Integrated immunogenomic analysis of single-cell and bulk profiling reveals novel tumor antigens and subtype-specific therapeutic agents in lung adenocarcinoma.

Background: In recent years, mRNA-based vaccines with promising safety and functional characteristics have gained significant momentum in cancer immunotherapy. However, stable immunological molecular subtypes of lung adenocarcinoma (LUAD) and novel tumor antigens for LUAD mRNA vaccine development remain elusive. Therefore, a novel approach is urgently needed to identify suitable LUAD subtypes and potential tumor antigens. Methods: The Cancer Genome Atlas (TCGA), the Genotype Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases were utilized to retrieve gene expression data. The LUAD Immunological Multi-Omics Classification (LIMOC) system was developed using seven machine learning (ML) algorithms by performing integrative immunogenomic analysis of single-cell and bulk tissue transcriptome profiling. Subsequently, a panel of approaches was applied to identify novel tumor antigens. Results: First, the LIMOC system was construct to identify three subtypes: LIMOC1, LIMOC2, and LIMOC3. Second, we identified CHIT1, LILRA4, and MEP1A as novel tumor antigens in LUAD; these genes were up-regulated, amplified, and mutated, and showed a positive association with APC infiltration, making them promising candidates for designing mRNA vaccines. Notably, the LIMOC2 subtype had the worst prognosis and could benefit most from mRNA immunization. Furthermore, we performed a comprehensive in silico screening of approximately 2000 compounds and identified Sorafenib and Azacitidine as potential subtype-specific therapeutic agents. Conclusions: Overall, our study established a robust LIMOC system and identified CHIT1, LILRA4, and MEP1A as promising tumor antigen candidates for development of anti-LUAD mRNA vaccines, particularly for the LIMOC2 subtype.

Benzosceptrin C induces lysosomal degradation of PD-L1 and promotes antitumor immunity by targeting DHHC3.

Programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) blockade has become a mainstay of cancer immunotherapy. Targeting the PD-1/PD-L1 axis with small molecules is an attractive approach to enhance antitumor immunity. Here, we identified a natural marine product, benzosceptrin C (BC), that enhances the cytotoxicity of T cells to cancer cells by reducing the abundance of PD-L1. Furthermore, BC exerts its antitumor effect in mice bearing MC38 tumors by activating tumor-infiltrating T cell immunity. Mechanistic studies suggest that BC can prevent palmitoylation of PD-L1 by inhibiting DHHC3 enzymatic activity. Subsequently, PD-L1 is transferred from the membrane to the cytoplasm and cannot return to the membrane via recycling endosomes, triggering lysosome-mediated degradation of PD-L1. Moreover, the combination of BC and anti-CTLA4 effectively enhances antitumor T cell immunity. Our findings reveal a previously unrecognized antitumor mechanism of BC and represent an alternative immune checkpoint blockade (ICB) therapeutic strategy to enhance the efficacy of cancer immunotherapy.

COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming.

Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.

Cytokine Storm in Acute Viral Respiratory Injury: Role of Qing-Fei-Pai-Du Decoction in Inhibiting the Infiltration of Neutrophils and Macrophages through TAK1/IKK/NF-[Formula: see text]B Pathway.

COVID-19 has posed unprecedented challenges to global public health since its outbreak. The Qing-Fei-Pai-Du decoction (QFPDD), a Chinese herbal formula, is widely used in China to treat COVID-19. It exerts an impressive therapeutic effect by inhibiting the progression from mild to critical disease in the clinic. However, the underlying mechanisms remain obscure. Both SARS-CoV-2 and influenza viruses elicit similar pathological processes. Their severe manifestations, such as acute respiratory distress syndrome (ARDS), multiple organ failure (MOF), and viral sepsis, are correlated with the cytokine storm. During flu infection, QFPDD reduced the lung indexes and downregulated the expressions of MCP-1, TNF-[Formula: see text], IL-6, and IL-1[Formula: see text] in broncho-alveolar lavage fluid (BALF), lungs, or serum samples. The infiltration of neutrophils and inflammatory monocytes in lungs was decreased dramatically, and lung injury was ameliorated in QFPDD-treated flu mice. In addition, QFPDD also inhibited the polarization of M1 macrophages and downregulated the expressions of IL-6, TNF-[Formula: see text], MIP-2, MCP-1, and IP-10, while also upregulating the IL-10 expression. The phosphorylated TAK1, IKK[Formula: see text]/[Formula: see text], and I[Formula: see text]B[Formula: see text] and the subsequent translocation of phosphorylated p65 into the nuclei were decreased by QFPDD. These findings indicated that QFPDD reduces the intensity of the cytokine storm by inhibiting the NF-[Formula: see text]B signaling pathway during severe viral infections, thereby providing theoretical and experimental support for its clinical application in respiratory viral infections.

