Integrated Exploration of Epigenetic Dysregulation Reveals a Stemness/EMT Subtype and MMP12 Linked to the Progression and Prognosis in Hepatocellular Carcinoma.

Epigenetic dysregulation drives aberrant transcriptional programs playing a critical role in hepatocellular carcinoma (HCC), which may provide novel insights into the heterogeneity of HCC. This study performed an integrated exploration on the epigenetic dysregulation of miRNA and methylation. We discovered and validated three patterns endowed with gene-related transcriptional traits and clinical outcomes. Specially, a stemness/epithelial-mesenchymal transition (EMT) subtype was featured by immune exhaustion and the worst prognosis. Besides, MMP12, a characteristic gene, was highly expressed in the stemness/EMT subtype, which was verified as a pivotal regulator linked to the unfavorable prognosis and further proven to promote tumor proliferation, invasion, and metastasis in vitro experiments. Proteomic analysis by mass spectrometry sequencing also indicated that the overexpression of MMP12 was significantly associated with cell proliferation and adhesion. Taken together, this study unveils innovative insights into epigenetic dysregulation and identifies a stemness/EMT subtype-specific gene, MMP12, correlated with the progression and prognosis of HCC.

Crosstalk of cell death pathways unveils an autophagy-related gene AOC3 as a critical prognostic marker in colorectal cancer.

The intricate crosstalk of various cell death forms was recently implicated in cancers, laying a foundation for exploring the association between cell death and cancers. Recent evidence has demonstrated that biological networks outperform snapshot gene expression profiles at discovering promising biomarkers or heterogenous molecular subtypes across different cancer types. In order to investigate the behavioral patterns of cell death-related interaction perturbation in colorectal cancer (CRC), this study constructed the interaction-perturbation network with 11 cell death pathways and delineated four cell death network (CDN) derived heterogeneous subtypes (CDN1-4) with distinct molecular characteristics and clinical outcomes. Specifically, we identified a subtype (CDN4) endowed with high autophagy activity and the worst prognosis. Furthermore, AOC3 was identified as a potential autophagy-related biomarker, which demonstrated exceptional predictive performance for CDN4 and significant prognostic value. Overall, this study sheds light on the complex interplay of various cell death forms and reveals an autophagy-related gene AOC3 as a critical prognostic marker in CRC.

Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma.

BACKGROUND: Recently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD). METHODS: Single-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS). RESULTS: This study identified TIME-related lncRNAs from integrated analysis of single­cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy. CONCLUSIONS: This study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients.

Corrigendum: Pyruvate kinase M2 promotes prostate cancer metastasis through regulating ERK1/2-COX-2 signaling.

[This corrects the article DOI: 10.3389/fonc.2020.544288.].

Cellular Ligand-Receptor Perturbations Unravel MEIS2 as a Key Factor for the Aggressive Progression and Prognosis in Stage II/III Colorectal Cancer.

Approximately 10-15% of stage II and 25-30% of stage III colorectal cancer (CRC) patients experience recurrence within 5 years after surgery, and existing taxonomies are insufficient to meet the needs of clinical precision treatment. Thus, robust biomarkers and precise management were urgently required to stratify stage II and III CRC and identify potential patients who will benefit from postoperative adjuvant therapy. Alongside, interactions of ligand-receptor pairs point to an emerging direction in tumor signaling with far-reaching implications for CRC, while their impact on tumor subtyping has not been elucidated. Herein, based on multiple large-sample multicenter cohorts and perturbations of the ligand-receptor interaction network, four well-characterized ligand-receptor-driven subtypes (LRDS) were established and further validated. These molecular taxonomies perform with unique heterogeneity in terms of molecular characteristics, immune and mutational landscapes, and clinical features. Specifically, MEIS2, a key LRDS4 factor, performs significant associations with proliferation, invasion, migration, and dismal prognosis of stage II/III CRC, revealing promising directions for prognostic assessment and individualized treatment of CRC patients. Overall, our study sheds novel insights into the implications of intercellular communication on stage II/III CRC from a ligand-receptor interactome perspective and revealed MEIS2 as a key factor in the aggressive progression and prognosis for stage II/III CRC.

