RNA M6A modification shaping cutaneous melanoma tumor microenvironment and predicting immunotherapy response.

Recent years have seen rising mortality rates linked to cutaneous melanoma (SKCM), despite advances in immunotherapy. Understanding RNA N6-methyladenosine (M6A) significance in SKCM is crucial for prognosis, tumor microenvironment (TME), immune cell presence, and immunotherapy efficacy. We analyzed 23 M6A regulators using SKCM samples from TCGA and GEO databases, identifying three M6A modification patterns linked to TME cell infiltration. Principal component analysis (PCA) yielded an M6A score for individual tumors, utilizing patient gene expression profiles and CNV data from TCGA. M6A modification patterns play a crucial role in SKCM development and progression, influencing tumor attributes such as inflammatory stage, subtype, TME interstitial activity, and genetic mutations. The M6A score independently predicts patient outcomes and correlates with improved response to immunotherapy, validated across anti-PD-1 and anti-PD-L1 therapy cohorts. M6A modifications significantly impact the TME landscape, with the M6A score serving as a predictive marker for immunotherapy response. Integrating M6A-related information into clinical practice could revolutionize SKCM management and treatment strategies.

Oncolytic adenovirus encoding apolipoprotein A1 suppresses metastasis of triple-negative breast cancer in mice.

BACKGROUND: Dysregulation of cholesterol metabolism is associated with the metastasis of triple-negative breast cancer (TNBC). Apolipoprotein A1 (ApoA1) is widely recognized for its pivotal role in regulating cholesterol efflux and maintaining cellular cholesterol homeostasis. However, further exploration is needed to determine whether it inhibits TNBC metastasis by affecting cholesterol metabolism. Additionally, it is necessary to investigate whether ApoA1-based oncolytic virus therapy can be used to treat TNBC. METHODS: In vitro experiments and mouse breast cancer models were utilized to evaluate the molecular mechanism of ApoA1 in regulating cholesterol efflux and inhibiting breast cancer progression and metastasis. The gene encoding ApoA1 was inserted into the adenovirus genome to construct a recombinant adenovirus (ADV-ApoA1). Subsequently, the efficacy of ADV-ApoA1 in inhibiting the growth and metastasis of TNBC was evaluated in several mouse models, including orthotopic breast cancer, spontaneous breast cancer, and human xenografts. In addition, a comprehensive safety assessment of Syrian hamsters and rhesus monkeys injected with oncolytic adenovirus was conducted. RESULTS: This study found that dysregulation of cholesterol homeostasis is critical for the progression and metastasis of TNBC. In a mouse orthotopic model of TNBC, a high-cholesterol diet promoted lung and liver metastasis, which was associated with keratin 14 (KRT14), a protein responsible for TNBC metastasis. Furthermore, studies have shown that ApoA1, a cholesterol reverse transporter, inhibits TNBC metastasis by regulating the cholesterol/IKBKB/FOXO3a/KRT14 axis. Moreover, ADV-ApoA1 was found to promote cholesterol efflux, inhibit tumor growth, reduce lung metastasis, and prolonged the survival of mice with TNBC. Importantly, high doses of ADV-ApoA1 administered intravenously and subcutaneously were well tolerated in rhesus monkeys and Syrian hamsters. CONCLUSIONS: This study provides a promising oncolytic virus treatment strategy for TNBC based on targeting dysregulated cholesterol metabolism. It also establishes a basis for subsequent clinical trials of ADV-ApoA1 in the treatment of TNBC.

Oncolytic viruses engineered to enforce cholesterol efflux restore tumor-associated macrophage phagocytosis and anti-tumor immunity in glioblastoma.

The codependency of cholesterol metabolism sustains the malignant progression of glioblastoma (GBM) and effective therapeutics remain scarce. In orthotopic GBM models in male mice, we identify that codependent cholesterol metabolism in tumors induces phagocytic dysfunction in monocyte-derived tumor-associated macrophages (TAMs), resulting in disease progression. Manipulating cholesterol efflux with apolipoprotein A1 (ApoA1), a cholesterol reverse transporter, restores TAM phagocytosis and reactivates TAM-T cell antitumor immunity. Cholesterol metabolomics analysis of in vivo-sorted TAMs further reveals that ApoA1 mediates lipid-related metabolic remodeling and lowers 7-ketocholesterol levels, which directly inhibits tumor necrosis factor signaling in TAMs through mitochondrial translation inhibition. An ApoA1-armed oncolytic adenovirus is also developed, which restores antitumor immunity and elicits long-term tumor-specific immune surveillance. Our findings provide insight into the mechanisms by which cholesterol metabolism impairs antitumor immunity in GBM and offer an immunometabolic approach to target cholesterol disturbances in GBM.

