Co-Expression Network Analysis Identified LTF in Association with Metastasis Risk and Prognosis in Clear Cell Renal Cell Carcinoma

Development and validation of a neutrophil extracellular traps-related gene signature for lower-grade gliomas

There is growing evidence linking neutrophil extracellular traps (NETs) to tumor genesis, growth, distant metastasis, and tumor-related thrombosis. However, the roles of NETs-related genes (NETRGs) on LGG prognosis remain unclear. The purpose of this study was to integrate multiple machine learning techniques and experiment validation to develop a reliable NETs-based signature that opens up novel approaches for assessing the prognosis and treatment response of LGG patients. Consensus clustering, k-means clustering and Nonnegative Matrix Factorization was used for the TCGA-LGG dataset and identified two NETs-related subgroups. The prognostic hallmark and nomogram for LGG were developed, which consist of five differentially expressed NETRGs (FPR1, PTAFR, SLC11A1, ICAM1, LTF) based on nine analytic approaches. The ROC curves and calibration curves of our NETRGs signature and nomogram exhibited strong and robust prognosis prediction abilities in both the TCGA-LGG training set and CGGA-325, CGGA-693 validation sets. The prognosis for LGG individuals in the low-risk category was better. The TISCH was used to examine the five NETRGs at the single-cell level. Common immunological checkpoints were expressed at greater levels in high-risk individuals. LGG individuals in the low-risk category posses a higher likelihood of being sensitive to Carmustine and Vincristine, as indicated by the drug sensitivity analysis. The qRT-PCR experiment and immunohistochemistry images confirmed that the expression of FPR1, PTAFR, SLC11A1 and ICAM1 are higher in low-grade oligodendroglioma. The NETRGs signature and nomogram can accurately and conveniently predict the LGG patients' prognosis, which can facilitate individualized treatment and the improvement of prognosis.

LTF as a Potential Predictive Biomarker for Durable Benefit From First-Line Chemo-Immunotherapy in Small Cell Lung Cancer

At present, only a limited fraction of patients with extensive-stage small cell lung cancer (ES-SCLC) achieve a sustained response to immune checkpoint blockade (ICB) therapy. The factors that drive therapeutic efficacy remain poorly delineated, and the field is devoid of reliable predictive biomarkers to guide personalized treatment decisions. Therefore, we conducted RNA sequencing of tumor samples from 21 patients prior to treatment to identify expression patterns associated with lasting benefit and used weighted gene co-expression network analysis (WGCNA) to identify key genes associated with favorable outcomes of chemotherapeutic immunotherapy. Multiplex immunofluorescence (mIF) quantification and reanalysis of publicly available datasets were used to validate the hub gene's association with the immune microenvironment and immunotherapy efficacy. The functional significance of the hub gene was further investigated in cellular models. We found that the durable clinical benefit (DCB) group exhibited significantly elevated levels of inflammation and interferon response compared to the no-durable benefit (NDB) group, alongside a notably lower proportion of Tregs and distinct metabolic features. Lactotransferrin (LTF) was identified as a hub gene associated with durable therapeutic benefits in chemo-immunotherapy. By further analysis, we proved that LTF acts as a tumor suppressor in small cell lung cancer, impacting cell proliferation, migration, and invasiveness. It also inhibits lipid metabolism in these cells. Elevated LTF expression is linked to better chemo-immunotherapy outcomes, suggesting its potential as a predictive biomarker for first-line treatment response in ES-SCLC.

THSWD upregulates the LTF/AMPK/mTOR/Becn1 axis and promotes lysosomal autophagy in hepatocellular carcinoma cells by regulating gut flora and metabolic reprogramming