Immune perturbation network identifies an EMT subtype with chromosomal instability and tumor immune-desert microenvironment.

Most gastric cancer (GC) subtypes are identified through transcriptional profiling overlooking dynamic changes and interactions in gene expression. Based on the background network of global immune genes, we constructed sample-specific edge-perturbation matrices and identified four molecular network subtypes of GC (MNG). MNG-1 displayed the best prognosis and vigorous cell cycle activity. MNG-2 was enriched by immune-hot phenotype with the potential for immunotherapy response. MNG-3 and MNG-4 were identified with epithelial-mesenchymal transition (EMT) peculiarity and worse prognosis, termed EMT subtypes. MNG-3 was characterized by low mutational burden and stromal cells and considered a replica of previous subtypes associated with poor prognosis. Notably, MNG-4 was considered a previously undefined subtype with a dismal prognosis, characterized by chromosomal instability and immune-desert microenvironment. This subtype tended to metastasize and was resistant to respond to immunotherapy. Pharmacogenomics analysis showed three therapeutic agents (NVP-BEZ235, LY2606368, and rutin) were potential interventions for MNG-4.

Multi-center validation of an immune-related lncRNA signature for predicting survival and immune status of patients with renal cell carcinoma: an integrating machine learning-derived study.

BACKGROUND: Long noncoding RNAs (lncRNAs) have been reported to play an important role in tumor immune modification. Nonetheless, the clinical implication of immune-associated lncRNAs in renal cell carcinoma (RCC) remains to be further explored. METHODS: 76 combinations of machine learning algorithms were integrated to develop and validate a machine learning-derived immune-related lncRNA signature (MDILS) in five independent cohorts (n = 801). We collected 28 published signatures and collated clinical variables for comparison with MDILS to verify its efficacy. Subsequently, molecular mechanisms, immune status, mutation landscape, and pharmacological profile were further investigated in different stratified patients. RESULTS: Patients with high MDILS displayed worse overall survival than those with low MDILS. The MDILS could independently predict overall survival and convey robust performance across five cohorts. MDILS has a significantly better performance compared with traditional clinical variables and 28 published signatures. Patients with low MDILS exhibited more abundant immune infiltration and higher potency of immunotherapeutic response, while patients with high MDILS might be more sensitive to multiple chemotherapeutic drugs (e.g., sunitinib and axitinib). CONCLUSION: MDILS is a robust and promising tool to facilitate clinical decision-making and precision treatment of RCC.

Identification and validation of SOCS1/2/3/4 as potential prognostic biomarkers and correlate with immune infiltration in glioblastoma.

Suppressor of cytokine signalling (SOCS) 1/2/3/4 are involved in the occurrence and progression of multiple malignancies; however, their prognostic and developmental value in patients with glioblastoma (GBM) remains unclear. The present study used TCGA, ONCOMINE, SangerBox3.0, UALCAN, TIMER2.0, GENEMANIA, TISDB, The Human Protein Atlas (HPA) and other databases to analyse the expression profile, clinical value and prognosis of SOCS1/2/3/4 in GBM, and to explore the potential development mechanism of action of SOCS1/2/3/4 in GBM. The majority of analyses showed that SOCS1/2/3/4 transcription and translation levels in GBM tissues were significantly higher than those in normal tissues. qRT-PCR, western blotting (WB) and immunohistochemical staining were used to verify that SOCS3 was expressed at higher mRNA and protein levels in GBM than in normal tissues or cells. High SOCS1/2/3/4 mRNA expression was associated with poor prognosis in patients with GBM, especially SOCS3. SOCS1/2/3/4 were highly contraindicated, which had few mutations, and were not associated with clinical prognosis. Furthermore, SOCS1/2/3/4 were associated with the infiltration of specific immune cell types. In addition, SOCS3 may affect the prognosis of patients with GBM through JAK/STAT signalling pathway. Analysis of the GBM-specific protein interaction (PPI) network showed that SOCS1/2/3/4 were involved in multiple potential carcinogenic mechanisms of GBM. In addition, colony formation, Transwell, wound healing and western blotting assays revealed that inhibition of SOCS3 decreased the proliferation, migration and invasion of GBM cells. In conclusion, the present study elucidated the expression profile and prognostic value of SOCS1/2/3/4 in GBM, which may provide potential prognostic biomarkers and therapeutic targets for GBM, especially SOCS3.