Thermal failure analysis and control algorithm improvement for the active controlled mount with consideration of thermal-magnetic coupling.

This paper proposes an improved narrowband filtered-x least mean square(FxLMS) algorithm to overcome the thermal failure problems of the active controlled mount(ACM). Firstly, the temperature-rising model and the thermal demagnetization model of the ACM are respectively developed. Combining these two models with the powertrain mounting system model, an analytical method is established for the thermal-magnetic coupling analysis of the ACM. Then a numerical simulation is carried out to obtain the permanent magnet(PM) temperature and the coil current. The ACM failure problem is discussed according to the working point trajectory. Finally, an improved algorithm is proposed. This algorithm is able to overcome the thermal failure problems by sacrificing part of the vibration isolation performance. The effectiveness of this algorithm is validated by numerical simulations and a comparison with conventional algorithms.

An Autophagy-Associated Prognostic Gene Signature for Breast Cancer.

Autophagy is closely related to breast cancer and has the dual role of promoting and inhibiting the progression of breast cancer. In this study, we aimed to establish an autophagy-related gene signature for the prognosis of breast cancer. A gene signature composed of the eight most survival-relevant autophagy-associated genes was identified by least absolute shrinkage and selection operator (LASSO) regression analysis. A risk score was calculated based on the gene signature, which divided breast cancer patients into low- or high-risk groups and showed good and poor prognosis, respectively. The risk score displayed good prognostic performance in both the training cohort (TCGA, 1-10-year AUC > 0.63) and the validation cohort (GEO, 1-10-year AUC > 0.66). The multivariate Cox regression and stratified analysis revealed that the risk score was an independent prognostic factor for breast cancer patients. Moreover, the high-risk score was associated with higher infiltration of neutrophils and M2-polarized macrophages, and lower infiltration of resting memory CD4+ T cells, CD8+ T cells, and NK cells. Finally, the high-risk score was associated with myc target, glycolysis, and mTORC1 signaling. The risk score developed based on the autophagy-associated gene signature was an independent prognostic biomarker for breast cancer.

Oncolytic vaccinia virus expressing a bispecific T-cell engager enhances immune responses in EpCAM positive solid tumors.

Insufficient intratumoral T-cell infiltration and lack of tumor-specific immune surveillance in tumor microenvironment (TME) hinder the progression of cancer immunotherapy. In this study, we explored a recombinant vaccinia virus encoding an EpCAM BiTE (VV-EpCAM BiTE) to modulate the immune suppressive microenvironment to enhance antitumor immunity in several solid tumors. VV-EpCAM BiTE effectively infected, replicated and lysed malignant cells. The EpCAM BiTE secreted from infected malignants effectively mediated the binding of EpCAM-positive tumor cells and CD3ϵ on T cells, which led to activation of naive T-cell and the release of cytokines, such as IFN-γ and IL-2. Intratumoral administration of VV-EpCAM BiTE significantly enhanced antitumor activity in malignancies with high other than with low EpCAM expression level. In addition, immune cell infiltration was significantly increased in TME upon VV-EpCAM BiTE treatment, CD8+ T cell exhaustion was reduced and T-cell-mediated immune activation was markedly enhanced. Taken together, VV-EpCAM BiTE sophistically combines the antitumor advantages of bispecific antibodies and oncolytic viruses, which provides preclinical evidence for the therapeutic potential of VV-EpCAM BiTE.

Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDHWT glioblastoma.

