Identification of Comprehensive Biomarkers in Patients With Mismatch Repair-Deficient Colon Adenocarcinoma Based on Parallel Multiomics.

Immunocheckpoint inhibitors have shown impressive efficacy in patients with colon cancer and other types of solid tumor that are mismatch repair-deficient (dMMR). Currently, PCR-capillary electrophoresis is one of the mainstream detection methods for dMMR, but its accuracy is still limited by germline mismatch repair (MMR) mutations, the functional redundancy of the MMR system, and abnormal methylation of MutL Homolog 1 promoter. Therefore, this study aimed to develop new biomarkers for dMMR based on artificial intelligence (AI) and pathologic images, which may help to improve the detection accuracy. To screen for the differential expression genes (DEGs) in dMMR patients and validate their diagnostic and prognostic efficiency, we used the expression profile data from the Cancer Genome Atlas (TCGA). The results showed that the expression of Immunoglobulin Lambda Joining 3 in dMMR patients was significantly downregulated and negatively correlated with the prognosis. Meanwhile, our diagnostic models based on pathologic image features showed good performance with area under the curves (AUCs) of 0.73, 0.86, and 0.81 in the training, test, and external validation sets (Jiangsu Traditional Chinese Medicine Hospital cohort). Based on gene expression and pathologic characteristics, we developed an effective prognosis model for dMMR patients through multiple Cox regression analysis (with AUC values of 0.88, 0.89, and 0.88 at 1-, 3-, and 5-year intervals, respectively). In conclusion, our results showed that Immunoglobulin Lambda Joining 3 and nucleus shape-related parameters (such as nuclear texture, nuclear eccentricity, nuclear size, and nuclear pixel intensity) were independent diagnostic and prognostic factors, suggesting that they could be used as new biomarkers for dMMR patients.

Development and validation of a microenvironment-related prognostic model for hepatocellular carcinoma patients based on histone deacetylase family.

BACKGROUND: Histone deacetylase (HDAC) family can remove acetyl groups from histone lysine residues, and their high expression is closely related to the poor prognosis of hepatocellular carcinoma (HCC) patients. Recently, it has been reported to play an immunosuppressive role in the microenvironment, but little is known about the mechanism. METHODS: Through machine learning, we trained and verified the prognostic model composed of HDACs. CIBERSORT was used to calculate the percentage of immune cells in the microenvironment. Based on co-expression network, potential targets of HDACs were screened. After that, qRT-PCR was employed to evaluate the expression of downstream genes of HDACs, while HPLC-CAD analysis was applied to detect the concentration of arachidonic acid (AA). Finally, Flow cytometry, WB and IHC experiments were used to detect CD86 expression in RAW246.7. RESULTS: We constructed a great prognostic model composed of HDAC1 and HDAC11 that was significantly associated with overall survival. These HDACs were related to the abundance of macrophages, which might be attributed to their regulation of fatty-acid-metabolism related genes. In vitro experiments, the mRNA expression of ACSM2A, ADH1B, CYP2C8, CYP4F2 and SLC27A5 in HCC-LM3 was significantly down-regulated, and specific inhibitors of HDAC1 and HDAC11 significantly promoted the expression of these genes. HDAC inhibitors can promote the metabolism of AA, which may relieve the effect of AA on the polarization of M1 macrophages. CONCLUSIONS: Our study revealed the blocking effect of HDAC1 and HDAC11 on the polarization of macrophages M1 in the microenvironment by inhibiting fatty acid metabolism.

