Identification and Validation of a Prognostic Model Based on Three MVI-Related Genes in Hepatocellular Carcinoma

Tang, Yongchang; Xu, Lei; Ren, Yupeng; Li, Yuxuan; Yuan, Feng; Cao, Mingbo; Zhang, Yong; Deng, Meihai; Yao, Zhicheng

doi:10.7150/ijbs.66536

Cited by 32 publications

(24 citation statements)

References 83 publications

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“…However, there is still a bottleneck in the treatment of HCC. Despite the use of various therapies to treat HCC, the prognosis of HCC remains poor due to the high frequency of metastasis and recurrence, with a poor ve-year survival rate [17][18][19] . Therefore, it is necessary to nd biomarkers that can diagnose HCC as early as possible and to study more molecular pathways and possible resistance mechanisms.…”

Section: Discussionmentioning

confidence: 99%

CircRNA-mTOR Promotes Hepatocellular Carcinoma Progression and Lenvatinib Resistance via PSIP1/c-Myc Axis Nuclear Translocation and Partially through Increasing EGFR Expression

Tang

Yuan

Cao

et al. 2023

Preprint

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Background Hepatocellular Carcinoma (HCC) is one of the major malignant tumors threatening human health. Lenvatinib resistance seriously restricts the efficacy of HCC, but the specific mechanism is not clear. Circular RNA (circRNA) plays an important role in the regulation of tumor drug resistance. Methods Key circRNA was screened by bioinformatics methods, and further identified by relevant validation experiments and HCC tissue samples. And, circRNA was evaluated as a diagnostic and prognostic marker for HCC progression at the clinical level. After then, through in vivo and in vitro experiments, the specific mechanism of the circRNA on the progression of HCC and lenvatinib resistance was explored at the molecular level. Results circRNA_0009792 (circRNA-mTOR) was highly expressed in HCC and is closely related to the prognosis of patients, which has good diagnostic value and clinical significance. In vivo and in vitro experiments showed that circRNA-mTOR could promote the progression of hepatocellular carcinoma and promote lenvatinib resistance by improving the stemness of HCC cells. Mechanismly, circRNA-mTOR could affect RNA-binding protein (PSIP1) nuclear translocation by specifically binding to it, and then which enhanced the stemness of HCC cells through PSIP1/c-Myc axis, hence promoting the progression of HCC and lenvatinib resistance. And furthermore, circRNA-mTOR at least partially induce lenvatinib resistance by increasing the expression of EGFR in HCC. Conclusions In conclusion, this study suggests that circRNA-mTOR can affect PSIP1/c-myc axis nuclear translocation, to make progress of HCC and the maintenance of steness of liver cancer cell to aggravate lenvatinib resistance, And partially increased EGFR over-expression to making chemo-resistance worse. CircRNA-mTOR has the potential to be a biomarker for the diagnosis and prognosis of HCC. This study provides a certain experimental basis for the targeted drug therapy of HCC, and puts forward new ideas, new insights and new methods in understanding the occurrence and development of HCC, and it is of great significance to seek new markers and targets for the diagnosis and treatment of HCC and reduce drug resistance.

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Section: Discussionmentioning

confidence: 99%

CircRNA-mTOR Promotes Hepatocellular Carcinoma Progression and Lenvatinib Resistance via PSIP1/c-Myc Axis Nuclear Translocation and Partially through Increasing EGFR Expression

Tang

Yuan

Cao

et al. 2023

Preprint

Self Cite

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“…MVI has been identified as an important histological parameter and limitational factor for OS and RFS after liver resection and transplantation before [ 33 , 34 ]. Although examination of suitable preoperative MVI prediction models is becoming more popular in recent years, postoperative histopathological examination currently seems to be the only valid option for proving MVI in HCC at current state [ 35 , 36 ]. Further we could identify the number of nodules as independent predictor for OS as also commonly known risk factor for reduced OS [ 37 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

Time to surgery is not an oncological risk factor in HCC patients undergoing liver resection