Background: Although isocitrate dehydrogenase (IDH) mutation serves as a prognostic signature for routine clinical management of glioma, nearly 90% of glioblastomas (GBM) patients have a wild-type IDH genotype (IDHWT) and lack reliable signatures to identify distinct entities. Methods: To develop a robust prognostic signature for IDHWT GBM patients, we retrospectively analyzed 4 public datasets of 377 primary frozen tumor tissue transcriptome profiling and clinical follow-up data. Samples were divided into a training dataset (204 samples) and a validation (173 samples) dataset. A prognostic signature consisting of 21 metabolism-related gene pairs (MRGPs) was developed based on the relative ranking of single-sample gene expression levels. GSEA and immune subtype analyses were performed to reveal differences in biological processes between MRGP risk groups. The single-cell RNA-seq dataset was used to examine the expression distribution of each MRG constituting the signature in tumor tissue subsets. Finally, the association of MRGs with tumor progression was biologically validated in orthotopic GBM models. Results: The metabolic signature remained an independent prognostic factor (hazard ratio, 5.71 [3.542-9.218], P < 0.001) for stratifying patients into high- and low-risk levels in terms of overall survival across subgroups with MGMTp methylation statuses, expression subtypes, and chemo/ratio therapies. Immune-related biological processes were significantly different between MRGP risk groups. Compared with the low-risk group, the high-risk group was significantly enriched in humoral immune responses and phagocytosis processes, and had more monocyte infiltration and less activated DC, NK, and γδ T cell infiltration. scRNA-seq dataset analysis identified that the expression levels of 5 MRGs (ABCA1, HMOX1, MTHFD2, PIM1, and PTPRE) in TAMs increased with metabolic risk. With tumor progression, the expression level of ABCA1 in TAMs was positively correlated with the population of TAMs in tumor tissue. Downregulation of ABCA1 levels can promote TAM polarization towards an inflammatory phenotype and control tumor growth. Conclusions: The metabolic signature is expected to be used in the individualized management of primary IDHWT GBM patients.

An engineered oncolytic vaccinia virus encoding a single-chain variable fragment against TIGIT induces effective antitumor immunity and synergizes with PD-1 or LAG-3 blockade.

BACKGROUND: In addition to directly lysing tumors, oncolytic viruses also induce antitumor immunity by recruiting and activating immune cells in the local tumor microenvironment. However, the activation of the immune cells induced by oncolytic viruses is always accompanied by high-level expression of immune checkpoints in these cells, which may reduce the efficacy of the oncolytic viruses. The aim of this study is to arm the oncolytic vaccinia virus (VV) with immune checkpoint blockade to enhance its antitumor efficacy. METHODS: Through homologous recombination with the parental VV, an engineered VV-scFv-TIGIT was produced, which encodes a single-chain variable fragment (scFv) targeting T-cell immunoglobulin and ITIM domain (TIGIT). The antitumor efficacy of the VV-scFv-TIGIT was explored in several subcutaneous and ascites tumor models. The antitumor efficacy of VV-scFv-TIGIT combined with programmed cell death 1 (PD-1) or lymphocyte-activation gene 3 (LAG-3) blockade was also investigated. RESULTS: The VV-scFv-TIGIT effectively replicated in tumor cells and lysed them, and prompt the infected tumor cells to secret the functional scFv-TIGIT. Compared with control VV, intratumoral injection of VV-scFv-TIGIT in several mouse subcutaneous tumor models showed superior antitumor efficacy, accompanied by more T cell infiltration and a higher degree of CD8+ T cells activation. Intraperitoneal injection of VV-scFv-TIGIT in a mouse model of malignant ascites also significantly improved T cell infiltration and CD8+ T cell activation, resulting in more than 90% of the tumor-bearing mice being cured. Furthermore, the antitumor immune response induced by VV-scFv-TIGIT was dependent on CD8+ T cells which mediated a long-term immunological memory and a systemic antitumor immunity against the same tumor. Finally, the additional combination of PD-1 or LAG-3 blockade further enhanced the antitumor efficacy of VV-scFv-TIGIT, increasing the complete response rate of tumor-bearing mice. CONCLUSIONS: Oncolytic virotherapy using engineered VV-scFv-TIGIT was an effective strategy for cancer immunotherapy. Administration of VV-scFv-TIGIT caused a profound reshaping of the suppressive tumor microenvironment from 'cold' to 'hot' status. VV-scFv-TIGIT also synergized with PD-1 or LAG-3 blockade to achieve a complete response to tumors with poor response to VV or immune checkpoint blockade monotherapy.

Recombinant adenovirus expressing the fusion protein PD1PVR improves CD8+ T cell-mediated antitumor efficacy with long-term tumor-specific immune surveillance in hepatocellular carcinoma.