Systematic analysis of transcriptome signature for improving outcomes in lung adenocarcinoma.

PURPOSE: The updated guidelines highlight gene expression-based multigene panel as a critical tool to assess overall survival (OS) and improve treatment for lung adenocarcinoma (LUAD) patients. Nevertheless, genome-wide expression signatures are still limited in real clinical utility because of insufficient data utilization, a lack of critical validation, and inapposite machine learning algorithms. METHODS: 2330 primary LUAD samples were enrolled from 11 independent cohorts. Seventy-six algorithm combinations based on ten machine learning algorithms were applied. A total of 108 published gene expression signatures were collected. Multiple pharmacogenomics databases and resources were utilized to identify precision therapeutic drugs. RESULTS: We comprehensively developed a robust machine learning-derived genome-wide expression signature (RGS) according to stably OS-associated RNAs (OSRs). RGS was an independent risk element and remained robust and reproducible power by comparing it with general clinical parameters, molecular characteristics, and 108 published signatures. RGS-based stratification possessed different biological behaviors, molecular mechanisms, and immune microenvironment patterns. Integrating multiple databases and previous studies, we identified that alisertib was sensitive to the high-risk group, and RITA was sensitive to the low-risk group. CONCLUSION: Our study offers an appealing platform to screen dismal prognosis LUAD patients to improve clinical outcomes by optimizing precision therapy.

Epigenetically regulated gene expression profiles decipher four molecular subtypes with prognostic and therapeutic implications in gastric cancer.

BACKGROUND: Gastric cancer (GC) is one of the most common malignant tumors of the digestive tract which seriously endangers the health of human beings worldwide. Transcriptomic deregulation by epigenetic mechanisms plays a crucial role in the heterogeneous progression of GC. This study aimed to investigate the impact of epigenetically regulated genes on the prognosis, immune microenvironment, and potential treatment of GC. RESULTS: Under the premise of verifying significant co-regulation of the aberrant frequencies of microRNA (miRNA) correlated (MIRcor) genes and DNA methylation-correlated (METcor) genes. Four GC molecular subtypes were identified and validated by comprehensive clustering of MIRcor and METcor GEPs in 1521 samples from five independent multicenter GC cohorts: cluster 1 was characterized by up-regulated cell proliferation and transformation pathways, with good prognosis outcomes, driven by mutations, and was sensitive to 5-fluorouracil and paclitaxel; cluster 2 performed moderate prognosis and benefited more from apatinib and cisplatin; cluster 3 was featured by an up-regulated ligand-receptor formation-related pathways, poor prognosis, an immunosuppression phenotype with low tumor purity, resistant to chemotherapy (e.g., 5-fluorouracil, paclitaxel, and cisplatin), and targeted therapy drug (apatinib) and sensitive to dasatinib; cluster 4 was characterized as an immune-activating phenotype, with advanced tumor stages, benefit more from immunotherapy and displayed worst prognosis. CONCLUSIONS: According to the epigenetically regulated GEPs, we developed four robust GC molecular subtypes, which facilitated the understanding of the epigenetic mechanisms underlying GC heterogeneity, offering an optimized decision-making and surveillance platform for GC patients.

Multi-omics analyses of CD276 in pan-cancer reveals its clinical prognostic value in glioblastoma and other major cancer types.

