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Field cancerization profile-based prognosis signatures lead to more robust risk evaluation in hepatocellular carcinoma.

ABSTRACT: The development of reliable biomarkers has been an urgent issue as well as a hot spot of research on the diagnosis, treatment, and prognostic evaluation of hepatocellular carcinoma (HCC). Here, we established and validated two field cancerization profile-based prognostic signatures (gene expression score [GES] and immune score [IS]) for HCC. Our study confirmed that field cancerization profile-based models outperform conventional models on risk evaluation, offering insights for further studies on prognostic model construction. The nomogram constructed by combining GES, IS, and TNM stage was proved effective in improving the individualized prediction of the overall risk of patients. Distinct peritumoral characteristics were observed in several immune cells (e.g., CD8 T cells and dendritic cells), which might explain the diversified prognosis and clinical benefit of immunotherapy. Moreover, a series of drug targets, prognosis-associated genes, and pathways were identified, which may contribute to molecular mechanism studies as well as therapeutic target development of HCC.

SUBMITTER: Huang L

PROVIDER: S-EPMC8800113 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Field cancerization profile-based prognosis signatures lead to more robust risk evaluation in hepatocellular carcinoma.

Huang Lu L Songyang Zhou Z Dai Zhiming Z Xiong Yuanyan Y

iScience 20220107 2

The development of reliable biomarkers has been an urgent issue as well as a hot spot of research on the diagnosis, treatment, and prognostic evaluation of hepatocellular carcinoma (HCC). Here, we established and validated two field cancerization profile-based prognostic signatures (gene expression score [GES] and immune score [IS]) for HCC. Our study confirmed that field cancerization profile-based models outperform conventional models on risk evaluation, offering insights for further studies o ...[more]

PMID: 35118360

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Project description:Laryngeal carcinoma is still a worldwide burden that has shown no significant improvement during the last few decades regarding definitive treatment strategies. The lack of suitable biomarkers for personalized treatment protocols and delineating field cancerization prevents further progress in clinical outcomes. In the light of this perspective, MicroRNAs could be promising biomarkers both in terms of diagnostic and prognostic value. The aim of this prospective study is to find strong prognostic microRNA biomarkers for advanced laryngeal carcinoma and molecular signatures of field cancerization. Sixty patients were enrolled and four samples were collected from each patient: tumor surface and depth, peritumor normal mucosa, and control distant laryngeal mucosa. Initially, a global microRNA profile was conducted in twelve patients from the whole cohort and subsequently, we validated a selected group of 12 microRNAs with RT-qPCR. The follow-up period was 24 months (SD ± 13 months). Microarray expression profile revealed 59 dysregulated microRNAs. The validated expression levels of miR-93-5p (χ2(2) = 4.68, log-rank p = 0.03), miR-144-3p (χ2(2) = 4.53, log-rank p = 0.03) and miR-210-3p (χ2(2) = 4.53, log-rank p = 0.03) in tumor samples exhibited strong association with recurrence-free survival as higher expression levels of these genes predict worse outcome. Tumor suppressor genes miR-144-3p (mean rank 1.58 vs 2.14 vs 2.29, p = 0.000) and miR-145-5p (mean rank 1.57 vs 2.15 vs 2.28, p = 0.000) were significantly dysregulated in peritumor mucosa with a pattern of expression consistent with paired tumor samples thus revealing a signature of field cancerization in laryngeal carcinoma. Additionally, miR-1260b, miR-21-3p, miR-31-3p and miR-31-5p were strongly associated with tumor grade. Our study reports the first global microRNA profile specifically in advanced laryngeal carcinoma that includes survival analysis and investigates the molecular signature of field cancerization. We report two strong biomarkers of field cancerization and three predictors for recurrence in advance stage laryngeal cancer.