PURPOSE: Treatment-associated upregulation of suppressive checkpoints and a lack of costimulatory signals compromise the antitumor efficacy of oncolytic virus immunotherapy. Therefore, we aimed to identify highly effective therapeutic targets to provide a proof-of-principle for immune checkpoint together with oncolytic virus-mediated viro-immunotherapy for cancer. METHODS: A fusion protein containing both the extracellular domain of programmed death-1 (PD-1) and the poliovirus receptor (PVR) was designed. Next, the corresponding expression fragment was inserted into the genome of a replication-competent adenovirus to generate Ad5sPD1PVR. The infection, expression, replication and oncolysis of Ad5sPD1PVR were investigated in hepatocellular carcinoma (HCC) cell lines. Immune activation and the antitumor efficacy of Ad5sPD1PVR were examined in HCC tumor models including a humanized immunocompetent mouse model. RESULTS: Ad5sPD1PVR effectively infected and replicated in HCC cells and secreted sPD1PVR. In a H22 ascitic HCC mouse model, intraperitoneal injection of Ad5sPD1PVR markedly recruited lymphocytes and activated antitumor immune responses. Ad5sPD1PVR exerted a profound antitumor effect on ascitic HCC. Furthermore, we found that Ad5sPD1PVR-H expressing sPD1PVR of human origin exhibited potent antitumor effects in a HCC humanized mouse model. We also found that CD8+ T cells mediated the antitumor effects and long-term tumor-specific immune surveillance induced by Ad5sPD1PVR. Finally, when combined with fludarabine, the antitumor efficacy of Ad5sPD1PVR was found to be further improved in the ascitic HCC model. CONCLUSIONS: From our data we conclude that the newly designed recombinant Ad5sPD1PVR virus significantly enhances CD8+ T cell-mediated antitumor efficacy with long-term tumor-specific immune surveillance in hepatocellular carcinoma, and that fludarabine is a promising therapeutic partner for Ad5sPD1PVR.

Enhanced antitumor efficacy of a novel oncolytic vaccinia virus encoding a fully monoclonal antibody against T-cell immunoglobulin and ITIM domain (TIGIT).

BACKGROUND: Oncolytic virotherapy with vaccinia virus (VV) can lead to effective anti-tumor immunity by turning "cold" tumors into "hot" tumors. However, its therapeutic potential is affected by the tumor's local immunosuppressive tumor microenvironment (TME). Therefore, it is necessary to explore the use of immune checkpoint inhibitors to arm oncolytic VVs to enhance their anti-tumor efficacy. METHODS: A novel recombinant oncolytic VV, VV-α-TIGIT, which encoded a fully monoclonal antibody against T-cell immunoglobulin and ITIM domain (TIGIT) was generated by homologous recombination with a shuttle plasmid. The anti-tumor efficacy of the VV-α-TIGIT was investigated in several subcutaneous and ascites tumor models. FINDINGS: The functional α-TIGIT was sufficiently produced and secreted by tumor cells infected with VV-α-TIGIT, which effectively replicated in tumor cells leading to significant oncolysis. Intratumoral injection of VV-α-TIGIT improved anti-tumor efficacy in several murine subcutaneous tumor models compared to VV-Control (without α-TIGIT insertion). Intraperitoneal injection of VV-α-TIGIT achieved approximately 70% of complete tumor regression in an ascites tumor model. At the same time, treatment with VV-α-TIGIT significantly increased the recruitment and activation of T cells in TME. Moreover, the in vivo anti-tumor activity of VV-α-TIGIT was largely dependent on CD8+ T cell-mediated immunity. Finally, the tumor-bearing mice cured of VV-α-TIGIT treatment resisted rechallenge with the same tumor cells, suggesting a long-term persistence of tumor-specific immunological memory. INTERPRETATION: The recombinant oncolytic virus VV-α-TIGIT successfully combines the advantages of oncolytic virotherapy and intratumorally expression of immune checkpoint inhibitor against TIGIT. This novel strategy can provide information on the optimal design of novel antibody-armed oncolytic viruses for cancer immunotherapy. FUNDING: This work was supported by the National Natural Science Foundation of China (81773255, 81472820, and 81700037), the Science and Technology Innovation Foundation of Nanjing University (14913414), and the Natural Science Foundation of Jiangsu Province of China (BK20171098).

Liraglutide activates nature killer cell-mediated antitumor responses by inhibiting IL-6/STAT3 signaling in hepatocellular carcinoma.