THSWD has the effect of reducing inflammation, improving microcirculation, and regulating immune status in patients with hepatocellular carcinoma. Regardless of its clear therapeutic effect, the underlying mechanism of action against hepatocellular carcinoma is not clear. To identify critical gut microbiota and its associated metabolites related to THSWD inhibition against hepatocellular carcinoma progression, we assessed the microbe-dependent anti-hepatocellular carcinoma effects of THSWD through 16 s rRNA gene sequencing, fecal microbial transplantation and antibiotic treatment. Metabolic analyses, transcriptomic analyses, and molecular experiments were performed to explore how THSWD modulates the gut microbiota against hepatocellular carcinoma progression. As confirmed by in vivo and in vitro assays, THSWD reduced tumour growth rate and promoted apoptosis in hepatocellular carcinoma cells in hepatocellular carcinoma model mice, and liver and kidney indexes were detected and confirmed the safety of THSWD. Transcriptomic analysis revealed that the targets of THSWD were significantly enriched in multiple lysosomal autophagy signalling pathways, suggesting that lysosomal autophagy is probably associated with THSWD's therapeutic effect. Based on the integrated data analysis, THSWD delays hepatocellular carcinoma progression by increasing the intestinal microbiota Duncaniella and augmenting the metabolite glabrol, and the joint analysis of metabolic and genomic data suggests that this metabolite is associated with lysosomal autophagy, and cellular experiments confirmed that the The differential metabolite glabrol induces apoptosis in hepatocellular carcinoma cells by triggering the lysosomal autophagy-mediated apoptosis signalling pathway. Supplementation with glabrol metabolites up regulates the LTF/AMPK/mTOR/Beclin1 axis and promotes hepatocellular carcinoma cells with lysosomal autophagy and induced apoptosis in hepatocellular carcinoma cells.

Explorations of novel MDR-related hub genes and the potential roles TRIM9 played in drug-resistant hepatocellular carcinoma

Current chemotherapeutic efficacy is limited by the rapid development of multidrug resistance (MDR) in hepatocellular carcinoma (HCC). In this study, 66 MDR-related hub genes in drug-resistant HCC were identified through combined analysis of differential expressed genes (DEGs), gene functional enrichment, Cox proportional regression, weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network construction. A prognostic risk model was established through the LASSO-Cox regression analysis. Based on the comparison of gene mutation frequency, tumor mutation burden (TMB) and immune infiltration in high- and low-risk groups, we explored the relationships between the MDR-related hub genes and immune regulation. The competitive endogenous RNA (ceRNA) network and associated non-coding RNAs (ncRNAs) were predicted to investigate the potential mechanisms. Five MDR-related hub genes in drug-resistant HCC were finally confirmed, namely ABCB6, FLNC, MCC, NAV3 and TRIM9. TRIM9 was identified as the most significant gene enhancing MDR. Inhibiting TRIM9 caused a decrease in the IC50 of doxorubicin (DOX), and significant increases in the intracellular uptake, retention and absorption of DOX in HepG2/ADR cells. These findings may provide new insights into the mechanism of MDR development. The MDR-related hub genes, especially TRIM9 may be targeted therapeutically to enhance the prognosis of patients with drug-resistant HCC.

Sensitivity of renal cell carcinoma to cuproptosis: a bioinformatics analysis and experimental verification

Purpose: Targeting cuproptosis is considered as a promising therapeutic strategy for the prevention of tumors. However, the potential role of cuproptosis and its related genes in clear cell renal cell carcinoma (ccRCC) remains elusive. The present study aims to explore the sensitivity of ccRCC to cuproptosis and its underlying mechanism. Methods: Cuproptosis differential genes (CDGs) were extracted using the GSE53757 and GSE66272 datasets. A comprehensive analysis of the role of CDGs was conducted through multiple public databases and experiments. Results: It was found that cuproptosis inducer elesclomol significantly induced cell death in 786-O and A498 cells. FDX and DLAT exhibited significantly low expression, which were independent prognostic factors for poor survival, and had a strong positive correlation in ccRCC patients. Functional analysis of differentially expressed genes positively or negatively correlated with both FDX1 and DLAT indicated that acetyl-CoA biosynthetic process and acetyl-CoA metabolic process were remarkably affected. In ccRCC patients, the methylation levels and sites of FDX1 and DLAT genes were dramatically correlated with overall survival (OS). The expressions of FDX1 and DLAT were closely related to immune infiltration and immune checkpoints. Docking results indicated that mitotane, adicicol and dihydrolipoic acid might be potential drug targets for FDX1 and DLAT. Conclusions: Overall, the present study demonstrates the sensitivity of ccRCC to cuproptosis, and targeting the combination of FDX1 and DLAT may be a novel therapeutic strategy to induce cuproptosis in ccRCC.

