Network-based prioritization of cancer biomarkers by phenotype-driven module detection and ranking

Development and validation of a copper-related gene prognostic signature in hepatocellular carcinoma

Introduction: Reliable biomarkers are in need to predict the prognosis of hepatocellular carcinoma (HCC). Whilst recent evidence has established the critical role of copper homeostasis in tumor growth and progression, no previous studies have dealt with the copper-related genes (CRGs) signature with prognostic potential in HCC. Methods: To develop and validate a CRGs prognostic signature for HCC, we retrospectively included 353 and 142 patients as the development and validation cohort, respectively. Copper-related Prognostic Signature (Copper-PSHC) was developed using differentially expressed CRGs with prognostic value. The hazard ratio (HR) and the area under the time-dependent receiver operating characteristic curve (AUC) during 3-year follow-up were utilized to evaluate the performance. Additionally, the Copper-PSHC was combined with age, sex, and cancer stage to construct a Copper-clinical-related Prognostic Signature (Copper-CPSHC), by multivariate Cox regression. We further explored the underlying mechanism of Copper-PSHC by analyzing the somatic mutation, functional enrichment, and tumor microenvironment. Potential drugs for the high-risk group were screened. Results: The Copper-PSHC was constructed with nine CRGs. Patients in the high-risk group demonstrated a significantly reduced overall survival (OS) (adjusted HR, 2.65 [95% CI, 1.83-3.84] and 3.30, [95% CI, 1.27-8.60] in the development and validation cohort, respectively). The Copper-PSHC achieved a 3-year AUC of 0.74 [95% CI, 0.67-0.82] and 0.71 [95% CI, 0.56-0.86] for OS in the development and validation cohort, respectively. Copper-CPSHC yield a 3-year AUC of 0.73 [95% CI, 0.66-0.80] and 0.72 [95% CI, 0.56-0.87] for OS in the development and validation cohort, respectively. Higher tumor mutation burden, downregulated metabolic processes, hypoxia status and infiltrated stroma cells were found for the high-risk group. Six small molecular drugs were screened for the treatment of the high-risk group. Conclusion: Copper-PSHC services as a promising tool to identify HCC with poor prognosis and to improve disease outcomes by providing potential clinical decision support in treatment.

Machine learning-based prognostic modeling of lysosome-related genes for predicting prognosis and immune status of patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide. Lysosomes are organelles that play an important role in cancer progression by breaking down biomolecules. However, the molecular mechanisms of lysosome-related genes in HCC are not fully understood.

Methods

We downloaded HCC datasets from TCGA and GEO as well as lysosome-related gene sets from AIMGO. After univariate Cox screening of the set of lysosome-associated genes differentially expressed in HCC and normal tissues, risk models were built by machine learning. Model effects were assessed using the concordance index (C-index), Kaplan-Meier (K-M) and receiver operating characteristic curves (ROC). Additionally, we explored the biological function and immune microenvironment between the high- and low-risk groups, and analyzed the response of the high- and low-risk groups to immunotherapy responsiveness and chemotherapeutic agents. Finally, we explored the function of a key gene (RAMP3) at the cellular level.

Results

Univariate Cox yielded 46 differentially and prognostically significant lysosome-related genes, and risk models were constructed using eight genes (RAMP3, GPLD1, FABP5, CD68, CSPG4, SORT1, CSPG5, CSF3R) derived from machine learning. The risk model was a better predictor of clinical outcomes, with the higher risk group having worse clinical outcomes. There were significant differences in biological function, immune microenvironment, and responsiveness to immunotherapy and drug sensitivity between the high and low-risk groups. Finally, we found that RAMP3 inhibited the proliferation, migration, and invasion of HCC cells and correlated with the sensitivity of HCC cells to Idarubicin.

Conclusion

Lysosome-associated gene risk models built by machine learning can effectively predict patient prognosis and offer new prospects for chemotherapy and immunotherapy in HCC. In addition, cellular-level experiments suggest that RAMP3 may be a new target for the treatment of HCC.

C-Biomarker.net: A Cytoscape app for the identification of cancer biomarker genes from cores of large biomolecular networks

Although there have been many studies revealing that biomarker genes for early cancer detection can be found in biomolecular networks, no proper tool exists to discover the cancer biomarker genes from various biomolecular networks. Accordingly, we developed a novel Cytoscape app called C-Biomarker.net, which can identify cancer biomarker genes from cores of various biomolecular networks. Derived from recent research, we designed and implemented the software based on parallel algorithms proposed in this study for working on high-performance computing devices. We tested our software on various network sizes and found the suitable size for each running mode on CPU or GPU. Interestingly, using the software for 17 cancer signaling pathways, we found that on average 70.59% of the top three nodes residing at the innermost core of each pathway are biomarker genes of the cancer respectively to the pathway. Similarly, by the software, we also found 100% of the top ten nodes at both cores of Human Gene Regulatory (HGR) network and Human Protein-Protein Interaction (HPPI) network are multi-cancer biomarkers. These case studies are reliable evidence for performance of cancer biomarker prediction function in the software. Through the case studies, we also suggest that true cores of directed complex networks should be identified by the algorithm of R-core rather than K-core as usual. Finally, we compared the prediction result of our software with those of other researchers and confirmed that our prediction method outperforms the other methods. Taken together, C-Biomarker.net is a reliable tool that efficiently detects biomarker nodes from cores of various large biomolecular networks. The software is available at https://github.com/trantd/C-Biomarker.net.

Identifying the critical states and dynamic network biomarkers of cancers based on network entropy

Background

There are sudden deterioration phenomena during the progression of many complex diseases, including most cancers; that is, the biological system may go through a critical transition from one stable state (the normal state) to another (the disease state). It is of great importance to predict this critical transition or the so-called pre-disease state so that patients can receive appropriate and timely medical care. In practice, however, this critical transition is usually difficult to identify due to the high nonlinearity and complexity of biological systems.

Methods

In this study, we employed a model-free computational method, local network entropy (LNE), to identify the critical transition/pre-disease states of complex diseases. From a network perspective, this method effectively explores the key associations among biomolecules and captures their dynamic abnormalities.

Results

Based on LNE, the pre-disease states of ten cancers were successfully detected. Two types of new prognostic biomarkers, optimistic LNE (O-LNE) and pessimistic LNE (P-LNE) biomarkers, were identified, enabling identification of the pre-disease state and evaluation of prognosis. In addition, LNE helps to find “dark genes” with nondifferential gene expression but differential LNE values.

Conclusions

The proposed method effectively identified the critical transition states of complex diseases at the single-sample level. Our study not only identified the critical transition states of ten cancers but also provides two types of new prognostic biomarkers, O-LNE and P-LNE biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03445-0.