Inflammatory IL-6/STAT3 signaling is constitutively activated in diverse cancers and is associated with malignant cell proliferation, invasion and escape of antitumor immunosurveillance. Liraglutide, a glucagon-like peptide-1 (GLP-1) analog, is commonly used to treat insulin-resistant diabetes. In this study, for the first time, we showed that liraglutide remarkably improved the antitumor immune responses in hepatocellular carcinoma (HCC). Furthermore, we showed that the antitumor activity was mediated by nature killer cells (NKs) but not CD8+ T cells. Finally, we showed that liraglutide enhanced NK-mediated cytotoxicity by suppressing the IL-6/STAT3 signaling pathway in HCC cells. Our findings unveil a novel therapeutic role of liraglutide by manipulating the innate immunity in cancer therapy.

Pan-Cancer Analysis of Immune Cell Infiltration Identifies a Prognostic Immune-Cell Characteristic Score (ICCS) in Lung Adenocarcinoma.

Background: The tumor microenvironment (TME) consists of heterogeneous cell populations, including malignant cells and nonmalignant cells that support tumor proliferation, invasion, and metastasis through extensive cross talk. The intra-tumor immune landscape is a critical factor influencing patient survival and response to immunotherapy. Methods: Gene expression data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Immune cell infiltration was determined by single-sample Gene Set Enrichment Analysis (ssGSEA) depending on the integrated immune gene sets from published studies. Univariate analysis was used to determine the prognostic value of the infiltrated immune cells. Least absolute shrinkage and selection operator (LASSO) regression was performed to screen for the most survival-relevant immune cells. An immune-cell characteristic score (ICCS) model was constructed by using multivariate Cox regression analysis. Results: The immune cell infiltration patterns across 32 cancer types were identified, and patients in the high immune cell infiltration cluster had worse overall survival (OS) but better progression-free interval (PFI) compared to the low immune cell infiltration cluster. However, immune cell infiltration showed inconsistent prognostic value depending on the cancer type. High immune cell infiltration (High CI) indicated a worse prognosis in brain lower grade glioma (LGG), glioblastoma multiforme (GBM), and uveal melanoma (UVM), and favorable prognosis in adrenocortical carcinoma (ACC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), sarcoma (SARC), and skin cutaneous melanoma (SKCM). LUAD prognosis was significantly influenced by the infiltration of 13 immune cell types, with high infiltration of all but Type 2 T helper (Th2) cells correlating with a favorable prognosis. The ICCS model based on six most survival-relevant immune cell populations was generated that classified patients into low- and high-ICCS groups with good and poor prognoses, respectively. The multivariate and stratified analyses further revealed that the ICCS was an independent prognostic factor for LUAD. Conclusions: The infiltration of immune cells in 32 cancer types was quantified, and considerable heterogeneity was observed in the prognostic relevance of these cells in different cancer types. An ICCS model was constructed for LUAD with competent prognostic performance, which can further deepen our understanding of the TME of LUAD and can have implications for immunotherapy.

Identification of key genes for predicting colorectal cancer prognosis by integrated bioinformatics analysis.

Colorectal cancer (CRC) is a life-threatening disease with a poor prognosis. Therefore, it is crucial to identify molecular prognostic biomarkers for CRC. The present study aimed to identify potential key genes that could be used to predict the prognosis of patients with CRC. Three CRC microarray datasets (GSE20916, GSE73360 and GSE44861) were downloaded from the Gene Expression Omnibus (GEO) database, and one dataset was obtained from The Cancer Genome Atlas (TCGA) database. The three GEO datasets were analyzed to detect differentially expressed genes (DEGs) using the BRB-ArrayTools software. Functional and pathway enrichment analyses of these DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein-protein interaction (PPI) network of DEGs was constructed, hub genes were extracted, and modules of the PPI network were analyzed. To investigate the prognostic values of the hub genes in CRC, data from the CRC datasets of TCGA were used to perform the survival analyses based on the sample splitting method and Cox regression model. Correlation among the hub genes was evaluated using Spearman's correlation analysis. In the three GEO datasets, a total of 105 common DEGs were identified, including 51 down- and 54 up-regulated genes in CRC compared with normal colorectal tissues. A PPI network consisting of 100 DEGs and 551 edges was constructed, and 44 nodes were identified as hub genes. Among these 44 genes, the four hub genes TIMP metallopeptidase inhibitor 1 (TIMP1), solute carrier family 4 member 4 (SLC4A4), aldo-keto reductase family 1 member B10 (AKR1B10) and ATP binding cassette subfamily E member 1 (ABCE1) were associated with overall survival (OS) in patients with CRC. Three significant modules were extracted from the PPI network. The hub gene TIMP1 was present in Module 1, ABCE1 was involved in Module 2 and SLC4A4 was identified in Module 3. Univariate analysis revealed that TIMP1, SLC4A4, AKR1B10 and ABCE1 were associated with the OS of patients with CRC. Multivariate analysis demonstrated that SLC4A4 may be an independent prognostic factor associated with OS. Furthermore, the results from correlation analysis revealed that there was no correlation between TIMP1, SLC4A4 and ABCE1, whereas AKR1B10 was positively correlated with SLC4A4. In conclusion, the four key genes TIMP1, SLC4A4, AKR1B10 and ABCE1 associated with the OS of patients with CRC were identified by integrated bioinformatics analysis. These key genes may be used as prognostic biomarkers to predict the survival of patients with CRC, and may therefore represent novel therapeutic targets for CRC.