Identification of potential core genes in lung cancer and therapeutic traditional Chinese medicine compounds using bioinformatics analysis

Lung cancer (LC) remains the leading cause of cancer-related death. We identified potential therapeutic targets and traditional Chinese medicine (TCM) compounds for LC treatment. GSE43346 and GSE18842 were derived from the Gene Expression Omnibus (GEO) database and used to identify differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Protein-protein interactions were analyzed using STRING and Cytoscape software. Hub gene expression was validated using Gene Expression Profiling Interactive Analysis and the Human Protein Atlas. Kaplan-Meier survival analysis was conducted to evaluate the prognostic value of hub genes in patients with LC. Therapeutic TCM compounds were screened using the Comparative Toxicogenomics Database, and DEGs were largely enriched in biological processes, including cell division and mitotic nuclear division, such as the cell cycle and p53 signaling pathways. Elevated expression of hub genes was observed in LC samples. Overexpression of CDC20, CCNB2, and TOP2A is an unfavorable prognostic factor for postprogressive survival in patients with LC. Paclitaxel, quercetin, and rotenone have been identified as active substances in TCM. CDC20, CCNB2, and TOP2A are novel hub genes associated with LC. Paclitaxel, quercetin, and rotenone can be used as therapeutic agents in TCM.

LTF as a Potential Prognostic and Immunological Biomarker in Glioblastoma

The lactoferrin (LTF) gene behaves like a tumor suppressor gene in diverse tumors, such as renal cancer, nasopharyngeal carcinoma and gastric cancer. However, the prognostic value of LTF expression in patients with glioblastoma remains unclear. In this study, the expression levels of LTF in patients with GBM were investigated in TCGA, GEPIA, CGGA and GEO database, and a survival analysis of LTF based on TCGA and CGGA was performed. Furthermore, the present study demonstrated the LTF gene co-expression, PPI network, KEGG/GO enrichment and immune cell infiltration analysis on TCGA and TIMER2.0 database. We found that LTF expression was significantly upregulated in GBM samples compared with normal samples and other glioma samples, and Kaplan-Meier analysis demonstrated that the overexpression of LTF were significantly associated with worse overall survival (OS) and 5-year OS in GBM patients (P < 0.05). KEGG/GO enrichment analysis demonstrated that functions of LTF concentrated in immune and inflammatory response and peptidase regulation (P < 0.05). Immune cell infiltration analysis presented that high LTF expression exhibited dysregulated immune infiltration (i.e., CD4 + T cells, neutrophils, macrophages, myeloid dendritic cells and cancer associated fibroblast). LTF was upregulated in tumors and correlated with worse OS in GBM patients, and LTF might function as an oncogene via inducing dysregulated immune infiltration in GBM.

Global analysis of iron metabolism-related genes identifies potential mechanisms of gliomagenesis and reveals novel targets

AIMS

This study aimed to investigate key regulators of aberrant iron metabolism in gliomas, and evaluate their effect on biological functions and clinical translational relevance.

METHODS

We used transcriptomic data from multiple cross-platform glioma cohorts to identify key iron metabolism-related genes (IMRGs) based on a series of bioinformatic and machine learning methods. The associations between IMRGs and prognosis, mesenchymal phenotype, and genomic alterations were analyzed in silico. The performance of the IMRGs-based signature in predicting temozolomide (TMZ) treatment sensitivity was evaluated. In vitro and in vivo experiments were used to explore the biological functions of these key IMRGs.

RESULTS

HMOX1, LTF, and STEAP3 were identified as the most essential IMRGs in gliomas. The expression levels of these genes were strongly related to clinicopathological and molecular features. The robust IMRG-based gene signature could be used for prognosis prediction. These genes facilitate mesenchymal transformation, driver gene mutations, and oncogenic alterations in gliomas. The gene signature was also associated with TMZ resistance. HMOX1, LTF, and STEAP3 knockdown in glioma cells significantly reduced cell proliferation, colony formation, migration, and malignant invasion.

