple negative breast cancer (TNBC) into different subtypes from different perspectives and investigated the characteristics of different subtypes to pursue individualized treatment. With the increase of immunotherapy and its preliminary application in TNBC treatment, the value of immune-related strategies in the treatment of TNBC has been initially reflected. Based thereon, this study plans to classify and further explore TNBC from the perspective of immune cell infiltration. Method:The fractions of immune cells of TNBC patients were assessed by six immune component analysis methods in The Cancer Genome Atlas (TCGA) database. Hub genes significantly related to poor prognosis were verified by weighted gene co-expression network analysis (WGCNA) analysis, Lasso analysis, and univariate KM analysis. Two cohorts of TNBC patients with complete prognosis information were collected for validation analysis. Finally, the Genomics of Drug Sensitivity in Cancer (GDSC) database was adopted to ascertain the sensitivity differences of different populations based on hub-gene grouping to different chemotherapy drugs. Results:Five hub genes (CD79A, CXCL13, IGLL5, LHFPL2, and PLEKHF1) of the key co-expression gene module could divide TNBC patients into two groups (Cluster A and Cluster B) based on consistency cluster analysis. The patients with Cluster A were responsible for significantly worse prognosis than the patients with Cluster B (P = 0.023). In addition, another classification method, PCoA, and two other datasets (GSE103091 and GSE76124), were used to obtain consistent results with previous findings, which verified the stability of the classification method and dataset in this study. The grouping criteria based on the previous results were developed and the accuracy of the cut-off values was validated. A prognosis model of TNBC patients was then constructed based on the grouping results of five hub genes and N staging as prognostic factors. The results of ROC and decision curve analyses showed that this model had high prediction accuracy and patients could benefit therefrom. Finally, GDSC database analysis proved that patients in Cluster A were more sensitive to Vinorelbine. Separate analysis of the sensitivity of patients in Cluster A to Gemcitabine and Vinorelbine showed that the patients in Cluster A exhibited higher sensitivity to Vinorelbine. We hypothesized that these five genes were related to gemcitabine resistance and they could serve as biomarkers for clinical drug decision-making after anthracene resistance and taxane resistance in patients with advanced TNBC. Conclusion:This study found five hub prognostic genes associated with macrophages, and a prognostic model was established to predict the survival of TNBC patients. Finally, these five genes were related to gemcitabine resistance in TNBC patients.

SUBMITTER: Su P

PROVIDER: S-EPMC8621725 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHigh-grade serious ovarian carcinoma (HGSOC) is a subtype of ovarian cancer with a different prognosis attributable to genetic heterogeneity. The prognosis of patients with advanced HGSOC requires prediction by genetic markers. This study systematically analyzed gene expression profile data to establish a genetic marker for predicting HGSOC prognosis.MethodsThe RNA-seq data set and information on clinical follow-up of HGSOC were retrieved from Gene Expression Omnibus (GEO) database, and the data were standardized by DESeq2 as a training set. On the other hand, HGSOC RNA sequence data and information on clinical follow-up were retrieved from The Cancer Genome Atlas (TCGA) as a test set. Additionally, ovarian cancer microarray data set was obtained from GEO as the external validation set. Prognostic genes were screened from the training set, and characteristic selection was performed using the least absolute shrinkage and selection operator (LASSO) with 80% re-sampling for 5000 times. Genes with a frequency of more than 2000 were selected as robust biomarkers. Finally, a gene-related prognostic model was validated in both the test and GEO validation sets.ResultsA total of 148 genes were found to be significantly correlated with HGSOC prognosis. The expression profile of these genes could stratify HGSOC prognosis and they were enriched to multiple tumor-related regulatory pathways such as tyrosine metabolism and AMPK signaling pathway. AKR1B10 and ANGPT4 were obtained after 5000-time re-sampling by LASSO regression. AKR1B10 was associated with the metastasis and progression of several tumors. In this study, Cox regression analysis was performed to create a 2-gene signature as an independent prognostic factor for HGSOC, which has the ability to stratify risk samples in all three data sets (p < 0.05). The Gene Set Enrichment Analysis (GSEA) discovered abnormally active REGULATION_OF_AUTOPHAGY and OLFACTORY_TRANSDUCTION pathways in the high-risk group samples.ConclusionThis study resulted in the creation of a 2-gene molecular prognostic classifier that distinguished clinical features and was a promising novel prognostic tool for assessing the prognosis of HGSOC. RiskScore was a novel prognostic model which might be effective in guiding accurate prognosis of HGSOC.

Project description:Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop prediction models for various clinical endpoints in lung cancer. However, the robustness of radiomic features is under question and has been identified as one of the roadblocks in the implementation of a radiomic-based prediction model in the clinic. Many past studies have suggested identifying the robust radiomic feature to develop a prediction model. In our earlier study, we identified robust radiomic features for prediction model development. The objective of this study was to develop and validate the robust radiomic signatures for predicting 2-year overall survival in non-small cell lung cancer (NSCLC). This retrospective study included a cohort of 300 stage I-IV NSCLC patients. Institutional 200 patients' data were included for training and internal validation and 100 patients' data from The Cancer Image Archive (TCIA) open-source image repository for external validation. Radiomic features were extracted from the CT images of both cohorts. The feature selection was performed using hierarchical clustering, a Chi-squared test, and recursive feature elimination (RFE). In total, six prediction models were developed using random forest (RF-Model-O, RF-Model-B), gradient boosting (GB-Model-O, GB-Model-B), and support vector(SV-Model-O, SV-Model-B) classifiers to predict 2-year overall survival (OS) on original data as well as balanced data. Model validation was performed using 10-fold cross-validation, internal validation, and external validation. Using a multistep feature selection method, the overall top 10 features were chosen. On internal validation, the two random forest models (RF-Model-O, RF-Model-B) displayed the highest accuracy; their scores on the original and balanced datasets were 0.81 and 0.77 respectively. During external validation, both the random forest models' accuracy was 0.68. In our study, robust radiomic features showed promising predictive performance to predict 2-year overall survival in NSCLC.

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Establishment and validation of an individualized macrophage-related gene signature to predict overall survival in patients with triple negative breast cancer.

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