Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC), ranking as the second-leading cause of global mortality among malignancies, poses a substantial burden on public health worldwide. Anoikis, a type of programmed cell death, serves as a barrier against the dissemination of cancer cells to distant organs, thereby constraining the progression of cancer. Nevertheless, the mechanism of genes related to anoikis in HCC is yet to be elucidated. Methods: This paper's data (TCGA-HCC) were retrieved from the database of the Cancer Genome Atlas (TCGA). Differential gene expression with prognostic implications for anoikis was identified by performing both the univariate Cox and differential expression analyses. Through unsupervised cluster analysis, we clustered the samples according to these DEGs. By employing the least absolute shrinkage and selection operator Cox regression analysis (CRA), a clinical predictive gene signature was generated from the DEGs. The Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to determine the proportions of immune cell types. The external validation data (GSE76427) were procured from Gene Expression Omnibus (GEO) to verify the performance of the clinical prognosis gene signature. Western blotting and immunohistochemistry (IHC) analysis confirmed the expression of risk genes. Results: In total, 23 prognostic DEGs were identified. Based on these 23 DEGs, the samples were categorized into four distinct subgroups (clusters 1, 2, 3, and 4). In addition, a clinical predictive gene signature was constructed utilizing ETV4, PBK, and SLC2A1. The gene signature efficiently distinguished individuals into two risk groups, specifically low and high, demonstrating markedly higher survival rates in the former group. Significant correlations were observed between the expression of these risk genes and a variety of immune cells. Moreover, the outcomes from the validation cohort analysis aligned consistently with those obtained from the training cohort analysis. The results of Western blotting and IHC showed that ETV4, PBK, and SLC2A1 were upregulated in HCC samples. Conclusion: The outcomes of this paper underscore the effectiveness of the clinical prognostic gene signature, established utilizing anoikis-related genes, in accurately stratifying patients. This signature holds promise in advancing the development of personalized therapy for HCC.

Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer.

Background: Pancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear. Methods: Study data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature. Results: A total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples. Conclusion: Our study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.

Identification and analysis of prognostic metabolic characteristics in colon adenocarcinoma.

Colon adenocarcinoma (COAD) is a highly lethal gastrointestinal malignancy. The five-year survival rate of metastatic colorectal cancer remains low, at 14 percent. Numerous publications have suggested a role for peroxisome proliferator-activated receptors (PPARs) in malignancy. Recent studies have shown that PPARs, as nuclear transcription factors, may serve as potential targets for the treatment of metabolic syndrome tumors and their associated complications. However, the molecular mechanism has not been thoroughly investigated. Hence, in order to enhance the prediction of personalized medicine for PPAR-associated modulators in malignancy treatment, a timely review becomes essential. Utilizing TCGA-COAD expression profile data and patient overall survival (OS) information, this study systematically conducted investigations to identify and develop Hub stem cell-related diagnostic and prognostic identification models, aiming to enhance the multi-gene markers for COAD. Utilizing the differential expression profiles of stem cell-related genes, an 11-gene (SLC27A4, CPT1C, CPT1B, CPT2, CYP4A11, FABP3, FABP7, AQP7, MMP1, ACOX1, ANGPTL4) diagnostic and prognostic model was developed. This model demonstrated precise diagnostic and prognostic capabilities and holds the potential to characterize the clinicopathologic features of COAD. Univariate and multivariate Cox proportional hazards regression analyses were conducted to ascertain the independent factors influencing OS outcomes in COAD. The results revealed that CPT1B, SLC27A4, and FABP3 were identified as independent risk prognostic factors for OS in COAD, whereas ACOX1 and CPT2 served as independent protective prognostic factors. The hub genes associated with PPARs were identified through the differential expression of contrast agent COAD and normal tissues. Finally, the investigation of variations in immune infiltration and the analysis of relevant biological pathways validate the prognostic significance of the independent post-factors within this molecular model. This research aims to provide references for comprehending the mechanism of post-transcriptional regulation of COAD and molecular therapy.

Comprehensive analysis and experimental verification of the mechanism of action of T cell-mediated tumor-killing related genes in Colon adenocarcinoma.