BACKGROUND: CD276 (also known as B7-H3) is one of the most important immune checkpoints of the CD28 and B7 superfamily, and its abnormal expression is closely associated with various types of cancer. It has been shown that CD276 is able to inhibit the function of T cells, and that this gene may potentially be a promising immunotherapy target for different types of cancer. METHODS: Since few systematic studies have been published on the role of CD276 in cancer to date, the present study has employed single-cell sequencing and bioinformatics methods to analyze the expression patterns, clinical significance, prognostic value, epigenetic alterations, DNA methylation level, tumor immune cell infiltration and immune functions of CD276 in different types of cancer. In order to analyze the potential underlying mechanism of CD276 in glioblastoma (GBM) to assess its prognostic value, the LinkedOmics database was used to explore the biological function and co-expression pattern of CD276 in GBM, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. In addition, a simple validation of the above analyses was performed using reverse transcription-quantitative (RT-q)PCR assay. RESULTS: The results revealed that CD276 was highly expressed, and was often associated with poorer survival and prognosis, in the majority of different types of cancer. In addition, CD276 expression was found to be closely associated with T cell infiltration, immune checkpoint genes and immunoregulatory interactions between lymphoid and a non-lymphoid cell. It was also shown that the CD276 expression network exerts a wide influence on the immune activation of GBM. The expression of CD276 was found to be positively correlated with neutrophil-mediated immunity, although it was negatively correlated with the level of neurotransmitters, neurotransmitter transport and the regulation of neuropeptide signaling pathways in GBM. It is noteworthy that CD276 expression was found to be significantly higher in GBM compared with normal controls according to the RT-qPCR analysis, and the co-expression network, biological function and chemotherapeutic drug sensitivity of CD276 in GBM were further explored. In conclusion, the findings of the present study have revealed that CD276 is strongly expressed and associated with poor prognosis in most types of cancer, including GBM, and its expression is strongly associated with T-cell infiltration, immune checkpoint genes, and immunomodulatory interactions between lymphocytes and non-lymphoid cells. CONCLUSIONS: Taken together, based on our systematic analysis, our findings have revealed important roles for CD276 in different types of cancers, especially GBM, and CD276 may potentially serve as a biomarker for cancer.

Machine learning algorithm-generated and multi-center validated melanoma prognostic signature with inspiration for treatment management.

BACKGROUND: Although immunotherapy and targeted treatments have dramatically improved the survival of melanoma patients, the intra- or intertumoral heterogeneity and drug resistance have hindered the further expansion of clinical benefits. METHODS: The 96 combination frames constructed by ten machine learning algorithms identified a prognostic consensus signature based on 1002 melanoma samples from nine independent cohorts. Clinical features and 26 published signatures were employed to compare the predictive performance of our model. RESULTS: A machine learning-based prognostic signature (MLPS) with the highest average C-index was developed via 96 algorithm combinations. The MLPS has a stable and excellent predictive performance for overall survival, superior to common clinical traits and 26 collected signatures. The low MLPS group with a better prognosis had significantly enriched immune-related pathways, tending to be an immune-hot phenotype and possessing potential immunotherapeutic responses to anti-PD-1, anti-CTLA-4, and MAGE-A3. On the contrary, the high MLPS group with more complex genomic alterations and poorer prognoses is more sensitive to the BRAF inhibitor dabrafenib, confirmed in patients with BRAF mutations. CONCLUSION: MLPS could independently and stably predict the prognosis of melanoma, considered a promising biomarker to identify patients suitable for immunotherapy and those with BRAF mutations who would benefit from dabrafenib.

Gene interaction perturbation network deciphers a high-resolution taxonomy in colorectal cancer.

Molecular subtypes of colorectal cancer (CRC) are currently identified via the snapshot transcriptional profiles, largely ignoring the dynamic changes of gene expressions. Conversely, biological networks remain relatively stable irrespective of time and condition. Here, we introduce an individual-specific gene interaction perturbation network-based (GIN) approach and identify six GIN subtypes (GINS1-6) with distinguishing features: (i) GINS1 (proliferative, 24%~34%), elevated proliferative activity, high tumor purity, immune-desert, PIK3CA mutations, and immunotherapeutic resistance; (ii) GINS2 (stromal-rich, 14%~22%), abundant fibroblasts, immune-suppressed, stem-cell-like, SMAD4 mutations, unfavorable prognosis, high potential of recurrence and metastasis, immunotherapeutic resistance, and sensitive to fluorouracil-based chemotherapy; (iii) GINS3 (KRAS-inactivated, 13%~20%), high tumor purity, immune-desert, activation of EGFR and ephrin receptors, chromosomal instability (CIN), fewer KRAS mutations, SMOC1 methylation, immunotherapeutic resistance, and sensitive to cetuximab and bevacizumab; (iv) GINS4 (mixed, 10%~19%), moderate level of stromal and immune activities, transit-amplifying-like, and TMEM106A methylation; (v) GINS5 (immune-activated, 12%~24%), stronger immune activation, plentiful tumor mutation and neoantigen burden, microsatellite instability and high CpG island methylator phenotype, BRAF mutations, favorable prognosis, and sensitive to immunotherapy and PARP inhibitors; (vi) GINS6, (metabolic, 5%~8%), accumulated fatty acids, enterocyte-like, and BMP activity. Overall, the novel high-resolution taxonomy derived from an interactome perspective could facilitate more effective management of CRC patients.

Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer.

As the most aggressive tumor, the outcome of pancreatic cancer (PACA) has not improved observably over the last decade. Anatomy-based TNM staging does not exactly identify treatment-sensitive patients, and an ideal biomarker is urgently needed for precision medicine. Based on expression files of 1280 patients from 10 multicenter cohorts, we screened 32 consensus prognostic genes. Ten machine-learning algorithms were transformed into 76 combinations, of which we selected the optimal algorithm to construct an artificial intelligence-derived prognostic signature (AIDPS) according to the average C-index in the nine testing cohorts. The results of the training cohort, nine testing cohorts, Meta-Cohort, and three external validation cohorts (290 patients) consistently indicated that AIDPS could accurately predict the prognosis of PACA. After incorporating several vital clinicopathological features and 86 published signatures, AIDPS exhibited robust and dramatically superior predictive capability. Moreover, in other prevalent digestive system tumors, the nine-gene AIDPS could still accurately stratify the prognosis. Of note, our AIDPS had important clinical implications for PACA, and patients with low AIDPS owned a dismal prognosis, higher genomic alterations, and denser immune cell infiltrates as well as were more sensitive to immunotherapy. Meanwhile, the high AIDPS group possessed observably prolonged survival, and panobinostat may be a potential agent for patients with high AIDPS. Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment of PACA.

Artificial intelligence-driven consensus gene signatures for improving bladder cancer clinical outcomes identified by multi-center integration analysis.

To accurately predict the prognosis and further improve the clinical outcomes of bladder cancer (BLCA), we leveraged large-scale data to develop and validate a robust signature consisting of small gene sets. Ten machine-learning algorithms were enrolled and subsequently transformed into 76 combinations, which were further performed on eight independent cohorts (n = 1218). We ultimately determined a consensus artificial intelligence-derived gene signature (AIGS) with the best performance among 76 model types. In this model, patients with high AIGS showed a higher risk of mortality, recurrence, and disease progression. AIGS is not only independent of traditional clinical traits [(e.g., American Joint Committee on Cancer (AJCC) stage)] and molecular features (e.g., TP53 mutation) but also demonstrated superior performance to these variables. Comparisons with 58 published signatures also indicated that AIGS possessed the best performance. Additionally, the combination of AIGS and AJCC stage could achieve better performance. Patients with low AIGS scores were sensitive to immunotherapy, whereas patients with high AIGS scores might benefit from seven potential therapeutics: BRD-K45681478, 1S,3R-RSL-3, RITA, U-0126, temsirolimus, MRS-1220, and LY2784544. Additionally, some mutations (TP53 and RB1), copy number variations (7p11.2), and a methylation-driven target were characterized by AIGS-related multi-omics alterations. Overall, AIGS provides an attractive platform to optimize decision-making and surveillance protocol for individual BLCA patients.

The effect of resolvin D1 on bone regeneration in a rat calvarial defect model.

Resolvin D1 (RvD1) is a pro-resolving lipid mediator of inflammation, endogenously synthesized from omega-3 docosahexaenoic acid. The purpose of this study was to investigate the effect of RvD1 on bone regeneration using a rat calvarial defect model. Collagen 3D nanopore scaffold (COL) and Pluronic F127 hydrogel (F127) incorporated with RvD1 (RvD1-COL-F127 group) or COL and F127 (COL-F127 group) were implanted in symmetrical calvarial defects. After implantation, RvD1 was administrated subcutaneously every 7 days for 4 weeks. The rats were sacrificed at weeks 1 and 8 post-implantation. Tissue samples were analyzed by real-time reverse transcriptase-polymerase chain reaction and histology at week 1. Radiographical and histological analyses were done at week 8. At week 1, calvarial defects treated with RvD1 exhibited decreased numbers of inflammatory cells and tartrate-resistant acid phosphatase (TRAP) positive cells, greater numbers of newly formed blood vessels, upregulated gene expression of vascular endothelial growth factor and alkaline phosphatase, and downregulated gene expression of receptor activator of nuclear factor-κB ligand, interleukin-1ß and tumor necrosis factor-α. At week 8, the radiographical results showed that osteoid area fraction of the RvD1-COL-F127 group was higher than that of the COL-F127 group, and histological examination exhibited enhanced osteoid formation and newly formed blood vessels in the RvD1-COL-F127 group. In conclusion, this study showed that RvD1 enhanced bone formation and vascularization in rat calvarial defects.

Impact of Resolvin D1 on the inflammatory phenotype of periodontal ligament cell response to hypoxia.

OBJECTIVE: Periodontal ligament cells (PDLCs) are critical for wound healing and regenerative capacity of periodontal diseases. Within an inflammatory periodontal pocket, a hypoxic environment can aggravate periodontal inflammation, where PDLCs response to the inflammation would change. Resolvin D1 (RvD1) is an endogenous lipid mediator, which can impact intracellular inflammatory pathways of periodontal/oral cells and periodontal regeneration. It is not clear how hypoxia and RvD1 impact the inflammatory responses of pro-inflammatory PDLCs phenotype. Therefore, this study aimed to test hypoxia could induce changes in pro-inflammatory phenotype of PDLCs and RvD1 could reverse it. METHODS: Human PDLCs were cultured from periodontal tissues from eight healthy individuals and were characterized by immunofluorescence staining of vimentin and cytokeratin. Cell viability was examined by Methyl-thiazolyl-tetrazolium (MTT) assay. To examine the effects of hypoxia and RvD1 on the inflammatory responses of pro-inflammatory PDLCs phenotype, protein levels and gene expressions of inflammatory cytokines and signal transduction molecules were measured by enzyme-linked immunosorbent assay (ELISA), western blotting (WB), and real-time quantitative reverse transcription PCR (real-time qRT-PCR). Alizarin red S staining and real-time qRT-PCR were employed to study the effects of hypoxia and RvD1 on the osteogenic differentiation of pro-inflammatory PDLCs phenotype. RESULTS: It was found that hypoxia increases the expression of inflammatory factors at the gene level (p < .05). RvD1 reduced the expression of IL-1ß (p < .05) in PDLCs under hypoxia both at the protein and RNA levels. There were increases in the expression of p38 mitogen-activated protein kinase (p38 MAPK, p < .01) and protein kinase B (Akt, p < .05) in response to RvD1. Also, a significantly higher density of calcified nodules was observed after treatment with RvD1 for 21 days under hypoxia. CONCLUSION: Our results indicate that hypoxia up-regulated the inflammatory level of PDLCs. RvD1 can reduce under-hypoxia-induced pro-inflammatory cytokines in the inflammatory phenotype of PDLCs. Moreover, RvD1 promotes the calcium nodules in PDLCs, possibly by affecting the p38 MAPK signaling pathway through Akt and HIF-1α.

