Development and verification of immune-related long non-coding RNA prognostic signature in bladder cancer

Abstract Background: Bladder cancer is the second most common malignant tumor in urogenital system. The research aimed to investigate the prognostic role of immune-related long non-coding RNA (lncRNA) in bladder cancer. Methods: We extracted 411 bladder cancer samples from The Cancer Genome Atlas database. Single-sample gene set enrichment analysis was employed to assess the immune cell infiltration of these samples. We recognized differentially expressed lncRNAs between tumors and paracancerous tissues, and differentially expressed lncRNAs between the high and low immune cell infiltration groups. Venn diagram analysis detected differentially expressed lncRNAs that intersected the above groups. LncRNAs with prognostic significance were identified by regression analysis and survival analysis. Multivariate Cox analysis was used to establish the risk score model. The nomogram was established and evaluated by receiver operating characteristic (ROC) curve analysis, concordance index (C-index) analysis, calibration chart, and decision curve analysis (DCA). Additionally, we performed gene set enrichment analysis to explore the potential functions of the screened lncRNAs in tumor pathogenesis.Results: Three hundred and twenty differentially expressed lncRNAs were recognized. We randomly divided patients into the training data set and the testing data set at a 2: 1 ratio. In the training data set, 9 immune-related lncRNAs with prognostic significance were identified. The risk score model was constructed to classify patients as high- and low-risk cohorts. Patients in the low-risk cohort had better survival outcomes than those in the high-risk cohort. The nomogram was established based on the indicators including age, gender, TNM stage, and risk score. The model’s predictive performance was confirmed by ROC curve analysis, C-index analysis, calibration chart, and DCA. The testing data set also achieved similar results. Bioinformatics analysis suggested that the 9-lncRNA signature was involved in modulation of various immune responses, antigen processing and presentation, and T cell receptor signaling pathway.Conclusions: The immune-related lncRNAs have the potential to predict the prognosis of bladder cancer and may play a key role in bladder cancer biology.Trial registration: It was a retrospective study and the gene expression data were obtained from the TCGA database. Trial registration was not needed.

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Identification of a Novel Glycolysis-Related LncRNA Signature for Predicting Overall Survival in Patients With Bladder Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.720421 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhenming Zheng ◽

Cong Lai ◽

Wenshuang Li ◽

Caixia Zhang ◽

Kaiqun Ma ◽

...

Keyword(s):

Bladder Cancer ◽

Roc Curve ◽

Prognostic Model ◽

Prognostic Significance ◽

Enrichment Analysis ◽

Curve Analysis ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Roc Curve Analysis ◽

Gene Set Enrichment

BackgroundBoth lncRNAs and glycolysis are considered to be key influencing factors in the progression of bladder cancer (BCa). Studies have shown that glycolysis-related lncRNAs are an important factor affecting the overall survival and prognosis of patients with bladder cancer. In this study, a prognostic model of BCa patients was constructed based on glycolysis-related lncRNAs to provide a point of reference for clinical diagnosis and treatment decisions.MethodsThe transcriptome, clinical data, and glycolysis-related pathway gene sets of BCa patients were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Set Enrichment Analysis (GSEA) official website. Next, differentially expressed glycolysis-related lncRNAs were screened out, glycolysis-related lncRNAs with prognostic significance were identified through LASSO regression analysis, and a risk scoring model was constructed through multivariate Cox regression analysis. Then, based on the median of the risk scores, all BCa patients were divided into either a high-risk or low-risk group. Kaplan-Meier (KM) survival analysis and the receiver operating characteristic (ROC) curve were used to evaluate the predictive power of the model. A nomogram prognostic model was then constructed based on clinical indicators and risk scores. A calibration chart, clinical decision curve, and ROC curve analysis were used to evaluate the predictive performance of the model, and the risk score of the prognostic model was verified using the TCGA data set. Finally, Gene Set Enrichment Analysis (GSEA) was performed on glycolysis-related lncRNAs.ResultsA total of 59 differentially expressed glycolysis-related lncRNAs were obtained from 411 bladder tumor tissues and 19 pericarcinomatous tissues, and 9 of those glycolysis-related lncRNAs (AC099850.3, AL589843.1, MAFG-DT, AC011503.2, NR2F1-AS1, AC078778.1, ZNF667-AS1, MNX1-AS1, and AC105942.1) were found to have prognostic significance. A signature was then constructed for predicting survival in BCa based on those 9 glycolysis-related lncRNAs. ROC curve analysis and a nomogram verified the accuracy of the signature.ConclusionThrough this study, a novel prognostic prediction model for BCa was established based on 9 glycolysis-related lncRNAs that could effectively distinguish high-risk and low-risk BCa patients, and also provide a new point of reference for clinicians to make individualized treatment and review plans for patients with different levels of risk.