CONCLUSION

The study presented a comprehensive view of key regulators underpinning iron metabolism in gliomas and provided new insights into novel therapeutic approaches.

Altered profile of glycosylated proteins in serum samples obtained from patients with Hashimoto's thyroiditis following depletion of highly abundant proteins

Objectives

Hashimoto's thyroiditis (HT) is one of the most common autoimmune disorders; however, its underlying pathological mechanisms remain unclear. Although aberrant glycosylation has been implicated in the N-glycome of immunoglobulin G (IgG), changes in serum proteins have not been comprehensively characterized. This study aimed to investigate glycosylation profiles in serum samples depleted of highly abundant proteins from patients with HT and propose the potential functions of glycoproteins for further studies on the pathological mechanisms of HT.

Methods

A lectin microarray containing 70 lectins was used to detect and analyze glycosylation of serum proteins using serum samples (N=27 HT; N=26 healthy control [HC]) depleted of abundant proteins. Significant differences in glycosylation status between HT patients and the HC group were verified using lectin blot analysis. A lectin-based pull-down assay combined with mass spectrometry was used to investigate potential glycoproteins combined with differentially present lectins, and an enzyme-linked immunosorbent assay (ELISA) was used to identify the expression of targeted glycoproteins in 131 patients with papillary thyroid carcinoma (PTC), 131 patients with benign thyroid nodules (BTN) patients, 130 patients with HT, and 128 HCs.

Results

Compared with the HC group, the majority of the lectin binding signals in HT group were weakened, while the Vicia villosa agglutinin (VVA) binding signal was increased. The difference in VVA binding signals verified by lectin blotting was consistent with the results of the lectin microarray. A total of 113 potential VVA-binding glycoproteins were identified by mass spectrometry and classified by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses. Using ELISA, we confirmed that lactoferrin (LTF) and mannan-binding lectin-associated serine protease 1 (MASP-1) levels were elevated in the serum of patients with HT and PTC.

Conclusion

Following depletion of abundant proteins, remaining serum proteins in HT patients exhibited lower glycosylation levels than those observed in HCs. An increased level of potential VVA-binding glycoproteins may play an important role in HT development. LTF and MASP-1 expression was significantly higher in the serum of HT and PTC patients, providing novel insight into HT and PTC.

The Potential Diagnostic Value of Immune-Related Genes in Interstitial Fibrosis and Tubular Atrophy after Kidney Transplantation

Background

Inflammation within areas of interstitial fibrosis and tubular atrophy (IF/TA) is associated with kidney allograft failure. The aim of this study was to reveal new diagnostic markers of IF/TA based on bioinformatics analysis.

Methods

Raw data of IF/TA samples after kidney transplantation and control samples after kidney transplantation were extracted from the Gene Expression Omnibus (GEO) database (GSE76882 and GSE120495 datasets), and genes that were differentially expressed between the two groups (DEGs) were screened. Gene Set Enrichment Analysis (GSEA), ESTIMATE and single sample GSEA (ssGSEA), least absolute shrinkage and selection operator (LASSO) regression analysis, and competing endogenous RNA (ceRNA) network were used to analyze the data.

Results

The results of GSEA revealed that multiple immune-related pathways were enriched in the IF/TA group, and subsequent immune landscape analysis also showed that the IF/TA group had higher immune and stromal scores and up to 15 types of immune cells occupied them, such as B cells, cytotoxic cells, and T cells. LASSO regression analysis selected 6 (including ANGPTL3, APOH, LTF, FCGR2B, HLA-DQA2, and EGF) out of 14 DE-IRGs as diagnostic genes to construct a diagnostic model. Then, receiver operating characteristic (ROC) curve analysis showed the powerful diagnostic value of the model, and the area under the curve (AUC) of a single diagnostic gene was greater than 0.75. The results of ingenuity pathway analysis (IPA) also indicated that DEGs were involved in the immune system and kidney disease-related pathways. Finally, we found multiple miRNAs that could regulate diagnostic genes from the ceRNA network.