BACKGROUND: Colorectal cancer (CRC) is a prevalent malignancy of the digestive tract. A new prognostic scoring model for colon adenocarcinoma (COAD) is developed in this study based on the genes involved in tumor cell-mediated killing of T cells (GSTTKs), accurately stratifying COAD patients, thus improving the current status of personalized treatment. METHOD: The GEO and TCGA databases served as the sources of the data for the COAD cohort. This study identified GSTTKs-related genes in COAD through single-factor Cox analysis. These genes were used to categorize COAD patients into several subtypes via unsupervised clustering analysis. The biological pathways and tumor microenvironments of different subgroups were compared. We performed intersection analysis between different subtypes to obtain intersection genes. Single-factor Cox regression analysis and Lasso-Cox analysis were conducted to establish clinical prognostic models. Two methods are used to assess the accuracy of model predictions: ROC and Kaplan-Meier analysis. Next, the prediction model was further validated in the validation cohort. Differential immune cell infiltration between various risk categories was identified via single sample gene set enrichment analysis (ssGSEA). The COAD model's gene expression was validated via single-cell data analysis and experiments. RESULT: We established two distinct GSTTKs-related subtypes. Biological processes and immune cell tumor invasion differed significantly between various subtypes. Clinical prognostic models were created using five GSTTKs-related genes. The model's risk score independently served as a prognostic factor. COAD patients were classified as low- or high-risk depending on their risk scores. Patients in the low-risk category recorded a greater chance of surviving. The outcomes from the validation cohort match those from the training set. Risk scores and several tumor-infiltrating immune cells were strongly correlated, according to ssGSEA. Single-cell data illustrated that the model's genes were linked to several immune cells. The experimental results demonstrated a significant increase in the expression of HOXC6 in colon cancer tissue. CONCLUSION: Our research findings established a new gene signature for COAD. This gene signature helps to accurately stratify the risk of COAD patients and improve the current status of individualized care.

Camptothecin Delivery via Tumor-Derived Exosome for Radiosensitization by Cell Cycle Regulation on Patient-Derived Xenograft Mice.

Purpose: While radiotherapy remains the leading clinical treatment for many tumors, its efficacy can be significantly hampered by the insensitivity of cells in the S phase of the cell cycle to such irradiation. Methods: Here, we designed a highly targeted drug delivery platform in which exosomes were loaded with the FDA-approved anti-tumor drug camptothecin (CPT) which is capable of regulating cell cycle. The utilized exosomes were isolated from patient tumors, enabling the personalized treatment of individuals to ensure better therapeutic outcomes. Results: This exosome-mediated delivery strategy was exhibited robust targeted to patient-derived tumor cells in vitro and in established patient-derived xenograft models. By delivering CPT to tumor cells, this nanoplatform was able to decrease cell cycle arrest in the S phase, increasing the frequency of cells in the G1 and G2/M phases such that they were more radiosensitive. Conclusion: This therapeutic approach was able to substantially enhance the sensitivity of patient-derived tumors to ionizing radiation, thereby improving the overall efficacy of radiotherapy without the need for a higher radiation dose.

Synergistic Radiosensitization Mediated by Chemodynamic Therapy via a Novel Biodegradable Peroxidases Mimicking Nanohybrid.

Purpose: Reactive oxygen species (ROS) are practically essential in radiotherapy to damage cancer cells; however, they are always inadequate for some malignant entities. Here, we designed a biodegradable mesoporous silica decorated with hemin and glucose oxidase (GOD@Hemin-MSN) to generate a chemodynamic therapy in order to enhance the killing capacity of radiotherapy. Methods: Mesoporous silica, as an outstanding drug carrier, can deliver hemin and glucose oxidase to the tumor site. With high level of metabolism activity, cancer cells are abundant in glucose, which can be oxidized into H2O2 by glucose oxidase (GOD) on site. The generated H2O2 is subsequently converted into intracellular ROS, especially hydroxyl radical within the tumor microenvironment, by hemin, which has mimetic peroxidase properties. By this means, the ROS can be supplemented or enriched to facilitate the killing of tumor cells. Results: The chemodynamic therapy induced by GOD@Hemin-MSN produced quantities of ROS, which compensated for their inadequacy as a result of radiotherapy, and exhibited remarkable antitumor efficacy, with a tumor inhibition rate of 91.5% in A549 tumor-bearing mice. Conclusion: This work has validated GOD@Hemin-MSN as a radiosensitizer in chemodynamic therapy, which showed biocompatibility and potential for translational application.

SCAMP4 is a novel prognostic marker and correlated with the tumor progression and immune infiltration in glioma.

