A novel DNA methylation-driver gene signature for long-term survival prediction of hepatitis-positive hepatocellular carcinoma patients

A novel subtype based on driver methylation-transcription in lung adenocarcinoma

AIMS

To identify driver methylation genes and a novel subtype of lung adenocarcinoma (LUAD) by multi-omics and elucidate its molecular features and clinical significance.

METHODS

We collected LUAD patients from public databases, and identified driver methylation genes (DMGs) by MethSig and MethylMix algrothms. And novel driver methylation multi-omics subtypes were identified by similarity network fusion (SNF). Furthermore, the prognosis, tumor microenvironment (TME), molecular features and therapy efficiency among subtypes were comprehensively evaluated.

RESULTS

147 overlapped driver methylation were identified and validated. By integrating the mRNA expression and methylation of DMGs using SNF, four distinct patterns, termed as S1-S4, were characterized by differences in prognosis, biological features, and TME. The S2 subtype showed unfavorable prognosis. By comparing the characteristics of the DMGs subtypes with the traditional subtypes, S3 was concentrated in proximal-inflammatory (PI) subtype, and S4 was consisted of terminal respiratory unit (TRU) subtype and PI subtype. By analyzing TME and epithelial mesenchymal transition (EMT) features, increased immune infiltration and higher expression of immune checkpoint genes were found in S3 and S4. While S4 showed higher EMT score and expression of EMT associated genes, indicating S4 may not be as immunosensitive as the S3. Additionally, S3 had lower TIDE and higher IPS score, indicating its increased sensitivity to immunotherapy.

CONCLUSION

The driver methylation-related subtypes of LUAD demonstrate prognostic predictive ability that could help inform treatment response and provide complementary information to the existing subtypes.

Machine learning integrations for development of a T-cell-tolerance derived signature to improve the clinical outcomes and precision treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is characterized by high rates of recurrence and metastasis and poor prognosis. A recently discovered concept of T cell tolerance (TCT) has become an entirely new target of cancer immunotherapy. Unfortunately, the effect of TCT on the outcomes of HCC has not been explored. In this study, 7 public datasets and one external clinical cohort, including 1716 HCC patients were explored. Through WGCNA analysis and differential analysis, we explored the key TCT-related modulates. A total of 95 machine learning integrations across all validation cohorts were compared and the optimal method with the highest average C-index value was selected to construct the TCT derived signature (TCTS). In all independent clinical cohorts, TCTS showed accurate prediction of the prognosis, and was significantly correlated with clinical indicators and molecular features. Compared with 77 published gene signatures, the TCTS exhibited superior predictive performance. In the external clinical cohort, a novel nomogram (comprising TNM stage, Hepatitis B, Vascular invasion, Perineural invasion, AFP and TCTS) was constructed to test the clinical performance of TCTS. The results showed that the high TCTS scoring group showed dismal prognosis, improved sensitivity to oxaliplatin and good response to anti-PD-1/PD-L1 immunotherapy. Moreover, the low TCTS score group had few genomic alterations, low immune activation and low PD-1/PD-L1 expression levels. In conclusion, TCTS is an ideal biomarker for predicting the clinical outcomes and improving precision treatment of HCC.

A novel DNA methylation-driver gene signature for long-term survival prediction of hepatitis-positive hepatocellular carcinoma patients

BACKGROUND

Abnormal DNA methylation is one of the most general epigenetic modifications in hepatocellular carcinoma (HCC). Recent research showed that DNA methylation was a prognostic indicator of all-cause HCC and nonviral HCC. However, whether DNA methylation-driver genes could be used for predicting survival, the probability of hepatitis-positive HCC remains unclear.

METHODS

In this study, DNA methylation-driver genes (MDGs) were screened by a joint analysis of methylome and transcriptome data of 142 hepatitis-positive HCC patients. Subsequently, a prognostic risk score and nomogram were constructed. Finally, correlation analyses between the risk score and signaling pathways and immunity were conducted by GSVA and CIBERSORT.

RESULTS

Through random forest screening and Cox progression analysis, 10 prognostic methylation-driver genes (AC008271.1, C11orf53, CASP8, F2RL2, GBP5, LUCAT1, RP11-114B7.6, RP11-149I23.3, RP11-383 J24.1, and SLC35G2) were screened out. As a result, a prognostic risk score signature was constructed. The independent value of the risk score for prognosis prediction were addressed in the TCGA-HCC and the China-HCC cohorts. Next, clinicopathological features were analyzed and HBV status and histological grade were screened to construct a nomogram together with the risk score. The prognostic efficiency of the nomogram was validated by the calibration curves and the concordance index (C index: 0.829, 95% confidence interval: 0.794-0.864), while its clinical application ability was confirmed by decision curve analysis (DCA). At last, the relationship between the risk score and signaling pathways, as well as the correlations between immune cells were elucidated preliminary.

CONCLUSIONS

Taken together, our study explored a novel DNA methylation-driver gene risk score signature and an efficient nomogram for long-term survival prediction of hepatitis-positive HCC patients.