Project description:BackgroundThe prognosis of hepatocellular carcinoma (HCC) patients remains poor. Identifying prognostic markers to stratify HCC patients might help to improve their outcomes.MethodsSix gene expression profiles (GSE121248, GSE84402, GSE65372, GSE51401, GSE45267 and GSE14520) were obtained for differentially expressed genes (DEGs) analysis between HCC tissues and non-tumor tissues. To identify the prognostic genes and establish risk score model, univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed based on the integrated DEGs by robust rank aggregation method. Then Kaplan-Meier and time-dependent receiver operating characteristic (ROC) curves were generated to validate the prognostic performance of risk score in training datasets and validation datasets. Multivariable Cox regression analysis was used to identify independent prognostic factors in liver cancer. A prognostic nomogram was constructed based on The Cancer Genome Atlas (TCGA) dataset. Finally, the correlation between DNA methylation and prognosis-related genes was analyzed.ResultsA twelve-gene signature including SPP1, KIF20A, HMMR, TPX2, LAPTM4B, TTK, MAGEA6, ANX10, LECT2, CYP2C9, RDH16 and LCAT was identified, and risk score was calculated by corresponding coefficients. The risk score model showed a strong diagnosis performance to distinguish HCC from normal samples. The HCC patients were stratified into high-risk and low-risk group based on the cutoff value of risk score. The Kaplan-Meier survival curves revealed significantly favorable overall survival in groups with lower risk score (P < 0.0001). Time-dependent ROC analysis showed well prognostic performance of the twelve-gene signature, which was comparable or superior to AJCC stage at predicting 1-, 3-, and 5-year overall survival. In addition, the twelve-gene signature was independent with other clinical factors and performed better in predicting overall survival after combining with age and AJCC stage by nomogram. Moreover, most of the prognostic twelve genes were negatively correlated with DNA methylation in HCC tissues, which SPP1 and LCAT were identified as the DNA methylation-driven genes.ConclusionsWe identified a twelve-gene signature as a robust marker with great potential for clinical application in risk stratification and overall survival prediction in HCC patients.

Project description:BackgroundLung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer. The distinctive genetic and epigenetic modifications in tumors and paired non-malignant samples, such as adjacent peri-tumor and tumor-distant normal lung tissues, have not been adequately studied.MethodsWe recruited 57 patients with resectable stage I-III LUAD and collected matched samples of the primary tumor, peri-tumoral tissues, and tumor-distant normal lung tissue. We performed bisulfite sequencing using a custom methylation panel to profile DNA methylation levels and obtained somatic variation landscape through targeted next-generation sequencing (NGS). We attempted to identify differential methylation blocks (DMBs) between the tumor, peri-tumor, and normal tissues.ResultsWe analyzed the DNA methylation patterns of matched tumor, peri-tumor, and normal lung tissue samples from 57 LUAD patients. No significantly different methylation blocks were found between peri-tumoral and normal tissues, while they both exhibited distinct methylation profiles compared to tumor tissues. A total of 1329 tumor-specific DMBs, which are potentially associated with aberrant gene expression in LUAD, were identified. Utilizing a consensus clustering algorithm, we classified the tumor samples into two subgroups (C1 and C2) based on distinct methylation profiles, independent of the patient's sex, tumor stage, smoking history, and tumor cell fraction. The C2 subgroup exhibited a higher malignancy density ratio (MD ratio), suggesting a more pronounced degree of field cancerization, while the C1 subgroup was characterized by a higher frequency of EGFR mutations. The DMBs between the two subgroups were enriched in the calcium signaling pathway. Notably, P2RX2 shows significant hypermethylation in the C2 subgroup, and its low expression in the external The Cancer Genome Atlas (TCGA) cohort may correlate with reduced overall survival in LUAD patients.ConclusionOur findings revealed distinct methylation patterns between tumor and pre-malignant samples, such as peri-tumor and normal tissues. Moreover, our study suggests that distinct clustering based on DNA methylation may indicate different prognoses in LUAD patients.