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Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.780601 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ya Jun Liu ◽

Alphonse Houssou Hounye ◽

Zheng Wang ◽

Xiaowei Liu ◽

Jun Yi ◽

...

Keyword(s):

Risk Score ◽

Immune Cell ◽

Noncoding Rnas ◽

Enrichment Analysis ◽

Long Noncoding Rnas ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Roc Curve Analysis ◽

Good Reliability ◽

Normal Tissues

Cholangiocarcinoma (CCA) is featured by common occurrence and poor prognosis. Autophagy is a biological process that has been extensively involved in the progression of tumors. Long noncoding RNAs (lncRNAs) have been discovered to be critical in diagnosing and predicting various tumors. It may be valuable to elaborate autophagy-related lncRNAs (ARlncRNAs) in CCA, and indeed, there are still few studies concerning the role of ARlncRNAs in CCA. Here, a prognostic ARlncRNA signature was constructed to predict the survival outcome of CCA patients. Through identification, three differentially expressed ARlncRNAs (DEARlncRNAs), including CHRM3.AS2, MIR205HG, and LINC00661, were screened and were considered predictive signatures. Furthermore, the overall survival (OS) of patients with high-risk scores was significantly lower than that of patients with low scores. Interestingly, the risk score was an independent factor for the OS of patients with CCA. Moreover, receiver operating characteristic (ROC) curve analysis showed that the screened and constructed prognosis signature for 1 year (AUC = 0.884), 3 years (AUC =0.759), and 5 years (AUC = 0.788) presented a high score of accuracy in predicting OS of CCA patients. Gene set enrichment analysis (GSEA) revealed that the three DEARlncRNAs were significantly enriched in CCA-related signaling pathways, including “pathways of basal cell carcinoma”, “glycerolipid metabolism”, etc. Quantitative real-time PCR (qRT-PCR) showed that expressions of CHRM3.AS2, MIR205HG, and LINC00661 were higher in CCA tissues than those in normal tissues, similar to the trends detected in the CCA dataset. Furthermore, Pearson’s analysis reported an intimate correlation of the risk score with immune cell infiltration, indicating a predictive value of the signature for the efficacy of immunotherapy. In addition, the screened lncRNAs were found to have the ability to modulate the expression of mRNAs by interacting with miRNAs based on the established lncRNA-miRNA-mRNA network. In conclusion, our study develops a novel nomogram with good reliability and accuracy to predict the OS of CCA patients, providing a significant guiding value for developing tailored therapy for CCA patients.

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Construction and Validation of a Macrophage-Associated Risk Model for Predicting the Prognosis of Osteosarcoma

Journal of Oncology ◽

10.1155/2021/9967954 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Kang-Wen Xiao ◽

Zhi-Bo Liu ◽

Zi-Hang Zeng ◽

Fei-Fei Yan ◽

Ling-Fei Xiao ◽

...

Keyword(s):

Gene Expression ◽

Survival Analysis ◽

High Risk ◽

Risk Score ◽

Risk Model ◽

Risk Group ◽

Curve Analysis ◽

Gene Set Enrichment Analysis ◽

Data Set ◽

Cox Regression Analysis

Background. Osteosarcoma is one of the most common bone tumors among children. Tumor-associated macrophages have been found to interact with tumor cells, secreting a variety of cytokines about tumor growth, metastasis, and prognosis. This study aimed to identify macrophage-associated genes (MAGs) signatures to predict the prognosis of osteosarcoma. Methods. Totally 384 MAGs were collected from GSEA software C7: immunologic signature gene sets. Differential gene expression (DGE) analysis was performed between normal bone samples and osteosarcoma samples in GSE99671. Kaplan–Meier survival analysis was performed to identify prognostic MAGs in TARGET-OS. Decision curve analysis (DCA), nomogram, receiver operating characteristic (ROC), and survival curve analysis were further used to assess our risk model. All genes from TARGET-OS were used for gene set enrichment analysis (GSEA). Immune infiltration of osteosarcoma sample was calculated using CIBERSORT and ESTIMATE packages. The independent test data set GSE21257 from gene expression omnibus (GEO) was used to validate our risk model. Results. 5 MAGs (MAP3K5, PML, WDR1, BAMBI, and GNPDA2) were screened based on protein-protein interaction (PPI), DGE, and survival analysis. A novel macrophage-associated risk model was constructed to predict a risk score based on multivariate Cox regression analysis. The high-risk group showed a worse prognosis of osteosarcoma ( p < 0.001) while the low-risk group had higher immune and stromal scores. The risk score was identified as an independent prognostic factor for osteosarcoma. MAGs model for diagnosis of osteosarcoma had a better net clinical benefit based on DCA. The nomogram and ROC curve also effectively predicted the prognosis of osteosarcoma. Besides, the validation result was consistent with the result of TARGET-OS. Conclusions. A novel macrophage-associated risk score to differentiate low- and high-risk groups of osteosarcoma was constructed based on integrative bioinformatics analysis. Macrophages might affect the prognosis of osteosarcoma through macrophage differentiation pathways and bring novel sights for the progression and prognosis of osteosarcoma.

