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Descriptive statistics of dataset from the meta-analysis and meta-regression analysis on prognostic significance of pre-treatment systemic hemato-immunological indices of cervical cancer patients.

ABSTRACT: In this study, we perform a meta-analysis and meta-regression analysis for the article entitled "Prognostic value of systemic hemato-immunological indices in uterine cervical cancer: A systemic review, meta-analysis, and meta-regression of observational studies." [1] We implemented quantitative meta-analyses and time series meta-regression analysis to determine whether systemic hemato-immunological indices, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), thrombocyte-to-lymphocyte ratio (TLR), and C-reactive protein/albumin ratio (CAR) are associated with an increased risk of cervical collision cancer. In all, 9558 patients from 22 studies were included after a systematic data search, performed comprehensively using the following databases: MEDLINE, Web of Science, Embase, and Cochrane. The meta-analysis was conducted with a random-effects model using the Review Manager software (Revman version 5.3). The overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS) data were compared among each observational study. All data are expressed as hazard ratios (HRs) and 95% confidence intervals (CIs), and were calculated using the generic inverse of variance method. Statistical heterogeneity was quantified using Cochrane's Q statistic and Higgins I² statistic. Subgroup analysis was performed to investigate the sources of heterogeneity. Furthermore, quality assessment of the included datasets was presented according to the Newcastle-Ottawa Scale method. Additionally, sensitivity analysis was conducted to explore the sources of heterogeneity and analyze whether the results were stable and reliable. Meta-analysis random-effect approach was used for the regression to evaluate the effect of age, presence of squamous cell carcinoma patients, and number of evaluated NLR and PLR parameters on patient survival.

SUBMITTER: Han X

PROVIDER: S-EPMC8039823 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The present dataset ("dataset 3") is a subset of a large metastudy on AML classfication. It contains normalized gene expression values of 1181 samples. In total, three datasets were generated, each containing data of a different platforms: dataset 1 (Affymetrix HG-U133 A microarrays), dataset 2 (Affymetrix HG-U133 2.0 microarrays) and dataset 3 (RNA-seq). Dataset 3 was generated using the following strategy: All data sets published in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) on 20 September 2017 were reviewed for inclusion in the present study. Basic criteria for inclusion were the cell type under study (human peripheral blood mononuclear cells (PMBCs) and/or bone marrow samples) as well as the species (Homo sapiens). Furthermore, GEO SuperSeries were excluded to avoid duplicated samples. We filtered the datasets for data generated with high-throughput RNA sequencing (RNA-seq) and excluded studies with very small sample sizes (< 10 samples). We then applied a disease-specific search, in which we filtered for acute myeloid leukemia, other leukemia and healthy or non-leukemia-related samples. The results of this search strategy were then internally reviewed and data were excluded based on the following criteria: (i) exclusion of duplicated samples, (ii) exclusion of studies that sorted single cell types (e.g. T cells or B cells) prior to gene expression profiling, (iii) exclusion of studies with inaccessible data. Other than that, no studies were excluded from our analysis. In total, the datasets contained samples from the following GSE Series: GSE63085, GSE32874, GSE58335, GSE86884, GSE63703, GSE63646, GSE63816, GSE72790, GSE81259, GSE85712, GSE45735, GSE64655, GSE87186, GSE49642, GSE52656, GSE62190, GSE66917, GSE67039, GSE61162, GSE67184, GSE49601, GSE78785, GSE79970. All raw data files were downloaded from GEO. Transcript abundances were calculated using kallisto version 0.43.0 and all data was normalized with the R package DESeq2 (R version R-3.2.4, DESeq2 version 1.12.4) with standard parameters. Genome build hg38 was used for read alignment. No filtering of low-expressed genes was performed.

Dataset Information

Descriptive statistics of dataset from the meta-analysis and meta-regression analysis on prognostic significance of pre-treatment systemic hemato-immunological indices of cervical cancer patients.

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