Six-gene prognostic signature for non-alcoholic fatty liver disease susceptibility using machine learning.

BACKGROUND: nonalcoholic fatty liver disease (NAFLD) is a common liver disease affecting the global population and its impact on human health will continue to increase. Genetic susceptibility is an important factor influencing its onset and progression, and there is a lack of reliable methods to predict the susceptibility of normal populations to NAFLD using appropriate genes. METHODS: RNA sequencing data relating to nonalcoholic fatty liver disease was analyzed using the "limma" package within the R software. Differentially expressed genes were obtained through preliminary intersection screening. Core genes were analyzed and obtained by establishing and comparing 4 machine learning models, then a prediction model for NAFLD was constructed. The effectiveness of the model was then evaluated, and its applicability and reliability verified. Finally, we conducted further gene correlation analysis, analysis of biological function and analysis of immune infiltration. RESULTS: By comparing 4 machine learning algorithms, we identified SVM as the optimal model, with the first 6 genes (CD247, S100A9, CSF3R, DIP2C, OXCT 2 and PRAMEF16) as predictive genes. The nomogram was found to have good reliability and effectiveness. Six genes' receiver operating characteristic curves (ROC) suggest an essential role in NAFLD pathogenesis, and they exhibit a high predictive value. Further analysis of immunology demonstrated that these 6 genes were closely connected to various immune cells and pathways. CONCLUSION: This study has successfully constructed an advanced and reliable prediction model based on 6 diagnostic gene markers to predict the susceptibility of normal populations to NAFLD, while also providing insights for potential targeted therapies.

Single cell and bulk transcriptome analysis identified oxidative stress response-related features of Hepatocellular Carcinoma.

Background: Hepatocellular Carcinoma (HCC) is a common lethal digestive system tumor. The oxidative stress mechanism is crucial in the HCC genesis and progression. Methods: Our study analyzed single-cell and bulk sequencing data to compare the microenvironment of non-tumor liver tissues and HCC tissues. Through these analyses, we aimed to investigate the effect of oxidative stress on cells in the HCC microenvironment and identify critical oxidative stress response-related genes that impact the survival of HCC patients. Results: Our results showed increased oxidative stress in HCC tissue compared to non-tumor tissue. Immune cells in the HCC microenvironment exhibited higher oxidative detoxification capacity, and oxidative stress-induced cell death of dendritic cells was attenuated. HCC cells demonstrated enhanced communication with immune cells through the MIF pathway in a highly oxidative hepatoma microenvironment. Meanwhile, using machine learning and Cox regression screening, we identified PRDX1 as a predictor of early occurrence and prognosis in patients with HCC. The expression level of PRDX1 in HCC was related to dysregulated ribosome biogenesis and positively correlated with the expression of immunological checkpoints (PDCD1LG2, CTLA4, TIGIT, LAIR1). High PRDX1 expression in HCC patients correlated with better sensitivity to immunotherapy agents such as sorafenib, IGF-1R inhibitor, and JAK inhibitor. Conclusion: In conclusion, our study unveiled variations in oxidative stress levels between non-tumor liver and HCC tissues. And we identified oxidative stress gene markers associated with hepatocarcinogenesis development, offering novel insights into the oxidative stress response mechanism in HCC.

Prognostic Role of Unfolded Protein Response-Related Genes in Hepatocellular Carcinoma.

AIMS: To reveal the prognostic role of unfolded protein response (UPR) -related genes in hepatocellular carcinoma (HCC). BACKGROUND: Hepatocellular carcinoma is a genetically heterogeneous tumor, and the prediction of its prognosis remains a challenge. Studies elucidating the molecular mechanisms of UPR have rapidly increased. However, the UPR molecular subtype characteristics of the related genes in HCC progression have yet to be thoroughly studied. OBJECTIVE: Conducting a comprehensive assessment of the prognostic signature of genes related to the UPR in patients with HCC can advance our understanding of the cellular processes contributing to the progression of HCC and offer innovative strategies in precise therapy. METHODS: Based on the gene expression profiles associated with UPR in HCC, we explored the molecular subtypes mediated by UPR-related genes and constructed a UPR-related genes signature that could precisely predict the prognosis for HCC. RESULTS: Using microarray data of HCC patients, differentially expressed UPR-related genes (DEGs) were discovered in malignancies and normal tissues. The HCC was classified into two molecular subtypes by the NMF algorithm based on DEGs modification of the UPR. Moreover, we developed a UPR-related model for predicting HCC patients' prognosis. The robustness of the UPR- related model was confirmed in external validation. Moreover, we analyzed immune responses in different risk groups. Analysis of immune functions revealed that Treg, Macrophages, aDCs, and MHC class-I were significantly up-regulated in high-risk HCC. At the same time, cytolytic activity and type I and II INF response were higher in a low-risk subgroup. CONCLUSION: This study identified two UPR molecular subtypes of HCC and developed a ten-gene HCC prognostic signature model (EXTL3, PPP2R5B, ZBTB17, CCT3, CCT4, CCT5, GRPEL2, HSP90AA1, PDRG1, and STC2), which can robustly forecast the progression of HCC.