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Two Gene Set Variation Index as Biomarker of Bacterial and Fungal Sepsis

BioMed Research International ◽

10.1155/2020/8182358 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Xiaowen Zheng ◽

Yifeng Luo ◽

Qian Li ◽

Jihua Feng ◽

Chunling Zhao ◽

...

Keyword(s):

Diagnostic Value ◽

Curve Analysis ◽

Differentially Expressed ◽

Specific Gene ◽

Independent Data ◽

Bacterial Sepsis ◽

Roc Curve Analysis ◽

Data Set ◽

Gene Set ◽

Fungal Sepsis

Background. The incidence of sepsis has been increasing in recent years. The molecular mechanism of different pathogenic sepsis remains elusive, and biomarkers of sepsis against different pathogens are still lacking. Methods. The microarray data of bacterial sepsis, fungal sepsis, and mock-treated samples were applied to perform differentially expressed gene (DEG) analysis to identify a bacterial sepsis-specific gene set and a fungal sepsis-specific gene set. Functional enrichment analysis was used to explore the body’s response to bacterial sepsis and fungal sepsis. Gene set variation analysis (GSVA) was used to score individual samples against the two pathogen-specific gene sets, and each sample gets a GSVA index. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of sepsis. An independent data set was used to validate the bacterial sepsis-specific GSVA index. Results. The genes differentially expressed only in bacterial sepsis and the genes differentially expressed only in fungal sepsis were significantly involved in different biological processes (BPs) and pathways. This indicated that the body’s responses to fungal sepsis and bacterial sepsis are varied. Twenty-two genes were identified as bacterial sepsis-specific genes and upregulated in bacterial sepsis, and 23 genes were identified as fungal sepsis-specific genes and upregulated in fungal sepsis. ROC curve analysis showed that both of the two pathogen sepsis-specific GSVA indexes may be a reliable biomarker for corresponding pathogen-induced sepsis (AUC=1.000), while the mRNA of CALCA (also known as PCT) have a poor diagnostic value with AUC=0.512 in bacterial sepsis and AUC=0.705 in fungi sepsis. In addition, the AUC of the bacterial sepsis-specific GSVA index in the independent data set was 0.762. Conclusion. We proposed a bacterial sepsis-specific gene set and a fungal sepsis-specific gene set; the bacterial sepsis GSVA index may be a reliable biomarker for bacterial sepsis.

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Prognostic and Predictive Values of the Autophagy Signature in Colon Cancer

10.21203/rs.3.rs-63586/v1 ◽

2020 ◽

Author(s):

Qian Xu ◽

Yugang Guo ◽

Jintao Fang ◽

Jiawei Zhou ◽

Guohui Ma ◽

...

Keyword(s):