Association of MTHFR and RFC1 gene polymorphisms with methotrexate efficacy and toxicity in Chinese Han patients with rheumatoid arthritis.

OBJECTIVE: The objective was to explore the association of methylene tetrahydrofolate reductase (MTHFR) C667T and A1298C and reduced folate carrier 1 (RFC-1) A80G single nucleotide polymorphisms (SNP) with rheumatoid arthritis (RA) and efficacy and toxicity of methotrexate (MTX) treatment in Chinese Han patients in Henan, China. METHODS: Two hundred ninety-six patients with RA were enrolled (cases) and 120 healthy individuals served as controls. The genotypes of MTHFR C667T and A1298C SNP and RFC-1 A80G SNP were detected by restriction fragment length polymorphism-PCR and compared between cases and controls. We analyzed correlations of clinical effect, toxicity, and SNPs after 6 months of MTX treatment. RESULTS: We detected no significant differences in MTHFR C677T and A1298C and RFC-1 A80G SNPs between cases and controls. The RFC-1 A80G SNP differed between RA patients with good and poor efficacy after 6 months of MTX, and was an independent factor of MTX efficacy. The MTHFR C677T SNP was differently distributed in the adverse drug reaction (ADR) and non-ADR groups and was an independent factor of MTX toxicity. CONCLUSIONS: In Chinese Han patients with RA, the MTHFR C667T SNP may correlate with MTX toxicity, whereas the RFC-1 A80G SNP may correlate with MTX efficacy rather than toxicity.

A Robust 8-Gene Prognostic Signature for Early-Stage Non-small Cell Lung Cancer.

Background: The current staging system is imprecise for prognostic prediction of early-stage non-small cell lung cancer (NSCLC). This study aimed to develop a robust prognostic signature for early-stage NSCLC, allowing classification of patients with a high risk of poor outcome and specific treatment decision. Method: In the present study, a comprehensive genome-wide profiling analysis was conducted using a retrospective pool of early-stage NSCLC patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE31210, GSE37745, and GSE50081 and The Cancer Genome Atlas (TCGA). Cox proportional hazards models were implemented to determine the association between gene expression levels and overall patient survival in each dataset. The common genes among all datasets were selected as candidate prognostic genes. A risk score model was developed and validated using four independent datasets and the entire cohort. The Kaplan-Meier with log-rank test was used to assess survival difference. Results: A univariate Cox proportional hazards regression analysis for each dataset showed that a total of 2280 genes in GSE31210, 762 genes in GSE37745, 871 genes in GSE50081, and 666 genes in TCGA were identified as candidate protective genes, while overall 2131 genes in GSE31210, 913 in GSE37745, 1107 in GSE50081, and 997 in TCGA were identified as candidate risky genes. There were 8 common genes associated with overall survival, including 7 mRNA and 1 lncRNA. By using the Step-wise multivariate Cox analysis, an 8-gene prognostic signature (CDCP1, HMMR, TPX2, CIRBP, HLF, KBTBD7, SEC24B-AS1, and SH2B1) for early-stage NSCLC was developed. Patients in the high-risk group had shorter overall survival than those in the low-risk group. Multivariate regression and stratified analysis suggested that the prognostic power of the 8-gene signature was independent of other clinical factors. Furthermore, the 8-gene signature achieved AUC values of 0.726, 0.701, 0.725 and 0.650 in GSE31210, GSE37745, GSE50081 and TCGA, respectively. Moreover, the combination of the 8-gene signature and the stage resulted to a better patient classification for survival prediction and treatment decision. Conclusion: This study developed a robust gene signature with great value for prognostic prediction in early-stage NSCLC, which may contribute to patient classification and personalized treatment decisions.

A robust six-gene prognostic signature for prediction of both disease-free and overall survival in non-small cell lung cancer.

BACKGROUND: The high mortality of patients with non-small cell lung cancer (NSCLC) emphasizes the necessity of identifying a robust and reliable prognostic signature for NSCLC patients. This study aimed to identify and validate a prognostic signature for the prediction of both disease-free survival (DFS) and overall survival (OS) of NSCLC patients by integrating multiple datasets. METHODS: We firstly downloaded three independent datasets under the accessing number of GSE31210, GSE37745 and GSE50081, and then performed an univariate regression analysis to identify the candidate prognostic genes from each dataset, and identified the gene signature by overlapping the candidates. Then, we built a prognostic model to predict DFS and OS using a risk score method. Kaplan-Meier curve with log-rank test was used to determine the prognostic significance. Univariate and multivariate Cox proportional hazard regression models were implemented to evaluate the influences of various variables on DFS and OS. The robustness of the prognostic gene signature was evaluated by re-sampling tests based on the combined GEO dataset (GSE31210, GSE37745 and GSE50081). Furthermore, a The Cancer Genome Atlas (TCGA)-NSCLC cohort was utilized to validate the prediction power of the gene signature. Finally, the correlation of the risk score of the gene signature and the Gene set variation analysis (GSVA) score of cancer hallmark gene sets was investigated. RESULTS: We identified and validated a six-gene prognostic signature in this study. This prognostic signature stratified NSCLC patients into the low-risk and high-risk groups. Multivariate regression and stratification analyses demonstrated that the six-gene signature was an independent predictive factor for both DFS and OS when adjusting for other clinical factors. Re-sampling analysis implicated that this six-gene signature for predicting prognosis of NSCLC patients is robust. Moreover, the risk score of the gene signature is correlated with the GSVA score of 7 cancer hallmark gene sets. CONCLUSION: This study provided a robust and reliable gene signature that had significant implications in the prediction of both DFS and OS of NSCLC patients, and may provide more effective treatment strategies and personalized therapies.

Silencing of XRCC4 increases radiosensitivity of triple-negative breast cancer cells.

BACKGROUND: Radiotherapy is an important locoregional treatment, and its effect on triple-negative breast cancer (TNBC) needs to be enhanced. The aim of the present study was to investigate the potential effects of XRCC4 on radiosensitivity of TNBC. METHODS: The RNAi technique was implemented to establish the TNBC stable cell line with XRCC4 knockdown. MTT assay was used to detect the effect of XRCC4 knockdown on cell proliferation. Western blot and immunohistochemistry assays were employed to identify protein expression. Colony assay was performed to detect the effect of XRCC4 knockdown on the colony formation ability of TNBC cells with radiation treatment. Comet assay was conducted to evaluate the influence of XRCC4 silencing on DNA repair activity in ionizing radiation. In addition, we performed a survival analysis based on data in TCGA database. RESULTS: XRCC4 knockdown by lentivirus-mediated shRNA had no significant effect on proliferation of TNBC cells. Knockdown of XRCC4 could substantially increase the sensitivity of TNBC cells to ionizing radiation. The DNA damage level was detected to be increased in the XRCC4 knockdown group, indicating there was a significant repair delay in the XRCC4-deleted cells. Clinical sample analysis exhibited that there were various XRCC4 expression in different patients with TNBC. Moreover, survival analysis showed that high expression of XRCC4 was significantly associated with poor progression-free survival after radiotherapy in TNBC patients. Conclusion: Our findings suggest that XRCC4 knockdown sensitizes TNBC cells to ionizing radiation, and could be considered as a novel predictor of radiosensitivity and a promising target for TNBC.

A RNA sequencing-based six-gene signature for survival prediction in patients with glioblastoma.