BACKGROUND: Glioma is the most prevalent brain tumor with high mortality and morbidity and the prognosis of patients remains very poor. Glioma therapy is largely limited by the extraordinary invasive capability in glioma and the lack of valuable biomarkers of LGG and GBM. So it is urgent and important for us to identify valuable biomarkers to treat glioma patients. SCAMP4 (Secretory Carrier-Associated Membrane Protein 4) has not been reported to be linked to cancer prognostic or any treatments. METHODS: We analyzed the role of SCAMP4 in LGG and GBM via the publicly available CGGA (The Chinese Glioma Atlas) and TCGA (The Cancer Genome Atlas) databases. The correlations between SCAMP4 and the immune cells were analyzed by applying CIBERSORT and TIMER, while R was utilized in the analysis of the statistical data. RESULTS: Our results indicated that SCAMP4 which is correlated to age, stage, grade and tumor status and may be a promising independent prognostic factor in LGG and GBM. Meanwhile, the expression of SCAMP4 is closely associated with some tumor-infiltrating immune cells such as Monocytes, NK cells activated, Macrophages, Mast cells resting and so on. Furthermore, during the in-depth analysis of the integrated correlations, we also find that isocitrate dehydrogenase 1 (IDH1) and SCAMP4 shared similar prognostic values. CONCLUSIONS: Together with all these findings, the identification of SCAMP4 as a new biomarker could elucidate how the immune microenvironment influence the glioma development. With further analysis, SCAMP4 may be a predictor for glioma prognosis.

The low expression of NUP62CL indicates good prognosis and high level of immune infiltration in lung adenocarcinoma.

A primary factor in tumor morbidity and mortality, lung adenocarcinoma (LUAD) is known to be a major subtype of lung cancer, having the lowest survival rate among all other cancers. Using The Cancer Genome Atlas (TCGA) database the relationship between the immune infiltrate and the NUP62CL was explored and the value of the NUP62CL expression in the prognosis and diagnosis LUAD was examined. Using the logistic regression and the Wilcoxon signed-rank test the relationship between the NUP62CL and the clinico-pathological features was analyzed. There was a significant correlation between the clinical stage (p = 0.005), the N (p = 0.004), and the decreased expression of NUP62CL. The prognosis of LUAD with high NUP62CL expression was revealed to be worse than that with low NUP62CL expression (p < 0.001) by the Kaplan-Meier survival analysis. The potentiality of NUP62CL to be a significant factor of prognosis for LUAD was indicated by the analyses of the multivariate and the univariate Cox regression models. In LUAD, the crucial role of recombination and maintenance of telomere as a significant pathway for NUP62CL was suggested by the Gene Set Enrichment Analysis (GSEA). To analyze the correlation between the genes and the tumor infiltrating immune cells the CIBERSORT was used. Moreover the positive correlation with the NUP62CL expression in LUAD of the infiltration level of the tumor infiltrating B lymphocytes and memory CD4+ T cells was exhibited by CIBERSORT. Therefore, NUP62CL may be a new valuable prognostic indicator for LUAD.

Development of a Prognostic Model for Ovarian Cancer Patients Based on Novel Immune Microenvironment Related Genes.

Ovarian cancer (OV) has become the most lethal gynecological cancer. However, its treatment methods and staging system are far from ideal. In the present study, taking the advantage of large-scale public cohorts, we extracted a list of immune-related prognostic genes that differentially expressed in tumor and normal ovarian tissues. Importantly, an individualized immune-related gene based prognostic model (IPM) for OV patients were developed. Furthermore, we validated our IPM in Gene Expression Omnibus (GEO) repository and compared the immune landscape and pathways between high-risk and low-risk groups. The results of our study can serve as an important model to identify the immune subset of patients and has potential for use in immune therapeutic selection and patient management.