Colon Cancer ◽

Risk Score ◽

Clinical Decision Making ◽

Cox Regression ◽

External Validation ◽

Gene Set Enrichment Analysis ◽

Training Data ◽

Regression Analyses ◽

Data Set ◽

Predictive Values

Abstract Background: In the clinical decision-making among patients with colon cancer (COAD), making an accurate prognosis of the patients plays a central role. The effects of autophagy on the clinical outcomes of cancer, including COAD, have been widely reported in numerous studies. Here, weaim to build a novel autophagy-associated, risk-stratification scoring system to predict the overall survival(OS)of patients with COAD. Methods: In this study, the candidate autophagy-related prognostic genes correlated with the survival of COAD patients from The Cancer Genome Atlas (TCGA) public RNA microarray and clinical data sets were selected as training data set. A cohort of 67 patients from TCGA and a cohort of 124 patients from GEO were used for the external validation. The autophagy-related mRNAs(ARGs) were analyzed by multivariate Cox regression analyses. Spearman correlation analysis were used to construct autophagy-related mRNAs and lncRNAs coexpression network. Results: 6 autophagy-related mRNAs and 14 lncRNAs with prognostic value were extracted for constructing two novel autophagy-related RNAs signatures, respectively. Univariate and multivariate Cox regression analyses were then demonstrated that the two signature could act as independent prognostic predictor for OS. Additionally, a prognostic nomogram incorporating the clinicopathological characteristics(patient’s age, tumor stage) and autophagy-related lncRNA risk score was constructed to predict the OS, which was used in the training and validation sets (5-year C-index: 0.826 and 0.895, respectively), demonstrating better discrimination ability and clinical net benefit than the risk score model. Further gene set enrichment analysis revealed that autophagy-associated lncRNAs were significantly enriched in cancer-related pathways.Conclusions: The identified autophagy-related mRNAs and lncRNAs signature had important clinical implications in prognosis prediction and the user-friendly nomogram may offer an extra insight for individualized therapy of COAD.

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Mortality risk prediction in lupus patients complicated with invasive infection in the emergency department: LUPHAS score

Therapeutic Advances in Musculoskeletal Disease ◽

10.1177/1759720x19885559 ◽

2019 ◽

Vol 11 ◽

pp. 1759720X1988555 ◽

Cited By ~ 1

Author(s):

Wanlong Wu ◽

Jun Ma ◽

Yuhong Zhou ◽

Chao Tang ◽

Feng Zhao ◽

...

Keyword(s):

Emergency Department ◽

Prediction Model ◽

Risk Score ◽

Roc Curve ◽

Curve Analysis ◽

Multivariable Logistic Regression Analysis ◽

Invasive Infection ◽

Roc Curve Analysis ◽

All Cause Mortality ◽

Qsofa Score

Background: Infection remains a major cause of morbidity and mortality in patients with systemic lupus erythematosus (SLE). This study aimed to establish a clinical prediction model for the 3-month all-cause mortality of invasive infection events in patients with SLE in the emergency department. Methods: SLE patients complicated with invasive infection admitted into the emergency department were included in this study. Patient’s demographic, clinical, and laboratory characteristics on admission were retrospectively collected as baseline data and compared between the deceased and the survivors. Independent predictors were identified by multivariable logistic regression analysis. A prediction model for all-cause mortality was established and evaluated by receiver operating characteristic (ROC) curve analysis. Results: A total of 130 eligible patients were collected with a cumulative 38.5% 3-month mortality. Lymphocyte count <800/ul, urea >7.6mmol/l, maximum prednisone dose in the past ⩾60 mg/d, quick Sequential Organ Failure Assessment (qSOFA) score, and age at baseline were independent predictors for all-cause mortality (LUPHAS). In contrast, a history of hydroxychloroquine use was protective. In a combined, odds ratio-weighted LUPHAS scoring system (score 3–22), patients were categorized to three groups: low-risk (score 3–9), medium-risk (score 10–15), and high-risk (score 16–22), with mortalities of 4.9% (2/41), 45.9% (28/61), and 78.3% (18/23) respectively. ROC curve analysis indicated that a LUPHAS score could effectively predict all-cause mortality [area under the curve (AUC) = 0.86, CI 95% 0.79–0.92]. In addition, LUPHAS score performed better than the qSOFA score alone (AUC = 0.69, CI 95% 0.59–0.78), or CURB-65 score (AUC = 0.69, CI 95% 0.59–0.80) in the subgroup of lung infections ( n = 108). Conclusions: Based on a large emergency cohort of lupus patients complicated with invasive infection, the LUPHAS score was established to predict the short-term all-cause mortality, which could be a promising applicable tool for risk stratification in clinical practice.

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Comprehensive Analysis of CPA4 as a Poor Prognostic Biomarker Correlated with Immune Cells Infiltration in Bladder Cancer

Biology ◽

10.3390/biology10111143 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1143

Author(s):

Chengcheng Wei ◽

Yuancheng Zhou ◽

Qi Xiong ◽

Ming Xiong ◽

Yaxin Hou ◽

...