Optimizing component formula suppresses lung cancer by blocking DTL-mediated PDCD4 ubiquitination to regulate the MAPK/JNK pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Salvia miltiorrhiza Bunge and Panax ginseng C. A. Meyer have special curative effect on cancer treatment. The optimizing component formula (OCF) extracted from those two herbs was in line with the anti-lung cancer treatment principle of activating blood and supplementing 'Qi'. However, the study on the mechanism of component formula has always been an insurmountable challenge. Nowadays, the application of network pharmacology and artificial intelligence (AI) in the field of TCM provides new ideas for the study of new targets and mechanisms of TCM, which promotes the modernization of TCM. AIM OF THE STUDY: This study aims to further explore the anti-lung cancer mechanism of OCF by using an integrated strategy of network pharmacology and AI technology. MATERIALS AND METHODS: Bioinformatic analysis was used to analyze the expression levels, prognosis and survival of DTL and PDCD4 in cancer patients. The binding strength of OCF and DTL was simulated by molecular docking, and the affinity between them was detected by Bio-layer interferometry. Network pharmacology was used to predict the active components, potential targets and pathways of OCF. The association between key targets and their corresponding components and DTL was analyzed by Ingenuity Pathway Analysis (IPA). MTT assay, colony formation assay, wound-healing assay and transwell assay were used to verify the inhibitory effects of OCF on lung cancer cells in vitro. qRT-PCR and Western blot assay were used to detect the effects of OCF on mRNA and protein expression of DTL, PDCD4 and key genes in MAPK/JNK pathways. RESULTS: Bioinformatics analysis showed that DTL was significantly up-regulated in lung cancer, which was associated with high malignancy rate, high metastasis rate and poor prognosis of primary tumor. PDCD4 was down-regulated in lung cancer, and associated with high metastasis rate and poor prognosis. The good affinity between OCF and DTL was predicted and verified by molecular docking and Bio-layer interferometry. Based on the network pharmacological databases, 40 active components and 220 corresponding targets of OCF were screened out. KEGG analysis showed that OCF component targets were mainly enriched in MAPK signaling pathway. IPA results showed the interrelationship between DTL, PDCD4, MAPK pathway genes and their corresponding OCF components. In addition, in vitro experiments demonstrated anti-lung cancer activity of OCF, as validated, via impairing cell viability and cell proliferation, as well as inhibiting migration and invasion abilities in lung cancer cells. qRT-PCR showed that OCF down-regulated the mRNA expression of DTL, MAP4K1, JNK, c-Jun and c-Myc, and up-regulated the mRNA expression of PDCD4 and P53 genes in A549 lung cancer cells. Western blot suggested that OCF suppressed the protein level of DTL and blocked the ubiquitination of PDCD4 in A549 lung cancer cells, and down-regulated the protein levels of MAP4K1, p-JNK and p-c-Jun while up-regulated the proteins expression level of P53. CONCLUSIONS: OCF might elicit an anti-lung cancer effect by blocking DTL-mediated PDCD4 ubiquitination and suppression of the MAPK/JNK pathway. Meanwhile, our work revealed that network pharmacology and AI technology strategy are cogent means of studying the active components and mechanism of TCM.

Highly heterogeneous-related genes of triple-negative breast cancer: potential diagnostic and prognostic biomarkers.

BACKGROUND: Triple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, showing aggressive clinical behaviors and poor outcomes. It urgently needs new therapeutic strategies to improve the prognosis of TNBC. Bioinformatics analyses have been widely used to identify potential biomarkers for facilitating TNBC diagnosis and management. METHODS: We identified potential biomarkers and analyzed their diagnostic and prognostic values using bioinformatics approaches. Including differential expression gene (DEG) analysis, Receiver Operating Characteristic (ROC) curve analysis, functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, survival analysis, multivariate Cox regression analysis, and Non-negative Matrix Factorization (NMF). RESULTS: A total of 105 DEGs were identified between TNBC and other breast cancer subtypes, which were regarded as heterogeneous-related genes. Subsequently, the KEGG enrichment analysis showed that these genes were significantly enriched in 'cell cycle' and 'oocyte meiosis' related pathways. Four (FAM83B, KITLG, CFD and RBM24) of 105 genes were identified as prognostic signatures in the disease-free interval (DFI) of TNBC patients, as for progression-free interval (PFI), five genes (FAM83B, EXO1, S100B, TYMS and CFD) were obtained. Time-dependent ROC analysis indicated that the multivariate Cox regression models, which were constructed based on these genes, had great predictive performances. Finally, the survival analysis of TNBC subtypes (mesenchymal stem-like [MSL] and mesenchymal [MES]) suggested that FAM83B significantly affected the prognosis of patients. CONCLUSIONS: The multivariate Cox regression models constructed from four heterogeneous-related genes (FAM83B, KITLG, RBM24 and S100B) showed great prediction performance for TNBC patients' prognostic. Moreover, FAM83B was an important prognostic feature in several TNBC subtypes (MSL and MES). Our findings provided new biomarkers to facilitate the targeted therapies of TNBC and TNBC subtypes.

Zi Shen Decoction Inhibits Growth and Metastasis of Lung Cancer via Regulating the AKT/GSK-3ß/ß-Catenin Pathway.

Lung cancer has become the leading cause of cancer-related death worldwide. Oxidative stress plays important roles in the pathogenesis of lung cancer. Many natural products show antioxidative activities in cancer treatment. Zi Shen decoction (ZSD) is a classic prescription for the treatment of lung disease. However, its effect on lung cancer lacks evidence-based efficacy. In this study, we investigated the anticancer effects of ZSD on lung cancer in vivo and in vitro. Our results showed that oral administration of ZSD suppressed the Lewis lung cancer (LLC) growth in a subcutaneous allograft model and promoted necrosis and inflammatory cell infiltration in the tumor tissues. Furthermore, ZSD not only inhibited tumor cell proliferation and migration but also induced cell apoptosis in lung cancer cells. PI3K/AKT signaling is well characterized in response to oxidative stress. The bioinformatics analysis and western blot assays suggested that ZSD decreased the enzyme activity of PI3K and AKT in vivo and in vitro. We also found that the AKT/GSK-3ß/ß-catenin pathway medicated anticancer effect of ZSD in lung cancer cells. In conclusion, we demonstrate for the first time that ZSD possesses antitumor properties, highlighting its potential use as an alternative strategy or adjuvant treatment for lung cancer therapy.

