A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma

Disulfidptosis-related signatures for prognostic and immunotherapy reactivity evaluation in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and a nonnegligible health concern on a worldwide scale. Disulfidptosis is a novel mode of cell death, which is mainly caused by the collapse of the actin skeleton. Although many studies have demonstrated that various types of cell death are associated with cancer treatment, the relationship between disulfidptosis and HCC has not been elucidated.

METHODS

Here, we mainly applied bioinformatics methods to construct a disulfidptosis related risk model in HCC patients. Specifically, transcriptome data and clinical information were downloaded from the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) database. A total of 45 co-expressed genes were extracted between the disulfidptosis-related genes (DRGs) and the differential expression genes (DEGs) of liver hepatocellular carcinoma (LIHC) in the TCGA database. The LIHC cohort was divided into two subgroups with different prognosis by k-mean consensus clustering and functional enrichment analysis was performed. Subsequently, three hub genes (CDCA8, SPP2 and RDH16) were screened by Cox regression and LASSO regression analysis. In addition, a risk signature was constructed and the HCC cohort was divided into high risk score and low risk score subgroups to compare the prognosis, clinical features and immune landscape between the two subgroups. Finally, the prognostic model of independent risk factors was constructed and verified.

CONCLUSIONS

High DRGs-related risk score in HCC individuals predict poor prognosis and are associated with poor immunotherapy response, which indicates that risk score assessment model can be utilized to guide clinical treatment strategy.

Nomogram Based on Platelet-Albumin-Bilirubin for Predicting Tumor Recurrence After Surgery in Alpha-Fetoprotein-Negative Hepatocellular Carcinoma Patients

Purpose

In this study, we developed a nomogram based on the platelet-albumin-bilirubin (PALBI) score to predict recurrence-free survival (RFS) after curative resection in alpha-fetoprotein (AFP)-negative (≤20 ng/mL) hepatocellular carcinoma (HCC) patients.

Patients and Methods

A total of 194 pathologically confirmed AFP-negative HCC patients were retrospectively analyzed. Univariate and multivariate Cox regression analyses were performed to screen the independent risk factors associated with RFS, and a nomogram prediction model for RFS was established according to the independent risk factors. The receiver operating characteristic (ROC) curve and the C-index were used to evaluate the accuracy and the efficacy of the model prediction. The correction curve was used to assess the calibration of the prediction model, and decision curve analysis was performed to evaluate the clinical application value of the prediction model.

Results

PALBI score, MVI, and tumor size were independent risk factors for postoperative tumor recurrence (P < 0.05). A nomogram prediction model based on the independent predictive factors was developed to predict RFS, and it achieved a good C-index of 0.704 with an area under the ROC curve of 0.661 and the sensitivity was 73.2%. Patients with AFP-negative HCC could be divided into the high-risk group or the low-risk group by the risk score calculated by the nomogram, and there was a significant difference in RFS between the two groups (P < 0.05). Decision curve analysis (DCA) showed that the nomogram increased the net benefit in predicting the recurrence of AFP-negative HCC and exhibited a wider range of threshold probabilities than the independent risk factors (PALBI score, MVI, and tumor size) by risk stratification.

Conclusion

The nomogram based on the PALBI score can predict RFS after curative resection in AFP-negative HCC patients and can help clinicians to screen out high-risk patients for early intervention.

Identification and construction of a 13-gene risk model for prognosis prediction in hepatocellular carcinoma patients

We attempted to screen out the feature genes associated with the prognosis of hepatocellular carcinoma (HCC) patients through bioinformatics methods, to generate a risk model to predict the survival rate of patients. Gene expression information of HCC was accessed from GEO database, and differentially expressed genes (DEGs) were obtained through the joint analysis of multi-chip. Functional and pathway enrichment analyses of DEGs indicated that the enrichment was mainly displayed in biological processes such as nuclear division. Based on TCGA-LIHC data set, univariate, LASSO, and multivariate Cox regression analyses were conducted on the DEGs. Then, 13 feature genes were screened for the risk model. Also, the hub genes were examined in our collected clinical samples and GEPIA database. The performance of the risk model was validated by Kaplan-Meier survival analysis and receiver operation characteristic (ROC) curves. While its universality was verified in GSE76427 and ICGC (LIRI-JP) validation cohorts. Besides, through combining patients' clinical features (age, gender, T staging, and stage) and risk scores, univariate and multivariate Cox regression analyses revealed that the risk score was an effective independent prognostic factor. Finally, a nomogram was implemented for 3-year and 5-year overall survival prediction of patients. Our findings aid precision prediction for prognosis of HCC patients.

