Dataset Information

Tissue-infiltrating lymphocytes signature predicts survival in patients with early/intermediate stage hepatocellular carcinoma.

ABSTRACT:

Background

Intratumoral immune infiltrates have manifested a robust prognostic signature in patients with hepatocellular carcinoma (HCC). We hypothesized that a novel tissue-related immune signature (TRIS) could improve the prediction of postoperative survival for patients diagnosed with early/intermediate HCC.

Methods

Twenty-eight immune features were immunohistochemically examined on 352 HCC specimens. The LASSO Cox regression model was used to construct a five-feature-based TRIS. The univariate and multivariate Cox analyses were performed. Based on independent predictors, the immune-clinical prognostic index (ICPI) was established. Performance assessment was measured with C-index and compared with seven traditional staging systems. The independent validation cohort (n =?393) was included to validate the model.

Results

By using the LASSO method, the TRIS were constructed on the basis of five immune features, CD3_{intratumoral (T)}, CD27_T, CD68_{peritumoral (P)}, CD103_T, and PD1_T. Multivariate Cox analysis showed that the TRIS was an independent prognostic predictor. In the training cohort, ?-glutamyl transferase, tumor diameter, tumor differentiation, and TRIS were incorporated into the ICPI. The ICPI presented satisfactory discrimination ability, with C-index values of 0.691 and 0.686 in the training and validation cohorts, respectively. Compared with seven conventional staging systems (C-index, training cohort, 0.548-0.597; validation cohort, 0.519-0.610), the ICPI exhibited better performance for early/intermediate-stage HCCs. Further, the patients were categorized into three subgroups with X-tile software, and the stratified ICPI presented a superior corrected Akaike information criterion and homogeneity in both cohorts.

Conclusions

Our ICPI was a useful and reliable prognostic tool which may offer good individualized prediction capability for HCC patients with early/intermediate stage.

SUBMITTER: Tian MX

PROVIDER: S-EPMC6549297 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe dysregulation of autophagy and immunological processes has been linked to various pathophysiological conditions, including cancer. Most notably, their particular involvement in hepatocellular carcinoma (HCC) is becoming increasingly evident. This has led to the possibility of developing a prognostic signature based on immuno-autophagy-related (IAR) genes. Given that long non-coding RNAs (lncRNAs) also play a special role in HCC, a combined signature utilizing IAR genes and HCC-associated long noncoding RNAs (as IARlncRNA) may potentially help in the clinical scenario.MethodWe used Pearson correlation analysis, Kaplan-Meier survival curves, univariate and multivariate Cox regression, and ROC curves to generate and validate a prognostic immuno-autophagy-related long non-coding RNA (IARlncRNA) signature. The Chi-squared test was utilized to investigate the correlation between the obtained signature and the clinical characteristics. CIBERSORT algorithms and the Wilcoxon rank sum test were applied to investigate the correlation between signature and infiltrating immune cells. GO and KEGG analyses were performed to derived signature-dependent pathways.ResultsHerein, we build an IAR-lncRNA signature (as first in the literature) and demonstrate its prognostic ability in hepatocellular carcinoma. Primarily, we identified three IARlncRNAs (MIR210HG, AC099850.3 and CYTOR) as unfavorable prognostic determinants. The obtained signature predicted the high-risk HCC group with shorter overall survival, and was further associated with clinical characteristics such as tumor grade (t = 10.918, p = 0.001). Additionally, several infiltrating immune cells showed varied fractions between the low-risk group and the high-risk HCC groups in association with the obtained signature. In addition, pathways analysis described by the signature clearly distinguishes both risk groups in HCC.ConclusionsThe immuno-autophagy-related long non-coding RNA (IARlncRNA) signature we established exhibits a prognostic ability in hepatocellular carcinoma. To our knowledge, this is the first attempt in the literature to combine three determinants (immune, autophagy and LnRNAs), thus requiring molecular validation of this obtained signature in clinical samples.

Project description:Hepatocellular carcinoma (HCC) is one of the most common public health challenges, worldwide. Because of molecular complexity and tumor heterogeneity, there are no effective predictive models for prognosis of HCC. This underlines the unmet need for accurate prognostic models for HCC. Analysis of GSE14520 data from gene omnibus (GEO) database identified multiple differentially expressed mRNAs (DEMs) between HCC and normal tissues. After randomly stratifying the patients into the training and testing groups, we performed univariate, lasso, and multivariable Cox regression analyses to delineate the prognostic gene signature in training set. We then used Kaplan-Meier plot, time-dependent receiver operating characteristic (ROC), multivariable Cox regression analysis of clinical information, nomogram, and decision curve analysis (DCA) to evaluate the predictive and overall survival value of a novel five-gene signature (CNIH4, SOX4, SPP1, SORBS2, and CCL19) within and across sets, separately and combined. We also validated the prognostic value of the five-gene signature using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC), GSE54236 and International Cancer Genome Consortium (ICGC) sets. Multivariable Cox regression analysis revealed that the five-gene signature and tumor node metastasis (TNM) stage were independent prognostic factors for overall survival of HCC patients in GSE14520 and TCGA-LIHC. Combining TNM stage clinical pathological parameters and nomogram greatly improved the prognosis prediction of HCC. Further gene set enrichment analysis (GSEA) revealed enrichment of KEGG pathways related to cell cycle in the high-risk group and histidine metabolism in the low-risk group. Finally, all these five mRNAs are overexpressed between 12 pairs of HCC and adjacent normal tissues by quantitative real-time PCR validation. In brief, a five-gene prognostic signature and a nomogram were identified and constructed, respectively, and further validated for their HCC prognostic value. The five-gene risk score together with TNM stage models could aid in rationalizing customized therapies in HCC patients.