HYR-2 plays an anti-lung cancer role by regulating PD-L1 and Akkermansia muciniphila.

Traditional Chinese medicine (TCM) plays a vital part in cancer treatment due to its unique superiority. Huoxue Yiqi Recipe-2 (HYR-2) was supposed to have therapeutic effect on lung cancer, which came from Ze Qi Decoction in one of the four great classics of TCM called "Synopsis of Prescriptions of the Golden Chamber". Network pharmacology demonstrated that the targets of active components from HYR-2 were significantly enriched in the signaling pathways, which were closely associated with non-small cell lung cancer (NSCLC) and programmed death ligand 1 (PD-L1). Then, data about NSCLC was downloaded from Gene Expression Omnibus database (GEO). The Cancer Genome Atlas (TCGA) and DisGeNET was analyzed by bioinformatics, and 214 biomarkers for NSCLC were obtained, containing 14 targets of active components from HYR-2 (which were significantly enriched in the PD-L1 related signaling pathway). In vivo and in vitro experiments showed that HYR and HYR-2 could inhibit the growth of lung cancer and down-regulate the expression of PD-L1, which might be related to the blocking effect of HYR-2 on the PI3K/Akt signaling pathway. Furthermore, HYR-2 promoted the transformation of M2 macrophages into M1 macrophages as well. It is deserved to be mentioned that the level of Akkermansia muciniphila was also significantly elevated by HYR-2, which was believed to enhance the therapeutic effect of PD-L1 antibodies. To sum up, HYR-2 might play an anti-lung cancer effect by down-regulating PD-L1 together with up-regulating Akkermansia muciniphila.

Based on Integrated Bioinformatics Analysis Identification of Biomarkers in Hepatocellular Carcinoma Patients from Different Regions.

Accumulating statistics have shown that liver cancer causes the second highest mortality rate of cancer-related deaths worldwide, of which 80% is hepatocellular carcinoma (HCC). Given the underlying molecular mechanism of HCC pathology is not fully understood yet, identification of reliable predictive biomarkers is more applicable to improve patients' outcomes. The results of principal component analysis (PCA) showed that the grouped data from 1557 samples in Gene Expression Omnibus (GEO) came from different populations, and the mean tumor purity of tumor tissues was 0.765 through the estimate package in R software. After integrating the differentially expressed genes (DEGs), we finally got 266 genes. Then, the protein-protein interaction (PPI) network was established based on these DEGs, which contained 240 nodes and 1747 edges. FOXM1 was the core gene in module 1 and highly associated with FOXM1 transcription factor network pathway, while FTCD was the core gene in module 2 and was enriched in the metabolism of amino acids and derivatives. The expression levels of hub genes were in line with The Cancer Genome Atlas (TCGA) database. Meanwhile, there were certain correlations among the top ten genes in the up- and downregulated DEGs. Finally, Kaplan-Meier curves and receiver operating characteristic (ROC) curves were plotted for the top five genes in PPI. Apart from CDKN3, the others were closely concerned with overall survival. In this study, we detected the potential biomarkers and their involved biological processes, which would provide a new train of thought for clinical diagnosis and treatment.

Elaboration of the Comprehensive Metabolic Profile of Salvianolic Acid A in Vivo and in Vitro Using UFLC-Q/TOF-MS.

Salvianolic acid A (Sal A) has a wide range of pharmacological activities. To date, there have been no systematic and detailed metabolite research data of Sal A after oral administration in vitro and in vivo. In this study, a rapid and systematic method based on ultrafast liquid chromatography-quadrupole-time-of-flight mass spectrometry was developed to detect metabolites of Sal A in vitro (human liver microsome, human intestinal microbiota, artificial gastric, and intestinal juice) and in vivo (urine, plasma, feces, and various organs collected after oral administration of Sal A to normal rats and pseudo-germ-free rats). A total of 26 metabolites of Sal A were characterized. These metabolites were formed through extensive metabolic reactions, such as hydroxylation, hydrogenation, and glucuronidation reactions. This study provides novel possibility for exploring the potential biological mechanism of Sal A, and aids the promotion of clinical application.