Cellular senescence-related gene signature as a valuable predictor of prognosis in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a lethal tumor. Its prognosis prediction remains a challenge. Meanwhile, cellular senescence, one of the hallmarks of cancer, and its related prognostic genes signature can provide critical information for clinical decision-making. METHOD: Using bulk RNA sequencing and microarray data of HCC samples, we established a senescence score model via multi-machine learning algorithms to predict the prognosis of HCC. Single-cell and pseudo-time trajectory analyses were used to explore the hub genes of the senescence score model in HCC sample differentiation. RESULT: A machine learning model based on cellular senescence gene expression profiles was identified in predicting HCC prognosis. The feasibility and accuracy of the senescence score model were confirmed in external validation and comparison with other models. Moreover, we analyzed the immune response, immune checkpoints, and sensitivity to immunotherapy drugs of HCC patients in different prognostic risk groups. Pseudo-time analyses identified four hub genes in HCC progression, including CDCA8, CENPA, SPC25, and TTK, and indicated related cellular senescence. CONCLUSIONS: This study identified a prognostic model of HCC by cellular senescence-related gene expression and insight into novel potential targeted therapies.

Evaluating the productivity of ancient Pu'er tea trees (Camellia sinensis var. assamica): a multivariate modeling approach.

BACKGROUND: The demand for productive economic plant resources is increasing with the continued growth of the human population. Ancient Pu'er tea trees [Camellia sinensis var. assamica (J. W. Mast.) Kitam.] are an important ecological resource with high economic value and large interests. The study intends to explore and evaluate critical drivers affecting the species' productivity, then builds formulas and indexes to make predicting the productivity of such valuable plant resources possible and applicable. RESULTS: Our analysis identified the ideal values of the seven most important environmental variables and their relative contribution (shown in parentheses) to the distribution of ancient Pu'er tea trees: annual precipitation, ca. 1245 mm (28.73%); min temperature of coldest month, ca. 4.2 °C (18.25%); precipitation of driest quarter, ca. 47.5 mm (14.45%); isothermality, 49.9% to 50.4% (14.11%); precipitation seasonality, ca. 89.2 (6.77%); temperature seasonality, ca. 391 (4.46%); and solar radiation, 12,250 to 13,250 kJ m-2 day-1 (3.28%). Productivity was indicated by the total value (viz. fresh leaf harvested multiplied by unit price) of each tree. Environmental suitability, tree growth, and management positively affected productivity; regression weights were 0.325, 0.982, and 0.075, respectively. The degree of productivity was classified as follows: > 0.8, "highly productive"; 0.5-0.8, "productive"; 0.3-0.5, "poorly productive"; and < 0.3, "unproductive". Overall, 53% of the samples were categorized as "poorly productive" or "unproductive"; thus, the management of these regions require attention. CONCLUSIONS: This model improves the accuracy of the predictions of ancient Pu'er tea tree productivity and will aid future analyses of distribution shifts under climate change, as well as the identification of areas suitable for Pu'er tea tree plantations. Our modeling framework provides insights that facilitate the interpretation of abstract concepts and could be applied to other economically valuable plant resources.