Glioblastoma (GBM) is an aggressive tumor of the central nervous system that has poor prognosis despite extensive therapy. Therefore, it is essential to identify a gene expression-based signature for predicting GBM prognosis. The RNA sequencing data of GBM patients from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases were employed in our study. The univariate and multivariate regression models were utilized to assess the relative contribution of each gene to survival prediction in both cohorts, and the common genes in two cohorts were identified as a final prognostic model. A prognostic risk score was calculated based on the prognostic gene signature. This prognostic signature stratified the patients into the low- and high-risk groups. Multivariate regression and stratification analyses were implemented to determine whether the gene signature was an independent prognostic factor. We identified a 6-gene signature through univariate and multivariate regression models. This prognostic signature stratified the patients into the low- and high-risk groups, implying improved and poor outcomes respectively. Multivariate regression and stratification analyses demonstrated that the predictive value of the 6-gene signature was independent of other clinical factors. This study highlights the significant implications of having a gene signature as a prognostic predictor in GBM, and its potential application in personalized therapy.

Identification of a 6-gene signature predicting prognosis for colorectal cancer.

BACKGROUND: An accurate and robust gene signature is of the utmost importance in assisting oncologists to make a more accurate evaluation in clinical practice. In our study, we extracted key mRNAs significantly related to colorectal cancer (CRC) prognosis and we constructed an expression-based gene signature to predict CRC patients' survival. METHODS: mRNA expression profiles and clinicopathological data of colon adenocarcinoma (COAD) cases and rectum adenocarcinoma (READ) were collected from The Cancer Genome Atlas database to investigate gene expression alteration associated to the prognosis of CRC. Differentially expressed mRNAs (DEMs) were detected between COAD/READ and normal tissue samples. Relying on a univariate and multivariate Cox regression analyses, a mRNA panel signature was established and used for predicting the overall survival (OS) in CRC patients. Receiver operating characteristic curve was used to evaluate the prognosis performance of our model through calculating the AUC values corresponding to the 3-year and 5-year survival. To assess the performance of gene signature in the given cancer subgroups (CRC entire cohort, COAD cohort, and READ cohort), a stratified analysis was carried out according to clinical factors. RESULTS: A total of 5341 and 5594 DEMs were collected from COAD vs. normal tissue samples, and READ vs. normal samples respectively. A univariate regression analysis for the common DEMs between COAD and READ cohorts resulted in 14 common mRNAs related to OS. The multivariate Cox regression analysis revealed that 6 of these mRNAs (EPHA6, TIMP1, IRX6, ART5, HIST3H2BB, and FOXD1) had significant prognostic value allowing the discrimination between high- and low-risk patients, implying poor and good outcomes, respectively. The stratified analysis identified 6-gene signature as an independent prognostic signature in predicting CRC patients' survival. CONCLUSIONS: The 6-gene signature could act as an independent biomarker for survival prediction of CRC patients.

Identification of a universal 6-lncRNA prognostic signature for three pathologic subtypes of renal cell carcinoma.

Renal cell carcinoma (RCC) is the most common adult renal epithelial cancer susceptible to metastasis and patients with irresectable RCC always have a poor prognosis. Long noncoding RNAs (lncRNAs) have recently been documented as having critical roles in the etiology of RCC. Nevertheless, the prognostic significance of lncRNA-based signature for outcome prediction in patients with RCC has not been well investigated. Therefore, it is essential to identify a lncRNA-based signature for predicting RCC prognosis. In the current study, we comprehensively analyzed the RNA sequencing data of the three main pathological subtypes of RCC (kidney renal clear cell carcinoma [KIRC], kidney renal papillary cell carcinoma [KIRP], and kidney chromophobe carcinoma [KICH]) from The Cancer Genome Atlas (TCGA) database, and identified a 6-lncRNA prognostic signature with the help of a step-wise multivariate Cox regression model. The 6-lncRNA signature stratified the patients into low- and high-risk groups with significantly different prognosis. Multivariate Cox regression analysis showed that predictive value of the 6-lncRNA signature was independent of other clinical or pathological factors in the entire cohort and in each cohort of RCC subtypes. In addition, the three independent prognostic clinical factors (including age, pathologic stage III, and stage IV) was also stratified into low- and high-risk groups basis on the risk score, and the stratification analyses demonstrated that the high-risk score was a poor prognostic factor. In conclusion, these findings indicate that the 6-lncRNA signature is a novel prognostic biomarker for all three subtypes of RCC, and can increase the accuracy of predicting overall survival.