Multi-omics and network pharmacology study reveals the effects of Dengzhan Shengmai capsule against neuroinflammatory injury and thrombosis induced by ischemic stroke.

ETHNOPHARMACOLOGICAL RELEVANCE: Dengzhan Shengmai capsule (DZSM) is a traditional herb medicine used by Dai, an ethnic-minority community living in Xishuang banna tropical rainforest in Southwest of China. It was originally intended to treat disorders caused by insufficient brain function, characterized by gibberish, unresponsiveness, or confusion. Accumulating clinical evidences exhibited that it is effective on treating ischemic stroke (IS). However, the action of DZSM against IS needs to be further elucidated. AIM OF THE STUDY: To investigate the effect of DZSM and its active components against IS and the way of its action by multi-omics and network pharmacology. MATERIALS AND METHODS: A middle cerebral artery occlusion/reperfusion (MCAO/R) rat model was established to investigate the effect of DZSM on the focal cerebral ischemia/reperfusion injury. An integrated strategy combining metabolomics, network pharmacology and transcriptomics was performed to systematically clarify the underlying mechanism of action of DZSM against IS. AutoDock Vina was applied to conduct molecular docking simulation for the binding between the potential active compounds and targets. Arachidonic acid (AA) induced platelet aggregation and lipopolysaccharide (LPS) stimulated microglial cells BV2 inflammation models were applied for the in vitro validation of effects of DZSM and its potential active compounds. RESULTS: In MCAO/R rats, DZSM could significantly reduce the infarct volume. Putative target prediction and functional enrichment analysis based on network pharmacological indicated that the key targets and the potential active compounds played important roles in DZSM's treatment to IS. The targets included four common genes (PTGS1, PTGS2, NFKB1 and NR1I2) and five key TFs (NFKB1, RELA, HIF1A, ESR1 and HDAC1), whilst 22 potential active compounds were identified. Molecular docking indicated that good binding affinity have been seen between those compounds and NR1I2, NFKB1, and RELA. Multi-omics study revealed that DZSM could regulate glutamate by influencing citrate cycle and glutamate involved pathways, and have showed neuroprotection activity and anti-inflammation activity by inhibiting NF-κB pathway. Neuroprotective effects of DZSM was validated by regulating of NF-κB signaling pathway and its downstream NO, TNF-α and IL-6 cytokines contributed to the activity of DZSM and its active compounds of scutellarin, quercetin 3-O-glucuronide, ginsenoside Rb1, schizandrol A and 3, 5-diCQA, whilst the antithrombotic activity of DZSM and its active compounds of schisanhenol, apigenin and schisantherin B were screened out by anti-platelet aggregation experiment. CONCLUSION: DZSM could against IS via regulating its downstream NO, TNF-α and IL-6 cytokines through NF-κB signaling pathway and alleviating thrombosis.

Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM.

With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug-disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net.

Identification of a DNA Methylation-Driven Genes-Based Prognostic Model and Drug Targets in Breast Cancer: In silico Screening of Therapeutic Compounds and in vitro Characterization.

DNA methylation is a vital epigenetic change that regulates gene transcription and helps to keep the genome stable. The deregulation hallmark of human cancer is often defined by aberrant DNA methylation which is critical for tumor formation and controls the expression of several tumor-associated genes. In various cancers, methylation changes such as tumor suppressor gene hypermethylation and oncogene hypomethylation are critical in tumor occurrences, especially in breast cancer. Detecting DNA methylation-driven genes and understanding the molecular features of such genes could thus help to enhance our understanding of pathogenesis and molecular mechanisms of breast cancer, facilitating the development of precision medicine and drug discovery. In the present study, we retrospectively analyzed over one thousand breast cancer patients and established a robust prognostic signature based on DNA methylation-driven genes. Then, we calculated immune cells abundance in each patient and lower immune activity existed in high-risk patients. The expression of leukocyte antigen (HLA) family genes and immune checkpoints genes were consistent with the above results. In addition, more mutated genes were observed in the high-risk group. Furthermore, a in silico screening of druggable targets and compounds from CTRP and PRISM databases was performed, resulting in the identification of five target genes (HMMR, CCNB1, CDC25C, AURKA, and CENPE) and five agents (oligomycin A, panobinostat, (+)-JQ1, voxtalisib, and arcyriaflavin A), which might have therapeutic potential in treating high-risk breast cancer patients. Further in vitro evaluation confirmed that (+)-JQ1 had the best cancer cell selectivity and exerted its anti-breast cancer activity through CENPE. In conclusion, our study provided new insights into personalized prognostication and may inspire the integration of risk stratification and precision therapy.

A Comprehensive Investigation to Reveal the Relationship Between Plasmacytoid Dendritic Cells and Breast Cancer by Multiomics Data Analysis.

Plasmacytoid dendritic cells (pDC) are an essential immune microenvironment component. They have been reported for crucial roles in linking the adaptive and immune systems. However, the prognostic role of the pDC in breast cancer (BRCA) was controversial. In this work, we collected large sample cohorts and did a comprehensive investigation to reveal the relationship between pDC and BRCA by multiomics data analysis. Elevated pDC levels were correlated with prolonged survival outcomes in BRCA patients. The distinct mutation landscape and lower burden of somatic copy number alterations (SCNA) and lower intratumoral heterogeneity were observed in the high pDC abundance group. Additionally, a more sensitive immune response and chemotherapies response were observed in the high pDC group, which implicates that patients with high pDC abundance can be benefited from the combination of chemotherapy and immunotherapy. In conclusion, the correlation between pDC abundance and BRCA patients' overall survival (OS) was found to be positive. We identified the molecular profiles of BRCA patients with pDC abundance. Our findings may be beneficial in aiding in the development of immunotherapy and elucidating on the precision treatment for BRCA.

Ferroptosis-Related Gene Model to Predict Overall Survival of Ovarian Carcinoma.

BACKGROUND: Ovarian cancer (OC) is the eighth most common cause of cancer death and the second cause of gynecologic cancer death in women around the world. Ferroptosis, an iron-dependent regulated cell death, plays a vital role in the development of many cancers. Applying expression of ferroptosis-related gene to forecast the cancer progression is helpful for cancer treatment. However, the relationship between ferroptosis-related genes and OC patient prognosis is still vastly unknown, making it still a challenge for developing ferroptosis therapy for OC. METHODS: The Cancer Genome Atlas (TCGA) data of OC were obtained and the datasets were randomly divided into training and test datasets. A novel ferroptosis-related gene signature associated with overall survival (OS) was constructed according to the training cohort. The test dataset and ICGC dataset were used to validate this signature. RESULTS: We constructed a model containing nine ferroptosis-related genes, namely, LPCAT3, ACSL3, CRYAB, PTGS2, ALOX12, HSBP1, SLC1A5, SLC7A11, and ZEB1, and predicted the OS of OC in TCGA. At a suitable cutoff, patients were divided into low risk and high risk groups. The OS curves of the two groups of patients had significant differences, and the time-dependent receiver operating characteristics (ROCs) were as high as 0.664, respectively. Then, the test dataset and the ICGC dataset were used to evaluate our model, and the ROCs of test dataset were 0.667 and 0.777, respectively. In addition, functional analysis and correlation analysis showed that immune-related pathways were significantly enriched. Meanwhile, we also integrated with other clinical factors and we found the synthesized clinical factors and ferroptosis-related gene signature improved prognostic accuracy relative to the ferroptosis-related gene signature alone. CONCLUSION: The ferroptosis-related gene signature could predict the OS of OC patients and improve therapeutic decision-making.

Integrated Bioinformatics Analysis Reveals Key Candidate Genes and Pathways Associated With Clinical Outcome in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) accounts for approximately 85-90% of all liver cancer cases and has poor relapse-free survival. There are many gene expression studies that have been performed to elucidate the genetic landscape and driver pathways leading to HCC. However, existing studies have been limited by the sample size and thus the pathogenesis of HCC is still unclear. In this study, we performed an integrated characterization using four independent datasets including 320 HCC samples and 270 normal liver tissues to identify the candidate genes and pathways in the progression of HCC. A total of 89 consistent differentially expression genes (DEGs) were identified. Gene-set enrichment analysis revealed that these genes were significantly enriched for cellular response to zinc ion in biological process group, collagen trimer in the cellular component group, extracellular matrix (ECM) structural constituent conferring tensile strength in the molecular function group, protein digestion and absorption, mineral absorption and ECM-receptor interaction. Network system biology based on the protein-protein interaction (PPI) network was also performed to identify the most connected and important genes based on our DEGs. The top five hub genes including osteopontin (SPP1), Collagen alpha-2(I) chain (COL1A2), Insulin-like growth factor I (IGF1), lipoprotein A (LPA), and Galectin-3 (LGALS3) were identified. Western blot and immunohistochemistry analysis were employed to verify the differential protein expression of hub genes in HCC patients. More importantly, we identified that these five hub genes were significantly associated with poor disease-free survival and overall survival. In summary, we have identified a potential clinical significance of these genes as prognostic biomarkers for HCC patients who would benefit from experimental approaches to obtain optimal outcome.

Network-Based Approach to Identify the Antiproliferative Mechanisms of Bruceine D in Breast Cancer From the Cancer Genome Atlas.

Bruceine D (BD) is a natural compound extracted from a Chinese herb Brucea javanica that has been used for anti-inflammatory and anti-cancer treatment. However, little is reported about BD's effects in breast cancer tumorigenesis. In this paper, we aimed to investigate the effect of BD in breast cancer and elucidate the potential mechanism of BD by integrated multiple databases. Our data suggested BD inhibited MCF-7 and MDA-MB-231 cells proliferation and promoted cells apoptosis. We integrated multiple bioinformatics analysis strategies to identify genes, hub modules and pathways associated with BD treatment. Three key pathways, including AMIT_SERUM_RESPONSE_40_MCF10A, BILD_HRAS_ONCOGENIC_SIGNATURE, and NAGASHIMA_NRG1_SIGNALING_UP were identified to be associated with therapeutic effects of BD in breast cancer. Additionally, we validated the key genes by using quantitative real-time PCR and western blot. In conclusion, these findings revealed potential molecular mechanisms of BD to treat breast cancer by affecting AMIT_SERUM_RESPONSE_40_MCF10A, BILD_HRAS_ONCOGENIC_SIGNATURE, and NAGASHIMA_NRG1_SIGNALING_UP pathways and regulating expression of ZFP36, EGR1, and FOS.

Computational systems pharmacology reveals an antiplatelet and neuroprotective mechanism of Deng-Zhan-Xi-Xin injection in the treatment of ischemic stroke.

Herbs are typically prescribed in traditional Chinese medicine (TCM) to treat complex diseases. The multicomponent nature of herbal drug ingredients makes it difficult to readily understand their mode of action. To decipher their molecular mechanisms, here we proposed a novel computational systems pharmacology based approach, which consisted of transcriptome profiling, data collection, statistical analysis, network algorithm, bioinformatics analysis and pharmacological validation. The network algorithm called signed random walk with restart (SRWR) was used to simulate the propagation of drugs' effects on networks. This algorithm could identify proteins either positively or negatively regulated (activated or inhibited) by drugs on human signaling networks. To establish proof of principle, the herbal product Deng-Zhan-Xi-Xin injection (DZXXI), which exhibits pharmacological effects in ischemic stroke but its mechanism was unclear, was analyzed. Eighty-three targets were predicted with high confidence for DZXXI's active compounds in plasma, and 87 differentially expressed genes (DEGs) were identified in MCF7 cells treated with DZXXI. These target genes were further found to be associated with pathways involved in neuronal apoptosis in ischemic stroke, such as NF-κB signaling, TNF signaling, and PI3K-Akt signaling. Intersection analysis between DZXXI's putative targets with ischemic stroke-associated genes identified two important targets (PTGS1, PTGS2) corresponding to four DZXXI compounds, which were further validated using in silico and in vitro/vivo models. The most inhibited genes identified by the SRWR algorithm were significantly enriched with ischemic stroke-associated disease genes, antiplatelet associated pathways, and their encoded proteins were enriched in brain, vascular endothelium and platelets. The CMAP analysis based on DEGs suggested that DZXXI could function as both an anti-inflammatory and anti-platelet agent. Taken together, the computational analysis suggested that DZXXI exhibited anti-platelet and neuroprotective effects in the treatment of ischemic stroke. These deductions were preliminarily confirmed by subsequent in vitro/vivo studies. This approach provides a systems perspective to study the relevance between herbal drugs and disease processes, and can reveal possible pharmacological effects of multiple ingredients within herbal product.

Systems Biology Analysis of the Effect and Mechanism of Qi-Jing-Sheng-Bai Granule on Leucopenia in Mice.

Qi-Jing-Sheng-Bai granule (QJSB) is a newly developed traditional Chinese medicine (TCM) formula. Clinically, it has been used for the treatment of leucopenia. However, its pharmacological mechanism needs more investigation. In this study, we firstly tested the effects of QJSB on leucopenia using mice induced by cyclophosphamide. Our results suggested that QJSB significantly raised the number of peripheral white blood cells, platelets and nucleated bone marrow cells. Additionally, it markedly enhanced the cell viability and promoted the colony formation of bone marrow mononuclear cells. Furthermore, it reversed the serum cytokines IL-6 and G-CSF disorders. Then, using transcriptomics datasets and metabonomic datasets, we integrated transcriptomics-based network pharmacology and metabolomics technologies to investigate the mechanism of action of QJSB. We found that QJSB regulated a series of biological processes such as hematopoietic cell lineage, homeostasis of number of cells, lymphocyte differentiation, metabolic processes (including lipid, amino acid, and nucleotide metabolism), B cell receptor signaling pathway, T cell activation and NOD-like receptor signaling pathway. In a summary, QJSB has protective effects to leucopenia in mice probably through accelerating cell proliferation and differentiation, regulating metabolism response pathways and modulating immunologic function at a system level.

A six-mRNA prognostic model to predict survival in head and neck squamous cell carcinoma.

BACKGROUND: Transcriptional dysregulation is one of the most important features of cancer genesis and progression. Applying gene expression dysregulation information to predict the development of cancers is useful for cancer diagnosis. However, previous studies mainly focused on the relationship between a single gene and cancer. Prognostic prediction using combined gene models remains limited. MATERIALS AND METHODS: Gene expression profiles were downloaded from The Cancer Genome Atlas and the data sets were randomly divided into training data sets and test data sets. A six-gene signature associated with head and neck squamous cell carcinoma (HNSCC) and overall survival (OS) was identified according to a training cohort by using weighted gene correlation network analysis and least absolute shrinkage and selection operator Cox regression. The test data set and gene expression omnibus (GEO) data set were used to validate this signature. RESULTS: We identified six candidate genes, namely, FOXL2NB, PCOLCE2, SPINK6, ULBP2, KCNJ18, and RFPL1, and, using a six-gene model, predicted the risk of death of head and neck squamous cell carcinoma in The Cancer Genome Atlas. At a selected cutoff, patients were clustered into low- and high-risk groups. The OS curves of the two groups of patients had significant differences, and the time-dependent receiver operating characteristics of OS, disease-specific survival (DSS), and progression-free survival (PFS) were as high as 0.766, 0.731, and 0.623, respectively. Then, the test data set and the GEO data set were used to evaluate our model, and we found that the OS time in the high-risk group was significantly shorter than in the low-risk group in both data sets, and the receiver operating characteristics of test data set were 0.669, 0.675, and 0.614, respectively. Furthermore, univariate and multivariate Cox regression analyses showed that the risk score was independent of clinicopathological features. CONCLUSION: The six-gene model could predict the OS of HNSCC patients and improve therapeutic decision-making.

Rapid characterization of chemical constituents and metabolites of Qi-Jing-Sheng-Bai granule by using UHPLC-Q-TOF-MS.

Qi-Jing-Sheng-Bai granule is an effective traditional Chinese medicine formula that has been widely used for the treatment of leukopenia post radiotherapy or chemotherapy. However, its chemical constituents were still unclear, which hindered interpreting bioactive constituents and studying integrative mechanisms. In this study, we developed a three-step strategy to characterize the chemical constituents and metabolites of Qi-Jing-Sheng-Bai by using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. As a result, a total of 143 compounds, including 56 flavonoids, 51 saponins, and 36 other compounds, of which contained six pairs of isomers, were tentatively identified and characterized via reference standards and by comparing mass spectrometry data with literature. After oral administration of 15 g/kg Qi-Jing-Sheng-Bai, a number of 42 compounds including 24 prototype compounds and 18 metabolites have been detected in the serum of rats. This work serves as the first reference for Qi-Jing-Sheng-Bai chemical components and metabolites. Moreover, it provided a rapid and valid analytical strategy for characterization of the chemical compounds and metabolites of traditional Chinese medicine formula.