Soil microbial community compositions and metabolite profiles of Achnatherum inebrians affect phytoremediation potential in Cd contaminated soil.

Cadmium (Cd) contamination poses serious risks to soil ecosystems and human health. Herein, the effect of two drunken horse grasses (Achnatherum inebrians) including endophytes Epichloë gansuensis infected (E+ ) and uninfected (E-) on the phytoremediation of Cd-contaminated soils were analyzed by coupling high-throughput sequencing and soil metabolomics. The results showed that the high-risk soil Cd decreased and the medium- and low-risk Cd fraction increased to varying degrees after planting E+ and E- plants in the soil. Meanwhile, total Cd content decreased by 19.7 % and 35.1 % in E+ and E- A. inebrians-planted soils, respectively. Principal coordinate analysis revealed a significant impact of E+ and E- plants on the soil microbial community. Most stress-tolerant and gram-positive functional bacterial taxa were enriched to stabilize Cd(II) in E+ planted soil. Several beneficial fungal groups related to saprotroph and symbiotroph were enriched to absorb Cd(II) in E- soil. Soil metabolomic analysis showed that the introduction of A. inebrians could weaken the threat of CdCl2 to soil microbe metabolism and improve soil quality, which in turn promoted plant growth and improved phytoremediation efficiency in Cd-contaminated soil. In conclusion, A. inebrians plants alleviate soil Cd pollution by regulating soil microbial metabolism and microbial community structure. These results provide valuable information for an in-depth understanding of the phytoremediation mechanisms of A. inebrians.

Stenocarpella chrysopogonis, a New Species Causing Leaf Streak Disease of Chrysopogon zizanioides in Southern China.

Vetiver grass (Chrysopogon zizanioides) has been widely used in recent years for ecological environment management, restoration of degraded ecosystems, and essential oil extraction. In 2019, a leaf streak disease of C. zizanioides was observed in Zhanjiang, Guangdong Province, China. The disease appeared as large streak lesions on the leaves, on which conidiomata were formed. A pathogenicity test with the fungus isolated from these lesions confirmed Koch's postulates and thus the fungus as the causal agent of this disease. A morphological resemblance of the pathogen to Stenocarpella was noted upon microscopic examination. Phylogenetic trees inferred from both individual and combined ITS, LSU, and tef1 sequences confirmed the pathogen as a species of the Diaporthaceae and revealed it to be closely related to Phaeocytostroma and Stenocarpella species. As morphological characters clearly placed the pathogen in the genus Stenocarpella, it was described as S. chrysopogonis.

Development, efficacy and side effects of antibody­drug conjugates for cancer therapy (Review).

Antibody-drug conjugates (ADCs) are anticancer drugs that combine cytotoxic small-molecule drugs (payloads) with monoclonal antibodies through a chemical linker and that transfer toxic payloads to tumor cells expressing target antigens. All ADCs are based on human IgG. In 2009, the Food and Drug Administration (FDA) approved gemtuzumab ozogamicin as the initial first-generation ADC. Since then, at least 100 ADC-related projects have been initiated, and 14 ADCs are currently being tested in clinical trials. The limited success of gemtuzumab ozogamicin has led to the development of optimization strategies for the next generation of drugs. Subsequently, experts have improved the first-generation ADCs and have developed second-generation ADCs such as ado-trastuzumab emtansine. Second-generation ADCs have higher specific antigen levels, more stable linkers and longer half-lives and show great potential to transform cancer treatment models. Since the first two generations of ADCs have served as a good foundation, the development of ADCs is accelerating, and third-generation ADCs, represented by trastuzumab deruxtecan, are ready for wide application. Third-generation ADCs are characterized by strong pharmacokinetics and high pharmaceutical activity, and their drug-to-antibody ratio mainly ranges from 2 to 4. In the past decade, the research prospects of ADCs have broadened, and an increasing number of specific antigen targets and mechanisms of cytotoxic drug release have been discovered and studied. To date, seven ADCs have been approved by the FDA for lymphoma, and three have been approved to treat breast cancer. The present review explores the function and development of ADCs and their clinical use in cancer treatment.