Development and Validation of a Clinical Prognostic Model Based on Immune-Related Genes Expressed in Clear Cell Renal Cell Carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent and terminal subtype of RCC. Reliable markers associated with the immune response are not available to predict the prognosis of patients with ccRCC. We exploited the extensive number of ccRCC samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repository to perform a comprehensive analysis of immune-related genes (IRGs). Methods: Based on TCGA data, we incorporated IRGs and their expression profiles of 72 normal and 539 ccRCC samples. Univariate Cox analysis was used to evaluate the relationship between overall survival (OS) and IRGs expression. The Lasso Cox regression model identified prognostic genes used to establish a clinical immune prognostic model. The TF-IRG network was used to study the potential molecular mechanisms of action and properties of ccRCC-specific IRGs. Multivariate Cox analysis established a clinical prognostic model of IRGs. Results: We found a significant correlation among 15 differentially expressed IRGs with the OS of patients with ccRCC. Gene function enrichment analysis showed that these IRGs are significantly associated with response to receptor ligand activity. Lasso Cox regression analysis identified 10 genes with the greatest prognostic value. A clinical prognostic model based on six IRGs, which performed well for predicting prognosis, revealed significant associations of patients' survival with age, sex, stage, tumor, node, and metastasis. Moreover, these findings reflect the infiltration of tumors by various immune cells. Conclusion: We identified six clinically significant IRGs and incorporated them into a clinical prognostic model with great significance for monitoring and predicting prognosis of ccRCC.

Low expression of RGL4 is associated with a poor prognosis and immune infiltration in lung adenocarcinoma patients.

Lung adenocarcinoma (LUAD) is a frequently diagnosed histologic subtype with increasing morbidity and mortality. RalGDS-Like 4 (RGL4) has not been reported to be associated with cancer risk, prognosis, immunotherapy or any other treatments. We perform a bioinformatics analysis on data downloaded from the Cancer Genome Atlas (TCGA)-LUAD, and we find that low expression of RGL4 is accompanied by worse outcomes and prognosis in LUAD patients. As a promising predictor, the potential influence and mechanisms of RGL4 on overall survival are worth exploring. Moreover, RGL4 expression is significantly associated with a variety of tumor-infiltrating immune cells (TIICs), particularly memory B cells, CD8+T cells and neutrophils. Subsequently, we evaluated the most notable KEGG pathways, including glycolysis gluconeogenesis, the P53 signaling pathway, RNA degradation, and the B cell receptor signaling pathway, among others. Our findings provide evidence that the decreased expression of RGL4 is significantly associated with poor prognosis and immune cell infiltration in patients with LUAD and highlight the use of RGL4 as a novel predictive biomarker for the prognosis of LUAD and other cancers. RGL4 may also be used in combination with immune checkpoints to identify the benefits of immunotherapy. Subjects: Bioinformatics, Genomics, Oncology, Thoracic surgery.

SYT16 is a prognostic biomarker and correlated with immune infiltrates in glioma: A study based on TCGA data.

BACKGROUND: Glioma is the most lethal primary brain tumor. Lower-grade glioma (LGG) is the crucial pathological type of Glioma. Immune-infiltration of the tumor microenvironment positively associated with overall survival in LGG. SYT16 is a gene has not been reported in cancer. We assess the role of SYT16 in LGG, via the publicly available TCGA database. METHODS: Gene Expression Profiling Interactive Analysis (GEPIA) was used to analyze the expression of SYT16 in LGG. We evaluated the influence of SYT16 on survival of LGG patients by survival module. Then, datasets of LGG were downloaded from TCGA. The correlations between the clinical information and SYT16 expression were analyzed using logistic regression. Univariable survival and Multivariate Cox analysis was used to compare several clinical characteristics with survival. we also explore the correlation between SYT16 and cancer immune infiltrates using CIBERSORT and correlation module of GEPIA. Gene set enrichment analysis (GSEA) was performed using the TCGA dataset. In addition, we use TIMER to explore the collection of SYT16 Expression and Immune Infiltration Level in LGG and to explore cumulative survival in LGG. RESULTS: The univariate analysis using logistic regression, indicated that increased SYT16 expression significantly correlated with the tumor grade. Moreover, multivariate analysis revealed that the up-regulated SYT16 expression is an independent prognostic factor for good prognosis. Specifically, SYT16 expression level has significant negative correlations with infiltrating levels of B cell, CD4+ T cells, Macrophages, Neutrophils and DCs in LGG. In addition, GSEA identified ingle organism behavior, gated channel activity, cognition, transporter complex and ligand gated channel activity  in Gene Ontology (GO) were differentially enriched in the high SYT16 expression phenotype pathway. Neuroactive ligand receptor interaction, calcium signaling pathway, long term potentiation, type II diabetes mellitus and long term depression were identified as differentially enriched  pathway in Kyoto Encyclopedia of Genes and Genomes (KEGG). CONCLUSION: SYT16 is a Prognostic Biomarker and Correlated with Immune Infiltrates in LGG.