Project description:Background: Evidence has shown that lactate, an immune signaling molecule, is associated with hepatocellular carcinoma (HCC) progression and immune suppression. Therefore, identifying lactate metabolism-related molecules is a promising therapeutic strategy to inhibit the development of HCC and overcome chemotherapy resistance. Long noncoding RNAs (lncRNAs) are related to tumorigenesis and metastasis. Hence, verifying the molecular subtypes of lncRNAs related to lactate metabolism will play a critical role in managing HCC. Methods: Based on HCC data in The Cancer Genome Atlas (TCGA), lactate metabolic pathway-related genes were enriched by gene collection and enrichment analysis (GSEA). Lactate metabolism-related lncRNAs (LM_lncRNAs) were identified by correlation analysis, HCC molecular subtypes were determined using nonnegative matrix factorization (NMF) clustering, and the response of the three subtypes to chemotherapeutics was further evaluated using the Genomic Tumor Sensitive Cell Line (GDSC) dataset. LM_lncRNAs were examined via Lasso-Cox regression analysis to determine prognosis for patients. A Nomagram plot was used to predict patient survival time. Results: Three molecular subtypes of HCC were identified. The survival rate of patients with C1 subtype was higher than that of those with C2 and C3. Additionally, patients with C3 subtype have higher levels of immune cell infiltration and high expression of genes related to immune checkpoints. The GDSC results indicated that patients with C3 subtypes were more sensitive to chemotherapy drugs such as sorafenib and sunitinib. The prognostic risk assessment model consisted of six risk factors (AC034229.4, AC131009.1, MYOSLID, AC008667.1, AC012073.1, AC068025.1) and two protective factors (LINC00402 and AC103858.1). Based on Kaplan-Meier analysis, low-risk HCC patients had a high survival rate, and the receiver operating characteristic curve (ROC), calibration curve, and C-index confirmed good prediction ability. Conclusion: In this study, the molecular subtyping method and prediction model of lactate metabolism-related lncRNAs (LM_lncRNAs) were constructed for the prognosis of HCC patients. This work demonstrated the potential targets of LM_lncRNAs and provided a novel perspective and therapeutic paradigm for future clinical translation.

Project description:BackgroundLiver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in.MethodsBig data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values.ResultsA list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, 'pathways in cancer,' was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982-0.998], P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences.DiscussionThe study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, 'pathways in cancer.' The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention.

Project description:BackgroundThe unbalance of mitophagy was closely related to hepatocellular carcinoma (HCC) progression. At present, it has not been uncovered about the influence of mitophagy genes on HCC prognosis and their potential pathogenesis.Materials and methodsThe expression and clinical information of HCC in TCGA cohort were used to identify mitophagy differentially expressed genes (MDEGs) with prognostic value. The prognostic model of mitophagy genes was built and externally validated by LASSO regression in TCGA cohort and ICGC cohort, respectively. The function of the prognostic signature and its association with immune cell infiltration were explored. The profile of MDEGs was validated with 39 pairs HCC and paracarcinoma tissues by quantitative reverse transcription-PCR (qRT-PCR).ResultsA total of 18 mitophagy genes that were upregulated and contributed to poor prognosis in HCC were identified. These genes could interact with each other. The correlation analysis showed that there was positively correlation among mitophagy genes. According to optimal λ value, 8 mitophagy gene signatures were involved in prognostic model. Based on median risk scores, HCC patients were divided into high-risk group and low-risk group. Compared with the low-risk group, the high-risk group has worse overall survival in TCGA cohort and ICGC cohort. The univariate and multivariate Cox regression analysis suggested that risk score was an independent prognostic factor of HCC patients. Time-dependent ROC curve was used to identify and validate good predicting performance of the prognostic model. Enrichment analysis showed that risk differentially expressed genes were enriched in various metabolism and cell division processes. The immune cell infiltration score and immune function were significantly different in two groups. qRT-PCR validation result showed that QSTM1, CSNK2B, PGAM5, and ATG5 were upregulated.ConclusionMitophagy genes could influence HCC progression through regulating the metabolism and immune functions and could be used to predict prognosis and considered as potential prognostic biomarker and precise therapeutic target of HCC.

Dataset Information

Field cancerization profile-based prognosis signatures lead to more robust risk evaluation in hepatocellular carcinoma.

Publications

Field cancerization profile-based prognosis signatures lead to more robust risk evaluation in hepatocellular carcinoma.

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