Conclusion

This study identified 6 IF/TA-related genes, which might be used as a new diagnosis model in the clinical practice.

Integrated bioinformatics analysis to identify the key gene associated with metastatic clear cell renal cell carcinoma

Metastasis of clear cell renal cell carcinoma (ccRCC) is a leading cause of death. The purpose of this research was to investigate the key gene in ccRCC tumor metastasis. Three microarray datasets (GSE22541, GSE85258, and GSE105261), which included primary and metastatic ccRCC tissues, were obtained from the Gene Expression Omnibus (GEO) database. Expression profiling and clinical data of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) dataset. A total of 20 overlapping differentially expressed genes (DEGs) were identified using the R limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the DEGs were mainly enriched in tumor metastasis-related pathways. Gene expression analysis and survival analysis in the GEPIA2 database further identified the key gene HSD11B2. qRT-PCR result manifested that HSD11B2 level was significantly down-regulated in ccRCC tissues compared with adjacent normal tissues. ROC analysis showed that HSD11B2 exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Univariate and multivariate Cox regression analysis showed that HSD11B2 expression was an independent prognostic factor. To establish a nomogram combining HSD11B2 expression and clinical factors, and a new method for predicting the survival probability of ccRCC patients. Gene Set Enrichment Analysis (GSEA) enrichment results showed that low expression of HSD11B2 was mainly enriched in tumor signaling pathways and immune-related pathways. Immune analysis revealed a significant correlation between HSD11B2 and tumor immune infiltrates in ccRCC. This study suggests that HSD11B2 can serve as a potential biomarker and therapeutic target for ccRCC metastasis.

Lactotransferrin promotes intervertebral disc degeneration by regulating Fas and inhibiting human nucleus pulposus cell apoptosis

Background: In recent years, intervertebral disc (IVD) degeneration (IDD) has increased in age. There is still a lack of effective treatment in clinics, which cannot improve the condition of IDD at the level of etiology.

Objective: To explore IDD pathogenesis at the cellular and gene levels and investigate lactotransferrin (LTF) expression in IDD patients and its possible mechanism.

Methods: We downloaded the IDD data set from the Gene Expression Omnibus (GEO) database, screened the differentially expressed genes (DEGs) and hub genes and performed Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to construct a protein–protein interaction (PPI) network. Subsequently, we verified LTF's regulatory mechanism through cell experiments. IL-1β was used to intervene in nucleus pulposus cells (NPCs) to construct the IDD cell model, and LTF and Fas expression was detected by qRT–PCR. LTF inhibitor, Fas inhibitor, LTF mimic, and Fas mimic were used to intervene in each group. Western blotting was used to detect Fas, Caspase-3, Bax, and Bcl-2 expression.

Results: A total of 131 DEGs and 10 hub genes were screened. LTF mRNA in the IDD model was significantly higher than that in the control group, while Fas' mRNA was significantly lower. When LTF was upregulated or downregulated in NPCs, apoptosis marker expression showed the opposite trend. The rescue test showed that LTF and Fas' overexpression greatly enhanced NPC apoptosis.

Conclusion: LTF promotes IDD progression by regulating Fas in NPCs, and it may be an effective gene therapy target.

A Neutrophil Extracellular Traps Signature Predicts the Clinical Outcomes and Immunotherapy Response in Head and Neck Squamous Cell Carcinoma

Background: Neutrophil extracellular traps (NETs) play an important role in the occurrence, metastasis and immune escape of cancers. This study aimed to investigate NET-related genes, their clinical prognostic value and their correlation with immunotherapy and anticancer drugs in patients with head and neck squamous cell carcinoma (HNSCC).

Methods: Differentially expressed NET-related genes in HNSCC were identified based on multiple public databases. To improve the clinical practicability and avoid overfitting, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were used to construct a prognostic risk model. A nomogram was further used to explore the clinical value of the model. Internal and external validation were conducted to test the model. Furthermore, the immune microenvironment, immunophenoscore (IPS) and sensitivity to anticancer drugs in HNSCC patients with different prognostic risks were explored.

Results: Six NET-related genes were screened to construct the risk model. In the training cohort, Kaplan–Meier (K-M) analysis showed that the overall survival (OS) of low-risk HNSCC patients was significantly better than that of high-risk HNSCC patients (p < 0.001). The nomogram also showed a promising prognostic value with a better C-index (0.726 vs 0.640) and area under the curve (AUC) (0.743 vs 0.706 at 3 years, 0.743 vs 0.645 at 5 years) than those in previous studies. Calibration plots and decision curve analysis (DCA) also showed the satisfactory predictive capacity of the nomogram. Internal and external validation further strengthened the credibility of the clinical prognostic model. The level of tumor mutational burden (TMB) in the high-risk group was significantly higher than that in the low-risk group (p = 0.017), and the TMB was positively correlated with the risk score (R = 0.11; p = 0.019). Moreover, the difference in immune infiltration was significant in HNSCC patients with different risks (p < 0.05). Furthermore, the IPS analysis indicated that anti-PD-1 (p < 0.001), anti-CTLA4 (p < 0.001) or combining immunotherapies (p < 0.001) were more beneficial for low-risk HNSCC patients. The response to anticancer drugs was also closely correlated with the expression of NET-related genes (p < 0.001).

Conclusion: This study identified a novel prognostic model that might be beneficial to develop personalized treatment for HNSCC patients.

Expression of Ferroptosis-Related Genes is Correlated with Immune Microenvironment in Diabetic Kidney Disease

OBJECTIVE

This study aims to explore the correlation between ferroptosis and immune microenvironment (IME) in diabetic kidney disease (DKD) to provide a new clue for exploring the underlying molecular mechanisms.

METHODS

Corresponding RNA data of DKD patients were downloaded from GEO databases. The weighted gene co-expression network analysis (WGCNA) was used to construct the network, and the selected hub genes, then, overlapped with ferroptosis-related genes (FRGs) from FerrDb. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the IME and immune status. Functional analyses including GO, KEGG and GSEA were conducted to elucidate the underlying mechanisms.

RESULTS

Two molecular subtypes were identified based on the expression of FRGs. ESTIMATE algorithm revealed that there were significant differences in ESTIMATE score between these two clusters of DKD patients, with no significant difference found in stromal score and immune score. In addition, TIMER algorithm indicated there was a significant difference in the degree of T cell infiltration. The ssGSEA algorithm showed immunity was mainly concentrated in thick ascending limb and distal convoluted tubule in adult kidney. GO, KEGG and GSEA analyses revealed that the differentially expressed genes (DEGs) were mainly enriched in immune and metabolism associated pathways.

CONCLUSION

The ferroptosis may be induced by dysregulation of IME, thereby accelerating the progression of DKD. Our work could be applied to provide a new clue for exploring the underlying molecular mechanisms and sheds novel light on the therapy strategy of DKD.

Web-based transcriptome analysis determines a sixteen-gene signature and associated drugs on hearing loss patients: A bioinformatics approach

Background

Hearing loss is becoming more and more general. It may occur at all age and affect the language learning ability of children and trigger serious social problems.

Methods

The hearing loss differentially expressed genes (HL‐DEGs) were recognized through a comparison with healthy subjects. The Gene Ontology (GO) analysis was executed by DAVID. The reactome analysis of HL‐DEGs was performed by Clue‐GO. Next, we used STRING, an online website, to identify crucial protein‐protein interactions among HL‐DEGs. Cytoscape software was employed to construct a protein‐protein interaction network. MCODE, a plug‐in of the Cytoscape software, was used for module analysis. Finally, we used DGIdb database to ascertain the targeted drugs for MCODE genes.

Results

Four hundred four HL‐DEGs were identified, among which the most up‐regulated 10 genes were AL008707.1, SDR42E1P5, BX005040.1, AL671883.2, MT1XP1, AC016957.1, U2AF1L5, XIST, DAAM2, and ADAMTS2, and the most down‐regulated 10 genes were ALOX15, PRSS33, IL5RA, SMPD3, IGHV1‐2, IGLV3‐9, RHOXF1P1, CACNG6, MYOM2, and RSAD2. Through STRING database and MCODE analysis, we finally got 16 MCODE genes. These genes can be regarded as hearing loss related genes. Through biological analysis, it is found that these genes are enriched in pathways related to apoptosis such as tumor necrosis factor. Among them, MMP8, LTF, ORM2, FOLR3, and TCN1 have corresponding targeted drugs. Foremost, MCODE genes should be investigated for its usefulness as a new biomarker for diagnosis and treatment.

Conclusion

In summary, our study produced a sixteen‐gene signature and associated drugs that could be diagnosis and treatment of hearing loss patients.

Transgelin Contributes to a Poor Response of Metastatic Renal Cell Carcinoma to Sunitinib Treatment

Renal cell carcinoma (RCC) represents about 2-3% of all cancers with over 400,000 new cases per year. Sunitinib, a vascular endothelial growth factor tyrosine kinase receptor inhibitor, has been used mainly for first-line treatment of metastatic clear-cell RCC with good or intermediate prognosis. However, about one-third of metastatic RCC patients do not respond to sunitinib, leading to disease progression. Here, we aim to find and characterize proteins associated with poor sunitinib response in a pilot proteomics study. Sixteen RCC tumors from patients responding (8) vs. non-responding (8) to sunitinib 3 months after treatment initiation were analyzed using data-independent acquisition mass spectrometry, together with their adjacent non-cancerous tissues. Proteomics analysis quantified 1996 protein groups (FDR = 0.01) and revealed 27 proteins deregulated between tumors non-responding vs. responding to sunitinib, representing a pattern of deregulated proteins potentially contributing to sunitinib resistance. Gene set enrichment analysis showed an up-regulation of epithelial-to-mesenchymal transition with transgelin as one of the most significantly abundant proteins. Transgelin expression was silenced by CRISPR/Cas9 and RNA interference, and the cells with reduced transgelin level exhibited significantly slower proliferation. Our data indicate that transgelin is an essential protein supporting RCC cell proliferation, which could contribute to intrinsic sunitinib resistance.

Identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most common pathological type of thyroid cancer. The effect of traditional anti-tumor therapy is not ideal for the patients with recurrence, metastasis and radioiodine resistance. The abnormal expression of immune-related genes (IRGs) has critical roles in the etiology of PTC. However, the effect of IRGs on PTC prognosis remains unclear.

METHODS

Based on The Cancer Genome Atlas (TCGA) and ImmPort databases, we integrated IRG expression profiles and progression-free intervals (PFIs) of PTC patients. First, we identified the differentially expressed IRGs and transcription factors (TFs) in PTC. Subsequently, an IRG model that can predict the PFI was constructed by using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses of the differentially expressed IRGs in the TCGA. Additionally, a protein-protein interaction (PPI) network showed the interactions between the differentially expressed genes (DEGs), and the top 30 genes with the highest degree were extracted from the network. Then, the key IRG was identified by the intersection analysis of the PPI network and univariate Cox regression, which was verified the differential expression of by western blotting and immunohistochemistry (IHC). ssGSEA was performed to understand the correlation between the key IRG expression level and immune activity.

RESULTS

A total of 355 differentially expressed IRGs and 43 differentially expressed TFs were identified in PTC patients. Then, eight IRGs were finally utilized to construct an IRG model. The respective areas under the curve (AUCs) of the IRG model reached 0.948, 0.820, and 0.831 at 1, 3 and 5 years in the training set. In addition, lactotransferrin (LTF) was determined as the key IRG related to prognosis. The expression level of LTF in tumor tissues was significantly lower than that in normal tissues. And the results of ssGSEA showed the expression level of LTF is closely related to immune activity.

CONCLUSIONS

These findings show that the prognostic model and key IRG may become promising molecular markers for the prognosis of PTC patients.

Alternatively Expressed Transcripts Analysis of Non-Small Cell Lung Cancer Cells under Different Hypoxic Microenvironment

Globally, non-small cell lung cancer (NSCLC) is the most fatal form of malignancy. Numerous studies have shown that people living at high altitudes are at a higher risk for cancer. Hypoxia is one of the most important features in high altitude area. Compared with normal cells, cancer cells are more adapted to hypoxia atmosphere. However, at high altitudes, hypoxic conditions are also accompanied by other altered environmental conditions. To identify the single influence of hypoxia, we performed second-generation sequencing to identify gene expression changes triggered by the different oxygen concentrations. We identified 782 genes in A549 cells and 1122 genes in H520 cells that showed altered expression by the combined analysis in 5% oxygen concentration group and 1% oxygen concentration group control group. We further analyzed these targets and found 113 genes altered in both cell lines. Interestingly, we found KxD1 was the only one in both top 10 lists. Further analysis revealed KxD1 to be significantly elevated in NSCLC patients and negatively correlated with prognosis in stage I and II NSCLC patients. Moreover, this correlation reversed in stage III patients. Additionally, compared with patients who only received clean margin operation or chemotherapy, patients who received radiotherapy also showed opposite result. Thus, KxD1 may be a promising target for the treatment of NSCLC in high-altitude areas.

Identifying a Potential Key Gene, TIMP1, Associated with Liver Metastases of Uveal Melanoma by Weight Gene Co-Expression Network Analysis

Purpose

Uveal melanoma (UM) is a primary intraocular tumor in adults, with a high percentage of metastases to the liver. Identifying potential key genes may provide information for early detection and prognosis of UM metastasis.

Patients and Methods

Differentially expressed genes (DEGs) were identified using the GSE22138 dataset. Weighted gene co-expression network analysis was used to construct co-expression modules. Functional enrichment analysis was performed for DEGs and genes of key modules. Hub genes were screened by co-expression network and protein–protein interaction network (PPI), and validated by survival analysis in The Cancer Genome Atlas database. Gene set enrichment analysis (GSEA) was used to explore the potential metastasis mechanism of UM. Transient transfection was used to investigate the effect of TIMP1 on the proliferation, migration, and invasion of UM cells.

Results

In total, 552 DEGs were identified between primary and metastatic UM and mainly enriched in extracellular matrix, cellular senescence and focal adhesion pathway. A weighted gene co‑expression network was built to identify key gene modules associated with UM metastasis (n=36). The turquoise module is positively correlated with metastasis and genes in this module were mainly enriched in peptidyl-tyrosine autophosphorylation and regulation of organ growth. The hub gene TIMP1 was screened out by co-expression network and PPI analysis. High expression of TIMP1 was related to p53 pathway by GSEA and short overall survival time. Experimental results indicated that overexpression of TIMP1 inhibited the proliferation and migration, while it had no significant effect on invasion of UM cells.

Conclusion

Our study indicates that TIMP1 might be associated with metastasis in UM, which might have important significance for identifying patients with high risk of metastasis and predicting the prognosis of UM.

Transcriptional landscape of cholangiocarcinoma revealed by weighted gene coexpression network analysis

Cholangiocarcinoma (CCA) is a type of cancer with limited treatment options and a poor prognosis. Although some important genes and pathways associated with CCA have been identified, the relationship between coexpression and phenotype in CCA at the systems level remains unclear. In this study, the relationships underlying the molecular and clinical characteristics of CCA were investigated by employing weighted gene coexpression network analysis (WGCNA). The gene expression profiles and clinical features of 36 patients with CCA were analyzed to identify differentially expressed genes (DEGs). Subsequently, the coexpression of DEGs was determined by using the WGCNA method to investigate the correlations between pairs of genes. Network modules that were significantly correlated with clinical traits were identified. In total, 1478 mRNAs were found to be aberrantly expressed in CCA. Seven coexpression modules that significantly correlated with clinical characteristics were identified and assigned representative colors. Among the 7 modules, the green and blue modules were significantly related to tumor differentiation. Seventy-eight hub genes that were correlated with tumor differentiation were found in the green and blue modules. Survival analysis showed that 17 hub genes were prognostic biomarkers for CCA patients. In addition, we found five new targets (ISM1, SULT1B1, KIFC1, AURKB and CCNB1) that have not been studied in the context of CCA and verified their differential expression in CCA through experiments. Our results not only promote our understanding of the relationship between the transcriptome and clinical data in CCA but will also guide the development of targeted molecular therapy for CCA.