Otto

Wang

Mantas

et al. 2023

Langenbecks Arch Surg

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Purpose Given limitations of the health care systems in case of unforeseeable events, e.g., the COVID pandemic as well as trends in prehabilitation, time from diagnosis to surgery (time to surgery, (TTS)) has become a research issue in malignancies. Thus, we investigated whether TTS is associated with oncological outcome in HCC patients undergoing surgery. Methods A monocentric cohort of 217 patients undergoing liver resection for HCC between 2009 and 2021 was analyzed. Individuals were grouped according to TTS and compared regarding clinical characteristics. Overall survival (OS) and recurrence-free survival (RFS) was compared using Kaplan-Meier analysis and investigated by univariate and multivariable Cox regressions. Results TTS was not associated with OS (p=0.126) or RFS (p=0.761) of the study cohort in univariate analysis. In multivariable analysis age (p=0.028), ASA (p=0.027), INR (0.016), number of HCC nodules (p=0.026), microvascular invasion (MVI; p<0.001), and postoperative complications (p<0.001) were associated with OS and INR (p=0.005), and number of HCC nodules (p<0.001) and MVI (p<0.001) were associated with RFS. A comparative analysis of TTS subgroups was conducted (group 1, ≤30 days, n=55; group 2, 31–60 days, n=79; group 3, 61–90 days, n=45; group 4, >90 days, n=38). Here, the median OS were 62, 41, 38, and 40 months (p=0.602 log rank) and median RFS were 21, 26, 26, and 25 months (p=0.994 log rank). No statistical difference regarding oncological risk factors were observed between these groups. Conclusion TTS is not associated with earlier tumor recurrence or reduced overall survival in surgically treated HCC patients.

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“…Advanced sequencing technologies have enabled large datasets, such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which have driven the search for novel and relevant cancer prognostic biomarkers (Wadowska et al, 2020, Holdenrieder, 2016. These studies demonstrated the huge potential of using public database resources to develop prognostic risk models, and there has been a growing body of research based on these two databases in recent years (Zhang et al, 2021, Wang et al, 2021, Tang et al, 2022. Ongoing assessment of these markers in a clinical setting may allow researchers to detect and analyze differentially expressed genes (DEGs) in various cancer types, which may help identify malignancy-associated gene signatures and allow clinicians to better manage cancer patients and design appropriate therapies (Ahluwalia et al, 2021, Huang et al, 2020.…”

Section: Introductionmentioning

confidence: 99%

Construction of a prognostic model for advanced non-small cell lung cancer using combined analysis of public databases

Yin

Wang

et al. 2023

Preprint

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Non-small cell lung cancer (NSCLC) has become a global health problem owing to its high incidence and poor treatment efficacy. This study aimed to establish a prognostic model for advanced NSCLC using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to evaluate the prognosis. Based on TCGA database, we obtained a dataset of 191 advanced NSCLC and 108 normal tissue samples after screening. We screened the differential expression between advanced NSCLC and normal tissues using the "limma" software package in R and identified differential expression genes. Weighted gene co-expression network analysis of differential genes was performed and the Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes were used for pathway enrichment analysis of the key genes in the core modules. Finally, univariate Cox regression analysis was used to determine genes associated with prognosis, and multivariate Cox regression analysis was used to construct a prognostic model. We used the receiver operating characteristic curve (ROC), Kaplan-Meier curve, and multivariate Cox regression analysis to test the prognostic value of the model. The GEO database (GSE68465) was used to screen a cohort of clinical and gene expression data from 69 patients to further validate the prognostic value of the model. We constructed a five-gene (CKS1B, SLC7A5, SPATS2, SPDL1, and CHEK2) prognostic model to predict overall survival in patients with advanced NSCLC. Kaplan-Meier analysis, area under the ROC curve (AUC), and C-index had good predictive power for gene signatures in TCGA dataset (P<0.0001; C-index 0.710; 95% confidence interval (0.681–0.738); AUC at 1 year, 2 years, and 3 years were 0.76, 0.75, and 0.73, respectively). The risk score was an independent risk factor for survival in patients with advanced NSCLC, after adjusting for other routine clinical parameters. Finally, we constructed a nomogram that integrated gene signatures and clinicopathological parameters. The risk score model constructed from the five survival-related genes can be used as prognostic biomarkers. The nomogram was closely related to the overall survival of the patients. This result may help future individualized treatment and clinical decision making.

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Identification and Validation of a Prognostic Model Based on Three MVI-Related Genes in Hepatocellular Carcinoma

Cited by 32 publications

References 83 publications

CircRNA-mTOR Promotes Hepatocellular Carcinoma Progression and Lenvatinib Resistance via PSIP1/c-Myc Axis Nuclear Translocation and Partially through Increasing EGFR Expression

CircRNA-mTOR Promotes Hepatocellular Carcinoma Progression and Lenvatinib Resistance via PSIP1/c-Myc Axis Nuclear Translocation and Partially through Increasing EGFR Expression

Time to surgery is not an oncological risk factor in HCC patients undergoing liver resection

Construction of a prognostic model for advanced non-small cell lung cancer using combined analysis of public databases

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