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

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.

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Two field cancerization feature-based prognostic signatures for HCC were developed

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Joint nomogram is effective in improving individualized risk prediction

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Different peritumor signatures were observed in several immune cells

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Several peritumoral drug targets, prognostic genes, and pathways were identified

Development and validation of a nomogram to predict postoperative pulmonary complications following thoracoscopic surgery

Background

Postoperative pulmonary complications (PPCs) after thoracoscopic surgery are common. This retrospective study aimed to develop a nomogram to predict PPCs in thoracoscopic surgery.

Methods

A total of 905 patients who underwent thoracoscopy were randomly enrolled and divided into a training cohort and a validation cohort at 80%:20%. The training cohort was used to develop a nomogram model, and the validation cohort was used to validate the model. Univariate and multivariable logistic regression were applied to screen risk factors for PPCs, and the nomogram was incorporated in the training cohort. The discriminative ability and calibration of the nomogram for predicting PPCs were assessed using C-indices and calibration plots.

Results

Among the patients, 207 (22.87%) presented PPCs, including 166 cases in the training cohort and 41 cases in the validation cohort. Using backward stepwise selection of clinically important variables with the Akaike information criterion (AIC) in the training cohort, the following seven variables were incorporated for predicting PPCs: American Society of Anesthesiologists (ASA) grade III/IV, operation time longer than 180 min, one-lung ventilation time longer than 60 min, and history of stroke, heart disease, chronic obstructive pulmonary disease (COPD) and smoking. With incorporation of these factors, the nomogram achieved good C-indices of 0.894 (95% confidence interval (CI) [0.866–0.921]) and 0.868 (95% CI [0.811–0.925]) in the training and validation cohorts, respectively, with well-fitted calibration curves.

Conclusion

The nomogram offers good predictive performance for PPCs after thoracoscopic surgery. This model may help distinguish the risk of PPCs and make reasonable treatment choices.

Five gene signatures were identified in the prediction of overall survival in resectable pancreatic cancer

Background

Although genes have been previously detected in pancreatic cancer (PC), aberrant genes that play roles in resectable pancreatic cancer should be further assessed.

Methods

Messenger RNA samples and clinicopathological data corrected with PC were downloaded from The Cancer Genome Atlas (TCGA). Resectable PC patients were randomly divided into a primary set and a validation set. Univariable Cox regression analysis, lasso-penalized Cox regression analysis, and multivariable Cox analysis were implemented to distinguish survival-related genes (SRGs). A risk score based on the SRGs was calculated by univariable Cox regression analysis. A genomic-clinical nomogram was established by integrating the risk score and clinicopathological data to predict overall survival (OS) in resectable PC.

Results

Five survival-related genes (AADAC, DEF8, HIST1H1C, MET, and CHFR) were significantly correlated with OS in resectable PC. The resectable PC patients, based on risk score, were sorted into a high-risk group that showed considerably unfavorable OS (p < 0.001) than the low-risk group, in both the primary set and the validation set. The concordance index (C-index) was calculated to evaluate the predictive performance of the nomogram were respectively in the primary set [0.696 (0.608–0.784)] and the validation set [0.682 (0.606–0.758)]. Additionally, gene set enrichment Analysis discovered several meaningful enriched pathways.

Conclusion

Our study identified five prognostic gene biomarkers for OS prediction and which facilitate postoperative molecular target therapy for the resectable PC, especially the nomic-clinical nomogram which may be used as an effective model for the postoperative OS evaluation and also an optimal therapeutic tool for the resectable PC.