Gambogic acid suppresses nasopharyngeal carcinoma via rewiring molecular network of cancer malignancy and immunosurveillance.

Nasopharyngeal carcinoma (NPC) is a malignant tumor highly prevalent in Southeast Asia. The distant metastasis and disease recurrence are still unsolved clinical problems. In recent years, traditional Chinese medicine (TCM) monomers have become significantly attractive due to their advantages. Using high throughput drug sensitivity screening, we identified gambogic acid (GA) as a common TCM monomer displaying multiple anti-NPC effects. GA could effectively inhibit the proliferation of low differentiated cells and highly metastatic cells in NPC via inducing apoptosis and G2/M cell cycle arrest. In addition, GA obviously repressed the abilities of cell clone, migration, invasion, angiogenesis and represented satisfied synergistic effects combined with chemotherapy. Importantly, we found the elevated immune checkpoint CD47 stimulated after chemotherapy was dramatically impaired by GA treatment. Mechanically, the network pharmacology analyses unraveled that the oncogenic signaling pathways including STATs were rewired by GA treatment. Taken together, our study reveals a molecular basis and provides a rationale for GA application as the treatment regime in NPC therapy in future.

SOCS3 Acts as an Onco-immunological Biomarker With Value in Assessing the Tumor Microenvironment, Pathological Staging, Histological Subtypes, Therapeutic Effect, and Prognoses of Several Types of Cancer.

The suppressor of cytokine signaling (SOCS) family contains eight members, including SOCS1-7 and CIS, and SOCS3 has been shown to inhibit cytokine signal transduction in various signaling pathways. Although several studies have currently shown the correlations between SOCS3 and several types of cancer, no pan-cancer analysis is available to date. We used various computational tools to explore the expression and pathogenic roles of SOCS3 in several types of cancer, assessing its potential role in the pathogenesis of cancer, in tumor immune infiltration, tumor progression, immune evasion, therapeutic response, and prognostic. The results showed that SOCS3 was downregulated in most The Cancer Genome Atlas (TCGA) cancer datasets but was highly expressed in brain tumors, breast cancer, esophageal cancer, colorectal cancer, and lymphoma. High SOCS3 expression in glioblastoma multiforme (GBM) and brain lower-grade glioma (LGG) were verified through immunohistochemical experiments. GEPIA and Kaplan-Meier Plotter were used, and this bioinformatics analysis showed that high SOCS3 expression was associated with a poor prognosis in the majority of cancers, including LGG and GBM. Our analysis also indicated that SOCS3 may be involved in tumor immune evasion via immune cell infiltration or T-cell exclusion across different types of cancer. In addition, SOCS3 methylation was negatively correlated with mRNA expression levels, worse prognoses, and dysfunctional T-cell phenotypes in various types of cancer. Next, different analytical methods were used to select genes related to SOCS3 gene alterations and carcinogenic characteristics, such as STAT3, SNAI1, NFKBIA, BCL10, TK1, PGS1, BIRC5, TMC8, and AFMID, and several biological functions were identified between them. We found that SOCS3 was involved in cancer development primarily through the JAK/STAT signaling pathway and cytokine receptor activity. Furthermore, SOCS3 expression levels were associated with immunotherapy or chemotherapy for numerous types of cancer. In conclusion, this study showed that SOCS3 is an immune-oncogenic molecule that may possess value as a biomarker for diagnosis, treatment, and prognosis of several types of cancer in the future.