Inflammatory Response-Related Long Non-Coding RNA Signature Predicts the Prognosis of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is a high mortality malignant tumor with genetic and phenotypic heterogeneity, making predicting prognosis challenging. Meanwhile, the inflammatory response is an indispensable player in the tumorigenesis process and regulates the tumor microenvironment, which can affect the prognosis of tumor patients. Methods: Using HCC samples in the TCGA-LIHC dataset, we explored lncRNA expression profiles associated with the inflammatory response. The inflammatory response-related lncRNA signature was constructed by univariate Cox regression, LASSO regression, and multivariate Cox regression methods based on inflammatory response-related differentially expressed lncRNAs in HCC. Results: Seven inflammatory response-related lncRNA signatures were identified in predicting HCC prognosis. Kaplan-Meier (K-M) survival analysis indicated that high-risk group HCC patients were associated with poor prognosis. The utility of the inflammatory response-related lncRNA signatures was proved by the AUC and DCA analysis. The nomogram further confirmed the accuracy of the novel signature in predicting HCC patients' prognoses. In validation, our novel signature is more accurate than traditional clinicopathological performance for prognosis prediction of HCC patients. GSEA analysis further elucidated the underlying mechanisms and pathways of HCC progression in the low- and high-risk groups. Moreover, immune cells infiltration responses and immune function analyses revealed a significant difference between high- and low-risk groups in cytolytic activity, MHC class I, type I INF response, type II INF response, inflammation-promoting, and T cell coinhibition. Finally, HHLA2, NRP1, CD276, TNFRSF9, TNFSF4, CD80, and VTCN1 were expressed higher in high-risk groups in the immune checkpoint analysis. Conclusions: A novel inflammatory response-related lncRNA signature (AC145207.5, POLHAS1, AL928654.1, MKLN1AS, AL031985.3, PRRT3AS1, and AC023157.2) is capable of predicting the prognosis of HCC patients and providing new immune targeted therapies insight.

Inflammation-Related Long Non-Coding RNA Signature Predicts the Prognosis of Gastric Carcinoma.

Background: Gastric carcinoma (GC) is a molecularly and phenotypically highly heterogeneous disease, making the prognostic prediction challenging. On the other hand, Inflammation as part of the active cross-talk between the tumor and the host in the tumor or its microenvironment could affect prognosis. Method: We established a prognostic multi lncRNAs signature that could better predict the prognosis of GC patients based on inflammation-related differentially expressed lncRNAs in GC. Results: We identified 10 differently expressed lncRNAs related to inflammation associated with GC prognosis. Kaplan-Meier survival analysis demonstrated that high-risk inflammation-related lncRNAs signature was related to poor prognosis of GC. Moreover, the inflammation-related lncRNAs signature had an AUC of 0.788, proving their utility in predicting GC prognosis. Indeed, our risk signature is more precise in predicting the prognosis of GC patients than traditional clinicopathological manifestations. Immune and tumor-related pathways for individuals in the low and high-risk groups were further revealed by GSEA. Moreover, TCGA based analysis revealed significant differences in HLA, MHC class-I, cytolytic activity, parainflammation, co-stimulation of APC, type II INF response, and type I INF response between the two risk groups. Immune checkpoints revealed CD86, TNFSF18, CD200, and LAIR1 were differently expressed between lowand high-risk groups. Conclusion: A novel inflammation-related lncRNAs (AC015660.1, LINC01094, AL512506.1, AC124067.2, AC016737.1, AL136115.1, AP000695.1, AC104695.3, LINC00449, AC090772.1) signature may provide insight into the new therapies and prognosis prediction for GC patients.

Quantum state tomography of molecules by ultrafast diffraction.

Ultrafast electron diffraction and time-resolved serial crystallography are the basis of the ongoing revolution in capturing at the atomic level of detail the structural dynamics of molecules. However, most experiments capture only the probability density of the nuclear wavepackets to determine the time-dependent molecular structures, while the full quantum state has not been accessed. Here, we introduce a framework for the preparation and ultrafast coherent diffraction from rotational wave packets of molecules, and we establish a new variant of quantum state tomography for ultrafast electron diffraction to characterize the molecular quantum states. The ability to reconstruct the density matrix, which encodes the amplitude and phase of the wavepacket, for molecules of arbitrary degrees of freedom, will enable the reconstruction of a quantum molecular movie from experimental x-ray or electron diffraction data.

A Network Pharmacology Approach to Reveal the Underlying Mechanisms of Artemisia annua on the Treatment of Hepatocellular Carcinoma.