Prognostic biomarker MITD1 and its correlation with immune infiltrates in hepatocellular carcinoma (HCC).

BACKGROUND: Hepatocellular carcinoma (HCC) is globally recognized as one of the most frequently occurring primary malignant liver tumors, making the identification of HCC biomarkers critically important. The protein MITD1 (Microtubule Interacting and Trafficking Domain containing 1) has been shown to interact with ESCRT-III and participates in cytokinesis, the last step in cell division. This is the first investigation into the expression of MITD1 and its prognostic value, potential biological functions and effects on the immune system in HCC patients. METHODS: The gene expression and clinicopathology analysis, enrichment analysis and immune infiltration analysis are based on data obtained from The Cancer Genome Atlas (TCGA), with additional bioinformatics analyses performed. The statistical analysis was conducted in R and immune responses of MITD1 expression in HCC were analyzed using TIMER and CIBERSORT. In addition, GEPIA, K-M survival analysis and data from the HPA were used to validate the outcomes. RESULTS: Our results highlighted that MITD1 plays a key role as an independent prognostic factor in patients with HCC. MITD1 expression was associated with age, grade, stage and tumor status. GSEA revealed that MITD1 is closely correlated with cell cycle control via the NOTCH signaling pathway. CIBERSORT analysis revealed that the amount of NK cells decreased when MITD1 expression was high. CONCLUSIONS: The identification of MITD1 as a new biomarker for HCC could help elucidate how changes in cytokinesis and the immune environment promote liver cancer development. With further analysis, MITD1 may be able to serve as a predictor for human HCC prognosis.

Increased expression of TTC21A in lung adenocarcinoma infers favorable prognosis and high immune infiltrating level.

BACKGROUND: Lung adenocarcinoma (LUAD) is a crucial pathological type of lung cancer. Immune-infiltration of the tumor microenvironment positively associated with overall survival in LUAD. TTC21A is a gene has not reported in cancer, and the mechanism behind it is still unclear. Our study assesses TTC21A role in LUAD, via TCGA data. METHODS: GEPIA was utilized to analyze the expression of TTC21A in LUAD. We evaluated the influence of TTC21A on survival of LUAD patients by survival module. Then, data sets of LUAD were downloaded from TCGA. The correlations between clinical information and TTC21A expression were analyzed using logistic regression. Clinicopathologic characteristics associated with overall survival in TCGA patients using Cox regression. In addition, we explored the correlation between TTC21A and cancer immune infiltrates using CIBERSORT and "Correlation" module of GEPIA. RESULTS: The univariate analysis using logistic regression, wherein TTC21A expression served as a categorical dependent variable (with a median expression value of 2.5), indicated that increased TTC21A expression is significantly correlated with pathological stage, tumor status and lymph nodes. Moreover, multivariate analysis revealed that the up-regulated TTC21A expression, negative results of pathological stage and distant metastasis are independent prognostic factors for good prognosis. Specifically, a positive correlation between increased TTC21A expression and immune infiltrating level of B cells, Neutrophils, Mast cells and T cells was established using CIBERSORT analysis. Furthermore, we confirmed it in "correlation" module of GEPIA. CONCLUSION: Together with all these findings, increased TTC21A expression correlates with favorable prognosis and increased proportion of immune cells, such as B cells, Neutrophils, Mast cells and T cells in LUAD. These conclusions indicate that TTC21A could serve as a potential biomarker to assess prognosis and immune infiltration level in LUAD.

Reduced GRAMD1C expression correlates to poor prognosis and immune infiltrates in kidney renal clear cell carcinoma.

There has been an increase in the mortality rate and morbidity of kidney cancer (KC) with kidney renal clear cell carcinoma (KIRC) being the most common subtype of KC. GRAMD1C (GRAM Domain Containing 1C) has not been reported to relate to prognosis and immunotherapy in any cancers. Using bioinformatics methods, we judged the prognostic value of GRAMD1C expression in KIRC and investigated the underlying mechanisms of GRAMD1C affecting the overall survival of KIRC based on data downloaded from The Cancer Genome Atlas (TCGA). The outcome revealed that reduced GRAMD1C expression could be a promising predicting factor of poor prognosis in kidney renal clear cell carcinoma. Meanwhile, GRAMDIC expression was significantly correlated to several tumor-infiltrating immune cells (TIICs), particularly the regulatory T cells (Tregs). Furthermore, GRAMD1C was most significantly associated with the mTOR signaling pathway, RNA degradation, WNT signaling pathway, toll pathway and AKT pathway in KIRC. Thus, GRAMD1C has the potential to become a novel predictor to evaluate prognosis and immune infiltration for KIRC patients.