Artificial Intelligence in microbiomes analysis: A review of applications in dermatology.

Microorganisms are closely related to skin diseases, and microbiological imbalances or invasions of exogenous pathogens can be a source of various skin diseases. The development and prognosis of such skin diseases are also closely related to the type and composition ratio of microorganisms present. Therefore, through detection of the characteristics and changes in microorganisms, the possibility for diagnosis and prediction of skin diseases can be markedly improved. The abundance of microorganisms and an understanding of the vast amount of biological information associated with these microorganisms has been a formidable task. However, with advances in large-scale sequencing, artificial intelligence (AI)-related machine learning can serve as a means to analyze large-scales of data related to microorganisms along with determinations regarding the type and status of diseases. In this review, we describe some uses of this exciting, new emerging field. In specific, we described the recognition of fungi with convolutional neural networks (CNN), the combined application of microbial genome sequencing and machine learning and applications of AI in the diagnosis of skin diseases as related to the gut-skin axis.

Advances in microbial metagenomics and artificial intelligence analysis in forensic identification.

Microorganisms, which are widely distributed in nature and human body, show unique application value in forensic identification. Recent advances in high-throughput sequencing technology and significant reductions in analysis costs have markedly promoted the development of forensic microbiology and metagenomics. The rapid progression of artificial intelligence (AI) methods and computational approaches has shown their unique application value in forensics and their potential to address relevant forensic questions. Here, we summarize the current status of microbial metagenomics and AI analysis in forensic microbiology, including postmortem interval inference, individual identification, geolocation, and tissue/fluid identification.

Comprehensive Analysis of Transcriptome and Metabolome Elucidates the Molecular Regulatory Mechanism of Salt Resistance in Roots of Achnatherum inebrians Mediated by Epichloë gansuensis.

Salinization of soil is a major environmental risk factor to plant functions, leading to a reduction of productivity of crops and forage. Epichloë gansuensis, seed-borne endophytic fungi, establishes a mutualistic symbiotic relationship with Achnatherum inebrians and confers salt tolerance in the host plants. In this study, analysis of transcriptome and metabolome was used to explore the potential molecular mechanism underlying the salt-adaptation of A. inebrians roots mediated by E. gansuensis. We found that E. gansuensis played an important role in the gene expression of the host's roots and regulated multiple pathways involved in amino acid metabolism, carbohydrate metabolism, TCA cycle, secondary metabolism, and lipid metabolism in the roots of A. inebrians. Importantly, E. gansuensis significantly induced the biological processes, including exocytosis, glycolytic process, fructose metabolic process, and potassium ion transport in roots of host plants at transcriptional levels, and altered the pathways, including inositol phosphate metabolism, galactose metabolism, starch, and sucrose metabolism at metabolite levels under NaCl stress. These findings provided insight into the molecular mechanism of salt resistance in roots of A. inebrians mediated by E. gansuensis and could drive progress in the cultivation of new salt-resistance breeds with endophytes.

Computer Image Analysis Reveals C-Myc as a Potential Biomarker for Discriminating between Keratoacanthoma and Cutaneous Squamous Cell Carcinoma.

The distinction between Keratoacanthoma (KA) and Cutaneous Squamous Cell Carcinoma (cSCC) is critical yet usually challenging to discriminate clinically and histopathologically. One approach to differentiate KA from cSCC is through assessing the immunohistochemical staining patterns of the three indicators, ß-catenin, C-Myc, and CyclinD1, which are critical molecules that play important roles in the Wnt/ß-catenin signaling pathway. Ki-67, as a proliferation biomarker for human tumor cells, was also assessed as an additional potential marker for differentiating KA from cSCC. In this report, these four indicators were analyzed in 42 KA and 30 cSCC cases with the use of the computer automated image analysis system. Computer automated image analysis is a time-based and cost-effective method of determining IHC staining in KA and cSCC samples. We found that C-Myc staining was predominantly localized in the nuclei of basal cells within KA patients, whereas cSCC staining was predominantly localized in the nuclei of diffuse cells. This C-Myc staining pattern has a sensitivity of 78.6% and a specificity of 66.7% for identifying KA. Moreover, positive rates of distinct expression patterns of C-Myc and Ki-67 may also serve as a means to clinically distinguish KA from cSCC. Taken together, our results suggest that these markers, in particular C-Myc, may be useful in differentiating KA from cSCC.

Prediction of Prognostic Risk Factors in Patients with Invasive Candidiasis and Cancer: A Single-Centre Retrospective Study.

Background: Invasive candidiasis is a common cancer-related complication with a high fatality rate. If patients with a high risk of dying in the hospital are identified early and accurately, physicians can make better clinical judgments. However, epidemiological analyses and mortality prediction models of cancer patients with invasive candidiasis remain limited. Method: A set of 40 potential risk factors was acquired in a sample of 258 patients with both invasive candidiasis and cancer. To begin, risk factors for Candida albicans vs. non-Candida albicans infections and persistent vs. nonpersistent Candida infections were analysed using classic statistical methods. Then, we applied three machine learning models (random forest, logistic regression, and support vector machine) to identify prognostic indicators related to mortality. Prediction performance of different models was assessed by precision, recall, F1 score, accuracy, and AUC. Results: Of the 258 patients both with invasive candidiasis and cancer included in the analysis. The median age of patients was 62 years, and 95 (36.82%) patients were older than 65 years, of which 178 (66.28%) were male. And 186 (72.1%) patients underwent surgery 2 weeks before data collection, 100 (39.1%) patients stayed in ICU during hospitalisation, 99 (38.4%) patients had bacterial blood infection, 85 (32.9%) patients had persistent invasive candidiasis, and 41 (15.9%) patients died within 30 days. The usage of drainage catheter and prolonged length of hospitalisation are the dominant risk factors for non-Candida albicans infections and persistent Candida infections, respectively. Risk factors, such as septic shock, history of surgery within the past 2 weeks, usage of drainage tubes, length of stay in ICU, total parenteral nutrition, serum creatinine level, fungal antigen, stay in ICU during hospitalisation, and total bilirubin level, were significant predictors of death. The RF model outperformed the LR and SVM models. Precision, recall, F1 score, accuracy, and AUC for RF were 64.29%, 75.63%, 69.23%, 89.61%, and 91.28%. Conclusions: In this study, the machine learning-based models accurately predicted the prognosis of cancer and invasive candidiasis patients. The algorithm could be used to help clinicians in high-risk patients' early intervention.

Machine-learning based prediction and analysis of prognostic risk factors in patients with candidemia and bacteraemia: a 5-year analysis.

Bacteraemia has attracted great attention owing to its serious outcomes, including deterioration of the primary disease, infection, severe sepsis, overwhelming septic shock or even death. Candidemia, secondary to bacteraemia, is frequently seen in hospitalised patients, especially in those with weak immune systems, and may lead to lethal outcomes and a poor prognosis. Moreover, higher morbidity and mortality associated with candidemia. Owing to the complexity of patient conditions, the occurrence of candidemia is increasing. Candidemia-related studies are relatively challenging. Because candidemia is associated with increasing mortality related to invasive infection of organs, its pathogenesis warrants further investigation. We collected the relevant clinical data of 367 patients with concomitant candidemia and bacteraemia in the first hospital of China Medical University from January 2013 to January 2018. We analysed the available information and attempted to obtain the undisclosed information. Subsequently, we used machine learning to screen for regulators such as prognostic factors related to death. Of the 367 patients, 231 (62.9%) were men, and the median age of all patients was 61 years old (range, 52-71 years), with 133 (36.2%) patients aged >65 years. In addition, 249 patients had hypoproteinaemia, and 169 patients were admitted to the intensive care unit (ICU) during hospitalisation. The most common fungi and bacteria associated with tumour development and Candida infection were Candida parapsilosis and Acinetobacter baumannii, respectively. We used machine learning to screen for death-related prognostic factors in patients with candidemia and bacteraemia mainly based on integrated information. The results showed that serum creatinine level, endotoxic shock, length of stay in ICU, age, leukocyte count, total parenteral nutrition, total bilirubin level, length of stay in the hospital, PCT level and lymphocyte count were identified as the main prognostic factors. These findings will greatly help clinicians treat patients with candidemia and bacteraemia.

Machine-learning based prediction of prognostic risk factors in patients with invasive candidiasis infection and bacterial bloodstream infection: a singled centered retrospective study.

BACKGROUND: Invasive candidal infection combined with bacterial bloodstream infection is one of the common nosocomial infections that is also the main cause of morbidity and mortality. The incidence of invasive Candidal infection with bacterial bloodstream infection is increasing year by year worldwide, but data on China is still limited. METHODS: We included 246 hospitalised patients who had invasive candidal infection combined with a bacterial bloodstream infection from January 2013 to January 2018; we collected and analysed the relevant epidemiological information and used machine learning methods to find prognostic factors related to death (training set and test set were randomly allocated at a ratio of 7:3). RESULTS: Of the 246 patients with invasive candidal infection complicated with a bacterial bloodstream infection, the median age was 63 years (53.25-74), of which 159 (64.6%) were male, 109 (44.3%) were elderly patients (> 65 years), 238 (96.7%) were hospitalised for more than 10 days, 168 (68.3%) were admitted to ICU during hospitalisation, and most patients had records of multiple admissions within 2 years (167/246, 67.9%). The most common blood index was hypoproteinemia (169/246, 68.7%), and the most common inducement was urinary catheter use (210/246, 85.4%). Moreover, the most frequently infected fungi and bacteria were Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis by machine learning method are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, C-Reactive protein (CRP), leukocyte count, neutrophil count, Procalcitonin (PCT), and total bilirubin level. CONCLUSION: Our results showed that the most common candida and bacteria infections were caused by Candida parapsilosis and Acinetobacter baumannii, respectively. The main predictors of death prognosis are serum creatinine level, age, length of stay, stay in ICU during hospitalisation, serum albumin level, CRP, leukocyte count, neutrophil count, PCT and total bilirubin level.

Elucidating the Molecular Mechanisms by which Seed-Borne Endophytic Fungi, Epichloë gansuensis, Increases the Tolerance of Achnatherum inebrians to NaCl Stress.

Seed-borne endophyte Epichloë gansuensis enhance NaCl tolerance in Achnatherum inebrians and increase its biomass. However, the molecular mechanism by which E. gansuensis increases the tolerance of host grasses to NaCl stress is unclear. Hence, we firstly explored the full-length transcriptome information of A. inebrians by PacBio RS II. In this work, we obtained 738,588 full-length non-chimeric reads, 36,105 transcript sequences and 27,202 complete CDSs from A. inebrians. We identified 3558 transcription factors (TFs), 15,945 simple sequence repeats and 963 long non-coding RNAs of A. inebrians. The present results show that 2464 and 1817 genes were differentially expressed by E. gansuensis in the leaves of E+ and E- plants at 0 mM and 200 mM NaCl concentrations, respectively. In addition, NaCl stress significantly regulated 4919 DEGs and 502 DEGs in the leaves of E+ and E- plants, respectively. Transcripts associated with photosynthesis, plant hormone signal transduction, amino acids metabolism, flavonoid biosynthetic process and WRKY TFs were differentially expressed by E. gansuensis; importantly, E. gansuensis up-regulated biology processes (brassinosteroid biosynthesis, oxidation-reduction, cellular calcium ion homeostasis, carotene biosynthesis, positive regulation of proteasomal ubiquitin-dependent protein catabolism and proanthocyanidin biosynthesis) of host grass under NaCl stress, which indicated an increase in the ability of host grasses' adaptation to NaCl stress. In conclusion, our study demonstrates the molecular mechanism for E. gansuensis to increase the tolerance to salt stress in the host, which provides a theoretical basis for the molecular breed to create salt-tolerant forage with endophytes.

LncRNA BASP1-AS1 interacts with YBX1 to regulate Notch transcription and drives the malignancy of melanoma.

Melanoma is a fatal skin malignant tumor with a poor prognosis. We found that long noncoding RNA BASP1-AS1 is essential for the development and prognosis of melanoma. The methylation, RNA sequencing, copy number variation, mutation data, and sample follow-up information of melanoma from The Cancer Genome Atlas (TCGA) were analyzed using weighted gene co-expression network analysis and 366 samples common to the three omics were selected for multigroup clustering analysis. A four-gene prognostic model (BASP1-AS1, LOC100506098, ARHGAP27P1, and LINC01532) was constructed in the TCGA cohort and validated using the GSE65904 series. The expression of BASP1-AS1 was upregulated in melanoma tissues and various melanoma cell lines. Functionally, the ectopic expression of BASP1-AS1 promoted cell proliferation, migration, and invasion in both A375 and SK-MEL-2 cells. Mechanically, BASP1-AS1 interacted with YBX1 and recruited it to the promoter of NOTCH3, initiating its transcription process. The activation of the Notch signaling then resulted in the transcription of multiple oncogenes, including c-MYC, PCNA, and CDK4, which contributed to melanoma progression. Thus, BASP1-AS1 could act as a potential biomarker for cutaneous malignant melanoma.

A 5-Year Review of Invasive Fungal Infection at an Academic Medical Center.

Background: Invasive fungal infection (IFI) is one of the most common nosocomial infections. However, data on the epidemiology of IFI and susceptibility to antifungal agents in China are quite limited, and in particular, no current data exist on the microbiological, and clinical characteristics of IFI patients in Northeast China. Objectives: The purpose of this study was to provide a retrospective review of the clinical characteristics, laboratory test results, and risk factor predictions of inpatients diagnosed with IFI. Multivariate regression analysis was used to assess prognostic factors associated with the mortality of these patients. Methods: We retrospectively analyzed the results from 509 patients with IFI extracted from the First Hospital of China Medical University from January 2013 to January 2018. Results: Neutrophil numbers, total bilirubin, length of stay in the ICU, renal failure, use of immunosuppressants within the past 30 days, stomach tube placement and septic shock were risk factors for death from IFI. Recent surgery (within 2 weeks) and drainage tube placement did not increase mortality in these IFI patients. Increased serum levels of PCT (AUC 0.601, 95% CI 0.536-0.665, P = 0.003) and CRP (AUC 0.578, 95% CI 0.512-0.644, P = 0.020) provided effective predictors of 30-day mortality rates. Conclusions: We report for the first time epidemiological data on invasive fungal infections in Northeast China over the past 5 years. Despite the limited available clinical data, these findings will greatly aid clinical health care workers with regard to the identification, prevention, and treatment of IFI in hospitalized patients.

Identification of Subtype-Specific Metastasis-Related Genetic Signatures in Sarcoma.

Background: Sarcomas are heterogeneous rare malignancies constituting approximately 1% of all solid cancers in adults and including more than 70 histological and molecular subtypes with different pathological and clinical development characteristics. Method: We identified prognostic biomarkers of sarcomas by integrating clinical information and RNA-seq data from TCGA and GEO databases. In addition, results obtained from cell cycle, cell migration, and invasion assays were used to assess the capacity for Tanespimycin to inhibit the proliferation and metastasis of sarcoma. Results: Sarcoma samples (N = 536) were divided into four pathological subtypes including DL (dedifferentiated liposarcoma), LMS (leiomyosarcoma), UPS (undifferentiated pleomorphic sarcomas), and MFS (myxofibrosarcoma). RNA-seq expression profile data from the TCGA dataset were used to analyze differentially expressed genes (DEGs) within metastatic and non-metastatic samples of these four sarcoma pathological subtypes with DEGs defined as metastatic-related signatures (MRS). Prognostic analysis of MRS identified a group of genes significantly associated with prognosis in three pathological subtypes: DL, LMS, and UPS. ISG15, NUP50, PTTG1, SERPINE1, and TSR1 were found to be more likely associated with adverse prognosis. We also identified Tanespimycin as a drug exerting inhibitory effects on metastatic LMS subtype and therefore can serve a potential treatment for this type of sarcoma. Conclusions: These results provide new insights into the pathogenesis, diagnosis, treatment, and prognosis of sarcomas and provide new directions for further study of sarcoma.

DnaJA4 is involved in responses to hyperthermia by regulating the expression of F-actin in HaCaT cells.

BACKGROUND: Hyperthermia in combination with DnaJA4-knockout (KO) obviously affects the anti-viral immunity of HaCaT cells. The mechanisms of this process are not yet fully explored. However, it is known that DnaJA4 interacts with actin cytoskeleton after hyperthermia. Our aim was to investigate the effects of DnaJA4 on F-actin in HaCaT cells following hyperthermia. METHODS: Wild-type (WT) and DnaJA4-KO HaCaT cells were isolated at either 37°C (unheated) or 44°C (hyperthermia) for 30 min followed by testing under conditions of 37°C and assessing at 6, 12, and 24 h after hyperthermia. The cytoskeleton was observed with immunofluorescence. Flow cytometry and Western blotting were used to detect the expression of F-actin and relevant pathway protein. RESULTS: DnaJA4-KO and hyperthermia changed the cytoskeleton morphology of HaCaT cells. F-actin expression levels were elevated in DnaJA4-KO cells compared with WT cells (6364.33 ±â€Š989.10 vs. 4272.67 ±â€Š918.50, P < 0.05). In response to hyperthermia, F-actin expression levels of both WT and DnaJA4-KO cells showed a tendency to decrease followed by an obvious recovery after hyperthermia (WT cells: unheated vs. 6 h after hyperthermia or 24 h after hyperthermia: 0.34 ±â€Š0.02 vs. 0.24 ±â€Š0.01, 0.31 ±â€Š0.01, P < 0.001, P < 0.05; DnaJA4-KO cells: unheated vs. 6 h after hyperthermia or 24 h after hyperthermia: 0.44 ±â€Š0.01 vs. 0.30 ±â€Š0.01, 0.51 ±â€Š0.02, P < 0.001, P < 0.01). WT cells restored to baseline levels observed in the unheated condition, while DnaJA4-KO cells exceeded baseline levels in the recovery. As the upstream factors of F-actin, a similar profile in rho-associated serine/threonine kinase 1 (ROCK 1) and RhoA expressions was observed after hyperthermia. While E-cadherin expression was decreased in response to hyperthermia, it was increased in DnaJA4-KO cells compared with WT cells. CONCLUSIONS: Hyperthermia affects the expression levels of F-actin in HaCaT cells. DnaJA4 knockout increases the expression of F-actin in HaCaT cells after hyperthermia. DnaJA4 regulates the expressions of F-actin and the related pathway proteins in response to hyperthermia in HaCaT cells.

IL­18 knockout alleviates atopic dermatitis­like skin lesions induced by MC903 in a mouse model.

Interleukin (IL)­18, a pro­inflammatory cytokine, plays an important role in a number of skin diseases. The aim of the present study was to investigate the role of IL­18 in the development of atopic dermatitis (AD). For this purpose, mice were divided into 4 groups (n=5/group) as follows: i) The wild­type (WT) controls; ii) IL18 knockout (KO) controls; iii) MC903­treated WT mice; and iv) MC903­treated KO mice. MC903 (4 nmol in ethanol) was topically applied daily for 15 consecutive days to the exposed skins of mice. AD­like symptoms and severity were evaluated by the scoring of AD (SCORAD). Serum immunoglobulin E (IgE) and thymic stromal lymphopoietin (TSLP) levels were determined with the use of an enzyme­linked immunosorbent assay. Immunohistochemistry was used to assess the expression of IL­1ß, signal transducer and activator of transcription (STAT)3 and filaggrin (FLG) in the skin lesions. RT­qPCR was performed to assess the mRNA levels of IL­1ß, IL­4, IL­9, STAT3, corticotropin­releasing hormone receptor (CRHR)1, CRHR2, TSLP and caspase­1 in the skin lesions. It was demonstrated that IL­18 may function as a pleiotropic pro­inflammatory cytokine in the development of AD­like lesions. IL­18 KO reduced aggravated AD­like lesions induced by MC903, in part by upregulating Th2 cytokines. IL­18 promoted the expression of FLG in the epidermis and CRHR2 in AD­like lesions, but downregulated the serum levels of IgE. On the whole, the findings of the present study demonstrate that IL­18 deficiency alleviates AD­induced lesions in mice.

Identification of differentially expressed methylated genes in melanoma versus nevi using bioinformatics methods.

BACKGROUND: Melanoma is a highly invasive malignant skin tumor. While melanoma may share some similarities with that of melanocytic nevi, there also exist a number of distinct differences between these conditions. An analysis of these differences may provide a means to more effectively evaluate the etiology and pathogenesis of melanoma. In particular, differences in aberrant methylation expression may prove to represent a critical distinction. METHODS: Data from gene expression datasets (GSE3189 and GSE46517) and gene methylation datasets (GSE86355 and GSE120878) were downloaded from the GEO database. GEO2R was used to obtain differentially expressed genes (DEGs) and differentially methylation genes (DMGs). Function and pathway enrichment of selected genes were performed using the DAVID database. A protein-protein interaction (PPI) network was constructed by STRING while its visualization was achieved with use of cytoscape. Primary melanoma samples from TCGA were used to identify significant survival genes. RESULTS: There was a total of 199 genes in the hypermethylation-low expression group, while 136 genes in the hypomethylation-high expression group were identified. The former were enriched in the biological processes of transcription regulation, RNA metabolism and regulation of cell proliferation. The later were highly involved in cell cycle regulation. 13 genes were screened out after survival analysis and included: ISG20, DTL, TRPV2, PLOD3, KIF3C, DLGAP4, PI4K2A, WIPI1, SHANK2, SLC16A10, GSTA4O, LFML2A and TMEM47. CONCLUSION: These findings reveal some of the methylated differentially expressed genes and pathways that exist between melonoma and melanocytic nevi. Moreover, we have identified some critical genes that may help to improve the diagnosis and treatment of melanoma.

Identification and Validation of Prognostically Relevant Gene Signature in Melanoma.

BACKGROUND: Currently, effective genetic markers are limited to predict the clinical outcome of melanoma. High-throughput multiomics sequencing data have provided a valuable approach for the identification of genes associated with cancer prognosis. METHOD: The multidimensional data of melanoma patients, including clinical, genomic, and transcriptomic data, were obtained from The Cancer Genome Atlas (TCGA). These samples were then randomly divided into two groups, one for training dataset and the other for validation dataset. In order to select reliable biomarkers, we screened prognosis-related genes, copy number variation genes, and SNP variation genes and integrated these genes to further select features using random forests in the training dataset. We screened for robust biomarkers and established a gene-related prognostic model. Finally, we verified the selected biomarkers in the test sets (GSE19234 and GSE65904) and on clinical samples extracted from melanoma patients using qRT-PCR and immunohistochemistry analysis. RESULTS: We obtained 1569 prognostic-related genes and 1101 copy-amplification, 1093 copy-deletions, and 92 significant mutations in genomic variants. These genomic variant genes were closely related to the development of tumors and genes that integrate genomic variation. A total of 141 candidate genes were obtained from prognosis-related genes. Six characteristic genes (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) were selected by random forest feature selection, many of which have been reported to be associated with tumor progression. Cox regression analysis was used to establish a 6-gene signature. Experimental verification with qRT-PCR and immunohistochemical staining proved that these selected genes were indeed expressed at a significantly higher level compared with the normal tissues. This signature comprised an independent prognostic factor for melanoma patients. CONCLUSIONS: We constructed a 6-gene signature (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) as a novel prognostic marker for predicting the survival of melanoma patients.