OBJECTIVE: To investigate the potential active ingredients and underlying mechanisms of Artemisia annua (AA) on the treatment of hepatocellular carcinoma (HCC) based on network pharmacology. METHODS: In the present study, we used a network pharmacological method to predict its underlying complex mechanism of treating HCC. First, we obtained relative compounds of AA based on the traditional Chinese medicine systems pharmacology (TCMSP) database and collected potential targets of these compounds by target fishing. Then, we built HCC-related targets target by the oncogenomic database of hepatocellular carcinoma (OncoDB.HCC) and biopharmacological network (PharmDB-K) database. Based on the matching results between AA potential targets and HCC targets, we built a protein-protein interaction (PPI) network to analyze the interactions among these targets and screen the hub targets by topology. Furthermore, the function annotation and signaling pathways of key targets were performed by Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using DAVID tools. Finally, the binding capacity between active ingredients and key targets was validated by molecular docking. RESULTS: A total of 19 main active ingredients of AA were screened as target prediction; then, 25 HCC-related common targets were seeked out via multiple HCC databases. The areas of nodes and corresponding degree values of EGFR, ESR1, CCND1, MYC, EGF, and PTGS2 were larger and could be easily found in the PPI network. Furthermore, GO and KEGG enrichment analysis showed that these key targets were significantly involved in multiple biological processes and pathways which participated in tumor cell proliferation, apoptosis, angiogenesis, tumor invasion, and metastasis to accomplish the anti-HCC activity. The molecular docking analysis showed that quercetin could stably bind to the active pocket of EGFR protein 4RJ5 via LibDock. CONCLUSION: The anticancer effects of AA on HCC were predicted to be associated with regulating tumor cell proliferation, apoptosis, angiogenesis, tumor invasion, and metastasis via various pathways such as the EGFR signaling pathway, ESR1 signaling pathway, and CCND1 signaling pathway. It is suggested that AA might be developed as a broad-spectrum antitumor drug based on its characteristics of multicomponent, multipath, and multitarget.

The Pyroptosis-Related Gene Signature Predicts the Prognosis of Hepatocellular Carcinoma.

Objective: Hepatocellular carcinoma (HCC) is a genetically and phenotypically heterogeneous tumor, and the prediction of its prognosis remains a challenge. In the past decade, studies elucidating the mechanisms that induce tumor cell pyroptosis has rapidly increased. The elucidation of their mechanisms is essential for the clinical development optimal application of anti-hepatocellular carcinoma therapeutics. Methods: Based on the different expression profiles of pyroptosis-related genes in HCC, we constructed a LASSO Cox regression pyroptosis-related genes signature that could more accurately predict the prognosis of HCC patients. Results: We identified seven pyroptosis-related genes signature (BAK1, CHMP4B, GSDMC, NLRP6, NOD2, PLCG1, SCAF11) in predicting the prognosis of HCC patients. Kaplan Meier survival analysis showed that the pyroptosis-related high-risk gene signature was associated with poor prognosis HCC patients. Moreover, the pyroptosis-related genes signature performed well in the survival analysis and ICGC validation group. The hybrid nomogram and calibration curve further demonstrated their feasibility and accuracy for predicting the prognosis of HCC patients. Meanwhile, the evaluation revealed that our novel signature predicted the prognosis of HCC patients more accurately than traditional clinicopathological features. GSEA analysis further revealed the novel signature associated mechanisms of immunity response in high-risk groups. Moreover, analysis of immune cell subsets with relevant functions revealed significant differences in aDCs, APC co-stimulation, CCR, check-point, iDCs, Macrophages, MHC class-I, Treg, and type II INF response between high- and low-risk groups. Finally, the expression of Immune checkpoints was enhanced in high-risk group, and m6A-related modifications were expressed differently between low- and high-risk groups. Conclusion: The novel pyroptosis-related genes signature can predict the prognosis of patients with HCC and insight into new cell death targeted therapies.

A Novel Signature Based on mTORC1 Pathway in Hepatocellular Carcinoma.

BACKGROUND: mTORC1 signal pathway plays a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between the mTORC1 signal pathway and HCC and establish the related gene signature. METHODS: HCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The genes included in mTORC1-associated signature were selected by performing univariate and multivariate Cox regression analyses and lasso regression analysis. The protein expression level of included genes was verified by The Human Protein Altas. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Moreover, the correlation between signature and immune cells infiltration was investigated. Furthermore, a nomogram was established and evaluated by C-index and calibration plot. RESULTS: The signature was established with the six genes (ETF1, GSR, SKAP2, HSPD1, CACYBP, and PNP). Three genes (ETF1, GSR, and HSPD1) have verified their protein expression level in HCC. Under the grouping from signature, patients in the high-risk group showed worse survival than those in the low-risk group in both three datasets. The signature was found to be significantly associated with the infiltration of B cells, CD4+ T-cells, CD8+ T-cells, dendritic cells, macrophages, and neutrophils. The univariate and multivariate Cox regression analysis indicated that mTORC1-related signature could be the potential independent prognostic factor in HCC. Finally, the nomogram involving age, gender, stage, and signature has been established and verified. CONCLUSION: The mTORC1-associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC.