• Alzheimer’s disease
• Mendelian randomization
• expression quantitative trait loci (eQTLs)
• genome-wide association studies (GWAS)
• single sample gene set enrichment analysis (ssGSEA)

• brain
• genomics

• Ischemic Stroke/genetics
• Ischemic Stroke/metabolism
• Humans
• Protein Interaction Maps/genetics
• Gene Regulatory Networks
• Gene Expression Profiling

• Adenoma-carcinoma sequence
• Advanced colorectal adenoma
• Colorectal cancer
• Immune repertoire

• Humans
• Colorectal Neoplasms/genetics
• Colorectal Neoplasms/immunology
• Colorectal Neoplasms/pathology
• Adenoma/genetics
• Adenoma/immunology
• Adenoma/pathology
• Republic of Korea
• Computational Biology/methods
• Gene Expression Regulation, Neoplastic
• Male
• Female
• Tumor Microenvironment/genetics
• Tumor Microenvironment/immunology
• Prognosis
• Middle Aged
• Aged
• Biomarkers, Tumor/genetics
• Kaplan-Meier Estimate
• Gene Expression Profiling

• NRF-2021R1A2C1008804 National Research Foundation of Korea

• Cancer therapy
• Drug resistance
• Histone demethylation
• JmjC domain
• KDM4A

• Gene Expression Omnibus (GEO)
• chemotherapy
• lung adenocarcinoma
• risk score

• Humans
• Prognosis
• Adenocarcinoma of Lung/drug therapy
• Adenocarcinoma of Lung/genetics
• Lung Neoplasms/drug therapy
• Lung Neoplasms/genetics
• Databases, Factual
• Health Status

• 2019AD32097 Science and Technology Plan Project of Jiaxing (Science and Technology Innovation Research for Livelihood)
• 2021AY30005 Science and Technology Plan Project of Jiaxing (Science and Technology Innovation Research for Livelihood)

• cancer
• computational biology and bioinformatics
• biomarkers

• Humans
• Tumor Microenvironment/genetics
• Prognosis
• Adenocarcinoma of Lung/genetics
• Adenocarcinoma of Lung/therapy
• CD3 Complex
• Immunotherapy
• Lung Neoplasms/genetics
• Lung Neoplasms/therapy
• Carrier Proteins
• Microfilament Proteins

• Scientific Research Project of Suining Central Hospital

• Breast invasive carcinoma
• Immune infiltration
• Prognosis
• Risk model
• m1A-related regulators

• 81802479 National Outstanding Youth Science Fund Project of National Natural Science Foundation of China

• Cox regression analysis
• Immunity
• LASSO analysis
• LUAD
• Signature
• STK11

• immune checkpoint molecules
• non-small cell lung cancer
• prediction
• prognosis
• signature

• Differentially expressed genes
• Lung adenocarcinoma
• Nomogram
• Prognostic signature
• Risk score

• metabolism
• molecular classification
• oxidative stress
• predictive preventive personalized medicine (PPPM)
• stomach adenocarcinoma

• Humans
• Precision Medicine
• Oxidative Stress/genetics
• Adenocarcinoma/genetics
• Stomach Neoplasms/genetics

• LUAD
• immune infiltration
• immunotherapy
• prognosis
• tumor microenvironment

• National Natural Science Foundation of China
• Natural Science Foundation of Jiangxi Province

Head and neck squamous cell carcinoma (HNSCC) is a type of malignant tumor with an increasing incidence worldwide and a meager 5-year survival rate. It is known that nuclear transporter factor 2 (NTF2) transports related proteins into the nucleus physiologically. However, the role of NTF2 in HNSCC remains unclear.

In this study, RNA-Seq data of HNSCC samples with corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. In addition, other expression profiling data were downloaded from the Gene Expression Omnibus (GEO) database. The differential expressions of NTF2, along with the overall survival (OS) rates were identified and analyzed. Then, the clinical features and expression levels of NTF2 were utilized to develop a prognostic model. The study also utilized the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods to determine the related pathways of NTF2. Furthermore, the Tumor Immune Estimation Resource (TIMER) database was referenced to discover the immune correlation of NTF2. In this research investigation, RT-qPCR, western blotting, Cell Counting Kit-8 (CCK-8) assay, wound-healing assay, and immunohistochemical (IHC) staining methods were adopted to perform experimental verifications.

This study’s results confirmed that the NTF2 expressions were significantly increased in HNSCC tissue when compared with normal tissue. In addition, the high expression levels of NTF2 were found to be associated with poor prognoses, which was confirmed via the IHC validations of HNSCC samples with survival data. The results of functional enrichment analysis showed that the NTF2 was associated with epithelial cell growth, skin differentiation, keratosis, and estrogen metabolism. Furthermore, the expressions of NTF2 were determined to be negatively involved with immune infiltrations and correlated with immune checkpoint blockade (ICB) responses following various ICB therapy strategies. The results of the CCK-8 assay and wound-healing assay confirmed the NTF2’s promoting effects on the proliferation and migration of tumor cells.

This study defined a novel prognostic model associated with the expressions of NTF2, which was shown to be independently related to the OS of HNSCC. It was concluded in this study that NTF2 might be a potential diagnostic and prognostic biomarker for HNSCC.

Background: Multiple factors influence the survival of patients with lung adenocarcinoma (LUAD). Specifically, the therapeutic outcomes of treatments and the probability of recurrence of the disease differ among patients with the same stage of LUAD. Therefore, effective prognostic predictors need to be identified.

Methods: Based on the tumor mutation burden (TMB) data obtained from The Cancer Genome Atlas (TCGA) database, LUAD patients were divided into high and low TMB groups, and differentially expressed glycolysis-related genes between the two groups were screened. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to obtain a prognostic model. A receiver operating characteristic (ROC) curve and a calibration curve were generated to evaluate the nomogram that was constructed based on clinicopathological characteristics and the risk score. Two data sets (GSE68465 and GSE11969) from the Gene Expression Omnibus (GEO) were used to verify the prognostic performance of the gene. Furthermore, differences in immune cell distribution, immune-related molecules, and drug susceptibility were assessed for their relationship with the risk score.

Results: We constructed a 5-gene signature (FKBP4, HMMR, B4GALT1, SLC2A1, STC1) capable of dividing patients into two risk groups. There was a significant difference in overall survival (OS) times between the high-risk group and the low-risk group (p < 0.001), with the low-risk group having a better survival outcome. Through multivariate Cox analysis, the risk score was confirmed to be an independent prognostic factor (HR = 2.709, 95% CI = 1.981–3.705, p < 0.001), and the ROC curve and nomogram exhibited accurate prediction performance. Validation of the data obtained in the GEO database yielded similar results. Furthermore, there were significant differences in sensitivity to immunotherapy, cisplatin, paclitaxel, gemcitabine, docetaxel, gefitinib, and erlotinib between the low-risk and high-risk groups.

Conclusion: Our results reveal that glycolysis-related genes are feasible predictors of survival and the treatment response of patients with LUAD.

Atherosclerosis is the predominant cause of cardiovascular diseases. Existing studies suggest that the development of atherosclerosis is closely related to inflammation and immunity, but whether there are differences and similarities between atherosclerosis occurring at different sites is still unknown. We elucidated the pathological characteristics of peripheral vascular diseases by using bioinformatic analyses on immune cells and inflammation-related gene expression in atherosclerotic arteries and plaques.

Eight data sets regarding atherosclerosis were downloaded from the Gene Expression Omnibus database. Human immune genes were obtained from the IMMPORT website. The samples were scored and divided into high- and low-immune groups. Then the samples were analysed using weighted gene co-expression network analysis, while the modules were analysed using functional enrichment. The protein–protein interaction network was constructed using the STRING and Cytoscape databases. The hub immune genes were screened, and the correlation between hub immune genes and immune cells was analysed.

Immune cells and their functions were significantly different during atherosclerosis development. The infiltration proportion of immune cells was approximately similar in samples from different sources of patients with carotid atherosclerosis. However, the sensitivity of lower extremity atherosclerosis samples to immune cells is lower than that of carotid atherosclerosis samples.The samples from the plaque and artery were mainly infiltrated by macrophages, T cells and mast cells. After immune cells were assessed, resting NK cells, activated mast cells and M0 macrophages were found to be key immune cells in atherosclerosis and plaque formation. In addition, CCL4, TLR2, IL1B and PTPRC were considered to be immune marker genes in atherosclerosis development. Conclusion. Bioinformatic data analysis confirms the essential role of immune cells in cardiovascular diseases, and also indicates some differences of immune and inflammation characteristics of atherosclerosis between carotid and lower extremity arteries.

Cancer stem cells (CSCs) can induce recurrence and chemotherapy resistance of lung adenocarcinoma (LUAD). Reliable markers identified based on CSC characteristic of LUAD may improve patients' chemotherapy response and prognosis. OCLR was used to calculate mRNA expression-based stemness index (mRNAsi) of LUAD patients' data in TCGA. Association analysis of mRNAsi was performed with clinical features, somatic mutation, and tumor immunity. A prognostic prediction model was established with LASSO Cox regression. Kaplan-Meier Plotter (KM-plotter) and time-dependent ROC were applied to assess signature performance. For LUAD, univariate and multivariate Cox analysis was performed to identify independent prognostic factors. LUAD tissues showed a noticeably higher mRNAsi in than nontumor tissues, and it showed significant differences in T, N, M, AJCC stages, and smoking history. The most frequently mutated gene was TP53, with a higher mRNAsi relating to more frequent mutation of TP53. The mRNAsi was significantly negatively correlated with immune score, stromal score, and ESTIMATE score in LUAD. The blue module was associated with mRNAsi. The 5-gene signature was confirmed as an independent indicator of LUAD prognosis that could promote personalized treatment of LUAD and accurately predict overall survival (OS) of LUAD patients.

• ADAM12
• lung adenocarcinoma
• prognostic indicator
• tumor immune microenvironment

The platelet-derived growth factor (PDGF) pathway is important in angiogenesis, which can accelerate the formation of vessels in tumor tissues and promote the progression of malignant tumors. To clarify the role of PDGF in the occurrence of renal cell carcinoma and targeted drug resistance, we explored the pathway in kidney renal clear cell carcinoma (KIRC) through bioinformatics analysis with the aim of supporting comprehensive and individualized therapy. First, we found 40 genes related to the PDGF pathway through gene set enrichment analysis and then obtained their expressions and clinical data in 32 different cancers from The Cancer Genome Atlas (TCGA). Mutations in these genes (including copy number and single-nucleotide variation) and mRNA expression were also detected. Next, we conducted a hazard ratio analysis to determine whether the PDGF pathway genes were risk or protective factors in tumors. Although PDGF-related genes acted as traditional oncogenes and were closely related to tumor angiogenesis in many cancers, our results indicated that most genes had a protective role in KIRC. We further analyzed the methylation modification of PDGF pathway genes and found that they were prevalent in 32 different cancers. Furthermore, 539 KIRC samples obtained from TCGA were divided into three clusters based on the mRNA expression of PDGF genes, including normal, inactive, and active PDGF gene expressions. The results from survival curve analysis indicated that the active PDGF cluster of patients had the best survival rate. Using the three clusters, we studied the correlation between the PDGF pathway and 12 common targeted drugs, as well as classical oncogenes and infiltrating immune cells. A prognostic risk model was constructed based on the PDGF score using LASSO-Cox regression analysis to analyze the value of the model in predicting the prognosis of patients with KIRC. Finally, 11 genes were selected for LASSO regression analysis, and the results demonstrated the high predictive value of this risk model and its close relationship with the pathological characteristics of KIRC (metastasis, size, grade, stage, etc.). In addition, we found that the risk score was an independent risk factor correlated with overall survival through univariate and multivariate analyses and a nomogram was built to assess patient prognosis. In conclusion, the occurrence and development of KIRC may be associated with an abnormally activated PDGF pathway, which may be a potential drug target in the treatment of KIRC.

• Innovative and Scientific Research Project of Sichuan Medical Youth

Bladder cancer (BLCA) is the most common malignancy whose early diagnosis can ensure a better prognosis. However, the predictive accuracy of commonly used predictors, including patients’ general condition, histological grade, and pathological stage, is insufficient to identify the patients who need invasive treatment. Autophagy is regarded as a vital factor in maintaining mitochondrial function and energy homeostasis in cancer cells. Whether autophagy-related genes (ARGs) can predict the prognosis of BLCA patients deserves to be investigated. Based on BLCA data retrieved from the Cancer Genome Atlas and ARGs list obtained from the Human Autophagy Database website, we identified prognosis-related differentially expressed ARGs (PDEARGs) through Wilcox text and constructed a PDEARGs-based prognostic model through multivariate Cox regression analysis. The predictive accuracy, independent forecasting capability, and the correlation between present model and clinical variables or tumor microenvironment were evaluated through R software. Enrichment analysis of PDEARGs was performed to explore the underlying mechanism, and a systematic prognostic signature with nomogram was constructed by integrating clinical variables and the aforementioned PDEARGs-based model. We found that the risk score generated by PDEARGs-based model could effectively reflect deteriorated clinical variables and tumor-promoting microenvironment. Additionally, several immune-related gene ontology terms were significantly enriched by PDEARGs, which might provide insights for present model and propose potential therapeutic targets for BLCA patients. Finally, a systematic prognostic signature with promoted clinical utility and predictive accuracy was constructed to assist clinician decision. PDEARGs are valuable prognostic predictors and potential therapeutic targets for BLCA patients.

• AUC, area under the curve
• CCLE, Cancer Cell Line Encyclopedia
• FPKM, fragments per kilobase of transcript per million
• GEO, Gene Expression Omnibus
• GO, gene ontology
• GSVA, gene set variation analysis
• HIC, immunohistochemistry
• HR, hazard ratio
• ICIs, immune checkpoints inhibitors
• IF, immunofluorescence
• Immune checkpoints
• LUAD, lung adenocarcinoma
• Lung adenocarcinoma
• NK cell, natural killer cell
• PBMC, Peripheral Blood Mononuclear Cell
• ROC, receiver operating characteristic
• RSF, random survival forest
• RT-qPCR, Real-time Quantitative Polymerase Chain Reaction
• Small nucleolar RNAs
• TCGA, The Cancer Genome Atlas
• TIISR signature
• TIISR, tumor-infiltrating immune-related snoRNA
• TIME, tumor immune microenvironment
• TPM, transcripts per kilobase million
• TSI, tissue specificity index
• Tumor cell immune infiltration
• ncRNA, noncoding RNA
• snoRNAs, small nucleolar RNAs
• ssGSEA, single-sample gene set enrichment analysis

Previous researches have highlighted that low-expressing deoxyribonuclease1-like 3 (DNASE1L3) may play a role as a potential prognostic biomarker in several cancers. However, the diagnosis and prognosis roles of DNASE1L3 gene in lung adenocarcinoma (LUAD) remain largely unknown. This research aimed to explore the diagnosis value, prognostic value, and potential oncogenic roles of DNASE1L3 in LUAD. We performed bioinformatics analysis on LUAD datasets downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), and jointly analyzed with various online databases. We found that both the mRNA and protein levels of DNASE1L3 in patients with LUAD were noticeably lower than that in normal tissues. Low DNASE1L3 expression was significantly associated with higher pathological stages, T stages, and poor prognosis in LUAD cohorts. Multivariate analysis revealed that DNASE1L3 was an independent factor affecting overall survival (HR = 0.680, p = 0.027). Moreover, decreased DNASE1L3 showed strong diagnostic efficiency for LUAD. Results indicated that the mRNA level of DNASE1L3 was positively correlated with the infiltration of various immune cells, immune checkpoints in LUAD, especially with some m6A methylation regulators. In addition, enrichment function analysis revealed that the co-expressed genes may participate in the process of intercellular signal transduction and transmission. GSEA indicated that DNASE1L3 was positively related to G protein-coupled receptor ligand biding (NES = 1.738; P adjust = 0.044; FDR = 0.033) and G alpha (i) signaling events (NES = 1.635; P adjust = 0.044; FDR = 0.033). Our results demonstrated that decreased DNASE1L3 may serve as a novel diagnostic and prognostic biomarker associating with immune infiltrates in lung adenocarcinoma.

• DNASE1L3
• LUAD
• TCGA database
• diagnostic
• prognostic

Lung adenocarcinoma (LUAD) is a heterogeneous disease. However the inner sub‐groups of LUAD have not been fully studied. Markers predicted the sub‐groups and prognosis of LUAD are badly needed.

To identify biomarkers associated with the sub‐groups and prognosis of LUAD.

Using nonnegative matrix factorization (NMF) clustering, LUAD patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) datasets and LUAD cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) dataset were divided into different sub‐consensuses based on the gene expression profiling. The overall survival of LUAD patients in each sub‐consensus was determined by Kaplan‐Meier survival analysis. The common genes which were differentially expressed in each sub‐consensus of LUAD patients and LUAD cell lines were identified using TBtools. The predictive accuracy of TPX2 and SELENBP1 for theinner sub‐consensuses of LUAD was determined by Receiver operator characteristic (ROC) analysis. The Kaplan‐Meier survival analysis was also used to test the prognostic significance of TPX2 and SELENBP1 in LUAD patients.

Using nonnegative matrix factorization clustering, LUAD patients in The Cancer Genome Atlas (TCGA), GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets were divided into three sub‐consensuses. Sub‐consensus3 LUAD patients were with low overall survival and were with high TP53 mutations. Similarly, LUAD cell lines were also divided into three sub‐consensuses by NMF method, and sub‐consensus2 cell lines were resistant to EGFR inhibitors. Identification of the common genes which were differentially expressed in different sub‐consensuses of LUAD patients and LUAD cell lines revealed that TPX2 was highly expressed in sub‐consensus3 LUAD patients and sub‐consensus2 LUAD cell lines. On the contrary, SELENBP1 was highly expressed in sub‐consensus1 LUAD patients and sub‐consensus1 LUAD cell lines. The expression levels of TPX2 and SELENBP1 could distinguish sub‐consensus3 LUAD patients or sub‐consensus2 LUAD cell lines from other sub‐consensuses of LUAD patients or cell lines. Moreover, compared with normal lung tissues, TPX2 was highly expressed, while, SELENBP1 was lowly expressed in LUAD tissues. Furthermore, the higher expression levels of TPX2 were associated with the lower relapse‐free survival and the lower overall survival of LUAD patients. While, the higher expression levels of SELENBP1 were associated with the higher relapse‐free survival and higher overall survival. At last, we showed that TP53 mutant LUAD patients were with higher TPX2 and lower SELENBP1 expressions.

Both iCluster and NMF method are proved to be robust LUAD classification systems. However, the LUAD patients in different iclusters had no significant clinical overall survival, while, sub‐consensus3 LUAD patients from NMF classification were with lower overall survival than other sub‐consensuses.

By integrated analysis of 1765 LUAD patients and 64 LUAD cell lines, we showed that NMF was a robust inner sub‐consensuses classification method of LUAD. TPX2 and SELENBP1 were differentially expressed in different LUAD sub‐ consensuses, and predicted the inner sub‐consensuses of LUAD with high accuracy. TPX2 was an unfavorable prognostic biomarker of LUAD which was up‐regulated in LUAD tissues and associated with the low overall survival of LUAD. SELENBP1 was a favorable prognostic biomarker of LUAD which was down‐regulated in LUAD tissues and associated with the prolonged overall survival of LUAD.

• SELENBP1
• TPX2
• nonnegative matrix factorization
• sub-consensus of lung adenocarcinoma

• NSCLC
• NSP3 (SH2D3C)
• dysfunctional T-cell phenotypes
• immune infiltrations
• in silico study
• organosulfur compounds
• pharmacokinetics
• tumor microenvironment

• MOST 109-2327-B-010-005 Ministry of Science and Technology

• TCGA
• angiogenesis pathway
• bioinformatics
• kidney renal clear cell carcinoma
• prognosis

• ATG5
• Autophagy
• Chemoresistance
• Macrophage M2 polarization
• PI3K/AKT pathway

• Bioinformatics analysis
• Biomarkers
• Embryonic germline genes
• Lung adenocarcinoma
• Prognosis

• ferroptosis
• lung squamous cell carcinoma
• overall survival
• prognostic signature

• ADAM12
• biomarker
• colorectal adenocarcinoma
• immune infiltration
• prognosis

• Guangxi Zhuang Region Health Department
• Science and Technology Department of Guangxi Zhuang Autonomous
• Specific Research Project of Guangxi for Research Bases and Talents

• Immune infiltrates
• Lung adenocarcinoma
• Nomogram
• Signature
• Tumor mutation burden

Although many biomarkers have been reported for detecting glioma, the prognosis for the disease remains poor, and therefore, new biomarkers need to be identified. GNG5, which is part of the G-protein family, has been associated with different malignant tumors, though the role of GNG5 in glioma has not been studied. Therefore, we aimed to identify the relationship between GNG5 and glioma prognosis and identify a new biomarker for the diagnosis and treatment of gliomas.

We used data on more than a thousand gliomas from multiple databases and clinical data to determine the expression of GNG5 in glioma. Based on clinical data and CGGA database, we identified the correlation between GNG5 and multiple molecular and clinical features and prognosis using various analytical methods. Co-expression analysis and GSEA were performed to detect GNG5-related genes in glioma and possible signaling pathways involved. ESTIMATE, ssGSEA, and TIMER were used to detect the relationship between GNG5 and the immune microenvironment. Functional experiments were performed to explore the function of GNG5 in glioma cells.

GNG5 is highly expressed in gliomas, and its expression level is positively correlated with pathological grade, histological type, age, and tumor recurrence and negatively correlated with isocitrate dehydrogenase mutation, 1p/19 co-deletion, and chemotherapy. Moreover, GNG5 as an independent risk factor was negatively correlated with the overall survival time. GSEA revealed the potential signaling pathways involved in GNG5 function in gliomas, including cell adhesion molecules signaling pathway. The ssGSEA, ESTIMATE, and TIMER based analysis indicated a correlation between GNG5 expression and various immune cells in glioma. In vivo and in vitro experiments showed that GNG5 could participate in glioma cell proliferation and migration.

Based on the large data platform and the use of different databases to corroborate results obtained using various datasets, as well as in vitro and in vivo experiments, our study reveals for the first time that GNG5, as an oncogene, is overexpressed in gliomas and can inhibit the proliferation and migration of glioma cells and lead to poor prognosis of patients. Thus, GNG5 is a potential novel biomarker for the clinical diagnosis and treatment of gliomas.

The online version contains supplementary material available at 10.1186/s12935-021-01935-7.

• Biomarker
• GNG5
• Glioma
• Oncogene

• LUAD
• TCGA
• invasion genes
• molecular subtype
• multi-gene signature

The prognostic prediction of hepatocellular carcinoma (HCC) is still challenging. Immune cells play a crucial role in tumor initiation, progression, and drug resistance. However, prognostic value of immune-related genes in HCC remains to be further clarified. In this study, the mRNA expression profiles and corresponding clinical information of HCC patients were downloaded from public databases. Then, we estimated the abundance of immune cells and identified the differentially infiltrated and prognostic immune cells. The weighted gene co-expression network analysis (WGCNA) was performed to identify immune-related genes in TCGA cohort and GEO cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a risk-scoring model in the TCGA cohort. HCC patients from the GSE14520 datasets were utilized for risk model validation. Our results found that high level of dendritic cell (DC) infiltration was associated with poor prognosis. Over half of the DC-related genes (58.2%) were robustly differentially expressed between HCC and normal specimens in the TCGA cohort. 17 differentially expressed genes (DEGs) were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. A 12-gene risk-scoring model was established to evaluate the prognosis of HCC. The high-risk group exhibits significantly lower OS rate of HCC patients than the low-risk group. The risk-scoring model shows benign predictive capacity in both GEO dataset and TCGA dataset. The 12-gene risk-scoring model may independently perform prognostic value for HCC patients. Receiver operating characteristic (ROC) curve analysis of the risk-scoring model in GEO cohort and TCGA cohort performed well in predicting OS. Taken together, the 12-gene risk-scoring model could provide prognostic and potentially predictive information for HCC. SDC3, NCF2, BTN3A3, and WARS were noticed as a novel prognostic factor for HCC.

• co-expression network construction
• hepatocellular carcinoma
• immune-related gene
• overall survival
• risk-scoring model

• ESTIMATE
• GEO
• TCGA
• bladder cancer
• overall survival
• tumor microenvironment

Background: Renal cell carcinoma (RCC) is the most common malignancy in the urinary system. Despite substantial improvements in available treatment options, the survival outcome of advanced RCC is unsatisfactory. Identifying novel biomarkers to assist in early diagnosis and to screen patients who are sensitive to immunotherapy would be beneficial. CD248 is a promising candidate that deserves to be investigated.

Methods: The Cancer Genome Atlas (TCGA) data set and clinical specimens were adopted to analyze the expression of CD248 between normal and tumor tissues. Univariate and multivariate Cox regression analyses were employed to identify independent prognostic factors and construct a CD248-based prognostic signature. The correlation among the present signature, tumor-infiltrating immune cells (TIICs), the tumor mutation burden (TMB), and immunomodulatory molecules was evaluated. The weighted gene co-expression network analysis (WGCNA), the enrichment analysis, and the miRNA correlation analysis were performed to explore the underlying mechanism of CD248 in the progression of RCC.

Results: The overexpression of CD248 in RCC was related to a poor prognosis, and a CD248-based prognostic signature could precisely stratify patients with RCC with different survival outcomes regardless of the training or testing cohort. The present signature could reflect the immunosuppressive landscape of RCC (i.e., increased infiltration of regulatory T cells and upregulated immune checkpoints), accompanied by deteriorated clinicopathologic indexes. The TMB and immunostimulatory molecules expression also increased with the risk score generated from the present signature. CD248 co-expressed gene sets were identified through the WGCNA algorithm, and several immunosuppressive Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched. The result of CD248-correlated miRNA further emphasized the importance of CD248 in RCC.

Conclusion: CD248 is a valuable biomarker to improve the diagnostic and therapeutic efficiency of RCC. The immunosuppressive effect of CD248 co-expressed genes may provide insight for the present study, and miRNA would help to reveal the mechanism of the expressive regulation of CD248.

• CD248
• immunotherapy
• molecular cancer signature
• prognosis
• renal cell carcinoma

Increasing evidence has elucidated the clinicopathological significance of individual TME component in predicting outcomes and immunotherapeutic efficacy in lung adenocarcinoma (LUAD). Therefore, we aimed to investigate whether comprehensive TME-based signatures could predict patient survival and therapeutic responses in LUAD, and to assess the associations among TME signatures, single nucleotide variations and clinicopathological characteristics.

In this study, we comprehensively estimated the TME infiltration patterns of 493 LUAD patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathological features of LUADs using two proposed computational algorithms. A TMEscore was then developed based on the TME signature genes, and its prognostic value was validated in different datasets. Bioinformatics analysis was used to evaluate the efficacy of the TMEscore in predicting responses to immunotherapy and chemotherapy.

Three TME subtypes were identified with no prognostic significance exhibited. Among them, naïve B cells accounted for the majority in TMEcluster1, while M2 TAMs and M0 TAMs took the largest proportion in TMEcluster2 and TMEcluster3, respectively. A total of 3395 DEGs among the three TME clusters were determined, among which 217 TME signature genes were identified. Interestingly, these signature genes were mainly involved in T cell activation, lymphocyte proliferation and mononuclear cell proliferation. With somatic variations and tumor mutation burden (TMB) of the LUAD samples characterized, a genomic landscape of the LUADs was thereby established to visualize the relationships among the TMEscore, mutation spectra and clinicopathological profiles. In addition, the TMEscore was identified as not only a prognosticator for long-term survival in different datasets, but also a predictive biomarker for the responses to immune checkpoint blockade (ICB) and chemotherapeutic agents. Furthermore, the TMEscore exhibited greater accuracy than other conventional biomarkers including TMB and microsatellite instability in predicting immunotherapeutic response (p < 0.001).

In conclusion, our present study depicted a comprehensive landscape of the TME signatures in LUADs. Meanwhile, the TMEscore was proved to be a promising predictor of patient survival and therapeutic responses in LUADs, which might be helpful to the future administration of personalized adjuvant therapy.

• lung adenocarcinoma
• signature
• survival
• therapeutic response
• tumor microenvironment

Immune and stromal component evaluation is necessary to establish accurate prognostic markers for the prediction of clinical outcomes in lung adenocarcinoma (LUAD). We aimed to develop a gene signature based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE)-stromal-immune score in LUAD.

The transcriptomic profiles of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA), and the immune and stromal scores were derived using the ESTIMATE algorithm. The prognostic signature genes were selected from the differentially expressed genes (DEGs) using the robust partial likelihood-based cox proportional hazards regression method. The negative log-likelihood and the Akaike Information Criterion (AIC) were used to identify the optimal gene signature. The validation was carried out in 2 independent datasets from the Gene Expression Omnibus (GSE68571 and GSE72094).

Patients with high ESTIMATE, stromal, and immune scores had better overall survivals (P=0.0035, P=0.066, and P=0.0077). The expression of thirty-seven genes was related to ESTIMATE-stromal-immune score. A risk stratification model was developed based on a gene signature containing CD74, JCHAIN, and PTGDS. The ESTIMATE-stromal-immune risk score was revealed to be a prognostic factor (P=0.009) after multivariate analysis. Four groups were classified based on this risk stratification model, yielding increasing survival outcomes (log-rank test, P=0.0051). This risk stratification model and other clinicopathological factors were combined to generate a nomogram. The calibration curves showed perfect agreement between the nomogram-predicted outcomes and those actually observed. Similar observations were made in 2 independent cohorts.

The gene signature based on the ESTIMATE-stromal-immune score could predict the prognosis of patients with LUAD.

• Lung adenocarcinoma (LUAD)
• immune
• prognosis
• stromal

• bioinformatics
• hub genes
• lung adenocarcinoma
• microRNAs
• prognostic marker

The immunological tumour microenvironment (TME) has occupied a very important position in the beginning and progression of non-small cell lung cancer (NSCLC). Prognosis of lung adenocarcinoma (LUAD) remains poor for the local progression and widely metastases at the time of clinical diagnosis. Our objective is to identify a potential signature model to improve prognosis of LUAD.

With the aim to identify a novel immune prognostic signature associated with overall survival (OS), we analysed LUADs extracted from The Cancer Genome Atlas (TCGA). Immune scores and stromal scores of TCGA-LUAD were downloaded from Estimation of STromal and Immune cells in MAlignant Tumour tissues Expression using data (ESTIMATE). LASSO COX regression was applied to build the prediction model. Then, the prognostic gene signature was validated in the GSE68465 dataset.

The data from TCGA datasets showed patients in stage I and stage II had higher stromal scores than patients in stage IV (P < 0.05), and for immune score patients in stage I were higher than patients in stage III and stage IV (P < 0.05). The improved overall survivals were observed in high stromal score and immune score groups. Patients in the high-risk group exhibited the inferior OS (P = 2.501e − 05). By validating the 397 LUAD patients from GSE68465, we observed a better OS in the low-risk group compared to the high-risk group, which is consistent with the results from the TCGA cohort. Nomogram results showed that practical and predicted survival coincided very well, especially for 3-year survival.

We obtained an 11 immune score related gene signature model as an independent element to effectively classify LUADs into different risk groups, which might provide a support for precision treatments. Moreover, immune score may play a potential valuable sole for estimating OS in LUADs.

• Gene signature
• LUAD
• Prognosis
• TME