Effect of region on the Outcome of Patients Receiving PD-1/PD-L1 Inhibitors for Advanced Cancer.

BACKGROUND: Regional differences were associated with cancer incidence and mortality. However, the correlation between regional differences and cancer immunotherapy efficacy was still not evaluated. In this study, we performed a meta-analysis to investigate whether regional differences play a role in efficacy of PD-1/L1 inhibitors in cancer patients. METHODS: A meticulous review of relevant randomized controlled trials that were sourced from the PubMed, Embase and MEDLINE databases. Overall survival (OS) and progression-free survival (PFS) were the primary outcome and secondary outcome in our study, respectively. We also assessed difference on the hazard ratio (HR) between European and North American groups. RESULTS: A total of 14 randomized clinical trials including 9387 patients were finally eligible for meta-analysis in our study. With respect to the pooled HR in treatment with PD-1/L1 inhibitors, North American patients presented OS as 0.60 (95% CI 0.53 to 0.67), and PFS as 0.49 (95% CI 0.40 to 0.59), whereas European patients presented OS as 0.76 (95% CI 0.62 to 0.90), and PFS as 0.58 (95% CI 0.44 to 0.72), relative to their corresponding control groups. OS efficacy thus varied significantly (Pheterogeneity = 0.028) between North American and European patients when treated with PD-1/L1 inhibitors. CONCLUSIONS: Our findings were very surprising especially considering the higher prevalence of cancer in Europe. Although PD-1/L1 inhibitors improved OS and PFS in both North American and European patients compared with controls, the magnitude of benefit was region-dependent. North American patients can benefit more from PD-1/L1 inhibitors than European patients. More researches were urgently demanded to explore its potential molecular mechanisms.

Identification of novel biomarkers and candidate small molecule drugs in non-small-cell lung cancer by integrated microarray analysis.

Background: Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC. Methods: The microarray datasets of GSE18842, GSE30219, GSE31210, GSE32863 and GSE40791 from Gene Expression Omnibus database were downloaded. The differential expressed genes (DEGs) between NSCLC and normal samples were identified by limma package. The construction of protein-protein interaction (PPI) network, module analysis and enrichment analysis were performed using bioinformatics tools. The expression and prognostic values of hub genes were validated by GEPIA database and real-time quantitative PCR. Based on these DEGs, the candidate small molecules for NSCLC were identified by the CMap database. Results: A total of 408 overlapping DEGs including 109 up-regulated and 296 down-regulated genes were identified; 300 nodes and 1283 interactions were obtained from the PPI network. The most significant biological process and pathway enrichment of DEGs were response to wounding and cell adhesion molecules, respectively. Six DEGs (PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5) which significantly up-regulated in NSCLC tissues, were selected as hub genes according to the results of module analysis. The GEPIA database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. Additionally, CMap predicted the 20 most significant small molecules as potential therapeutic drugs for NSCLC. DL-thiorphan was the most promising small molecule to reverse the NSCLC gene expression. Conclusions: Based on the gene expression profiles of 696 NSCLC samples and 237 normal samples, we first revealed that PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5 could act as the promising novel diagnostic and therapeutic targets for NSCLC. Our work will contribute to clarifying the molecular mechanisms of NSCLC initiation and progression.

The promising novel biomarkers and candidate small molecule drugs in lower-grade glioma: Evidence from bioinformatics analysis of high-throughput data.

Overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past 30 years, with survival averaging approximately 7 years. This study aimed to identify novel promising biomarkers of LGG and reveal its potential molecular mechanisms by integrated bioinformatics analysis. The microarray datasets of GSE68848 and GSE4290 were selected from GEO database for integrated analysis. In total, 293 overlapping differentially expressed genes (DEGs) were detected using the limma package. One hundred and eighty-eight nodes with 603 interactions were obtained from the establishment of protein-protein interaction (PPI) network. Functional and signaling pathway enriched were significantly correlated with the synapse and calcium signaling pathway, respectively. Module analysis revealed eight hub genes with high connectivity, which included CHRM1, DLG2, GABRD, GRIN1, HTR2A, KCNJ3, KCNJ9, and NUSAP1, and they were markedly correlated with patients' prognosis. The mining of the Gene Expression Profiling Interactive Analysis database and qPCR further confirmed the abnormal expression of these key genes with their prognostic value in LGG. We eventually predicted the 20 most vital small molecule drugs, which potentially reverse the carcinogenic state of LGG, as per the CMap (connectivity map) database and these DEGs, and MS-275 (enrichment score = -0.939) was considered as the most promising small molecule to treat LGG. In conclusion, our study provided eight reliable novel molecular biomarkers for diagnosis, prognosis prediction, and treatment targets for LGG. These conclusions will contribute to a better comprehension of molecular mechanisms fundamental to LGG occurrence and progression, and providing new insights for future development of genomic individualized treatment in LGG.

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gastric cancer.

BACKGROUND AND OBJECTIVE: The underlying molecular mechanisms of gastric cancer (GC) have yet not been investigated clearly. In this study, we aimed to identify hub genes involved in the pathogenesis and prognosis of GC. METHODS: We integrated five microarray datasets from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between GC and normal samples were analyzed with limma package. Gene ontology (GO) and KEGG enrichment analysis were performed using DAVID. Then we established the protein-protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING). The prognostic analysis of hub genes were performed through Gene Expression Profiling Interactive Analysis (GEPIA). Additionally, we used real-time quantitative PCR to validate the expression of hub genes in 5 pairs of tumor tissues and corresponding adjacent tissues. Finally, the candidate small molecules as potential drugs to treat GC were predicted in CMap database. RESULTS: Through integrating five microarray datasets, a total of 172 overlap DEGs were detected including 79 up-regulated and 93 down-regulated genes. Biological process analysis of functional enrichment showed these DEGs were mainly enriched in digestion, collagen fibril organization and cell adhesion. Signaling pathway analysis indicated that these DEGs played an vital in ECM-receptor interaction, focal adhesion and metabolism of xenobiotics by cytochrome P450. Protein-protein interaction network among the overlap DEGs was established with 124 nodes and 365 interactions. Three DEGs with high degree of connectivity (NID2, COL4A1 and COL4A2) were selected as hub genes. The GEPIA database confirmed that overexpression levels of hub genes were significantly associated with worse survival of patients. Finally, the 20 most significant small molecules were obtained based on CMap database and spiradoline was the most promising small molecule to reverse the GC gene expression. CONCLUSIONS: Our results indicated that NID2, COL4A1 and COL4A2 could be the potential novel biomarkers for GC diagnosis prognosis and the promising therapeutic targets. The present study may be crucial to understanding the molecular mechanism of GC initiation and progression.

The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high-throughput data.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is the most common subtype of renal tumor. However, the molecular mechanisms of KIRC pathogenesis remain little known. The purpose of our study was to identify potential key genes related to the occurrence and prognosis of KIRC, which could serve as novel diagnostic and prognostic biomarkers for KIRC. METHODS: Three gene expression profiles from gene expression omnibus database were integrated to identify differential expressed genes (DEGs) using limma package. Enrichment analysis and PPI construction for these DEGs were performed by bioinformatics tools. We used Gene Expression Profiling Interactive Analysis (GEPIA) database to further analyze the expression and prognostic values of hub genes. The GEPIA database was used to further validate the bioinformatics results. The Connectivity Map was used to identify candidate small molecules that could reverse the gene expression of KIRC. RESULTS: A total of 503 DEGs were obtained. The PPI network with 417 nodes and 1912 interactions was constructed. Go and KEGG pathway analysis revealed that these DEGs were most significantly enriched in excretion and valine, leucine, and isoleucine degradation, respectively. Six DEGs with high degree of connectivity (ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA) were selected as hub genes, which significantly associated with worse survival of patients. Finally, we identified the top 20 most significant small molecules and pipemidic acid was the most promising small molecule to reverse the KIRC gene expression. CONCLUSIONS: This study first uncovered six key genes in KIRC which contributed to improving our understanding of the molecular mechanisms of KIRC pathogenesis. ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA could serve as the promising novel biomarkers for KIRC diagnosis, prognosis, and treatment.