Keyword(s):

Bladder Cancer ◽

T Cell ◽

Immune Cells ◽

Prognostic Significance ◽

Gene Set Enrichment Analysis ◽

Marker Genes ◽

Gene Marker ◽

Normal Tissues ◽

Kaplan Meier ◽

Diagnostic Potential

Carboxypeptidase A4 (CPA4) has shown the potential to be a biomarker in the early diagnosis of certain cancers. However, no previous research has linked CPA4 to therapeutic or prognostic significance in bladder cancer. Using data from The Cancer Genome Atlas (TCGA) database, we set out to determine the full extent of the link between CPA4 and BLCA. We further analyzed the interacting proteins of CPA4 and infiltrated immune cells via the TIMER2, STRING, and GEPIA2 databases. The expression of CPA4 in tumor and normal tissues was compared using the TCGA + GETx database. The connection between CPA4 expression and clinicopathologic characteristics and overall survival (OS) was investigated using multivariate methods and Kaplan–Meier survival curves. The potential functions and pathways were investigated via gene set enrichment analysis. Furthermore, we analyze the associations between CPA4 expression and infiltrated immune cells with their respective gene marker sets using the ssGSEA, TIMER2, and GEPIA2 databases. Compared with matching normal tissues, human CPA4 was found to be substantially expressed. We confirmed that the overexpression of CPA4 is linked with shorter OS, DSF(Disease-specific survival), PFI(Progression-free interval), and increased diagnostic potential using Kaplan–Meier and ROC analysis. The expression of CPA4 is related to T-bet, IL12RB2, CTLA4, and LAG3, among which T-bet and IL12RB2 are Th1 marker genes while CTLA4 and LAG3 are related to T cell exhaustion, which may be used to guide the application of checkpoint blockade and the adoption of T cell transfer therapy.

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Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

10.21203/rs.3.rs-49217/v1 ◽

2020 ◽

Author(s):

Jianfeng Zheng ◽

Jinyi Tong ◽

Benben Cao ◽

Xia Zhang ◽

Zheng Niu

Keyword(s):

Cervical Cancer ◽

High Risk ◽

Risk Score ◽

Immune Cell ◽

Risk Group ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Training Set ◽

Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

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Identification of Survival and Therapeutic Response-Related Ferroptosis Regulators in Bladder Cancer through Data Mining and Experimental Validation

Cancers ◽

10.3390/cancers13236069 ◽

2021 ◽

Vol 13 (23) ◽

pp. 6069

Author(s):

Pu Zhang ◽

Zijian Liu ◽

Decai Wang ◽

Yunxue Li ◽

Yuan Zhang ◽

...

Keyword(s):

Bladder Cancer ◽

Risk Score ◽

Immune Cell ◽

Predictive Accuracy ◽

Chemotherapy Sensitivity ◽

Real Time Quantitative Pcr ◽

Risk Of Death ◽

Prediction Function ◽

Mutation Burden ◽

Selection Operator

Ferroptosis has been reported to regulate tumorigenesis, metastasis, drug resistance and the immune response. However, the potential roles of ferroptosis regulators in the advancement of bladder cancer remain to be explored. We systematically evaluated the multidimensional alteration landscape of ferroptosis regulators in bladder cancer and checked if their expression correlated with the ferroptosis index. We used least absolute shrinkage and selection operator regression to form a signature consisting of seven ferroptosis regulator. We confirmed the signature’s prognostic and predictive accuracy with five independent datasets. A nomogram was built to predict the overall survival and risk of death of patients. The relative expression of the genes involved in the signature was also clarified by real-time quantitative PCR. We found the risk score was related to tumor progression and antitumor immunity-related pathways. Moreover, there existed negative association between the relative antitumor immune cell infiltration level and the risk score, and higher tumor mutation burden was found in the group of lower risk score. We used The Tumor Immune Dysfunction and Exclusion database and IMvigor210 cohort having immunotherapy efficacy results to confirm the prediction function of the risk score. Furthermore, the ferroptosis regulator signature could also reflect the chemotherapy sensitivity of bladder cancer.

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Establishment of a Macrophage Phenotypic Switch Related Prognostic Signature in Patients With Pancreatic Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.619517 ◽

2021 ◽

Vol 11 ◽

Author(s):

Mu-xing Li ◽

Hang-yan Wang ◽

Chun-hui Yuan ◽

Zhao-lai Ma ◽

Bin Jiang ◽

...

Keyword(s):

Pancreatic Cancer ◽

Regression Analysis ◽

Signaling Pathway ◽

Risk Score ◽

Prognostic Value ◽

Risk Group ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Prognostic Signature ◽

Phenotypic Switch

IntroductionMacrophage phenotype switch plays a vital role in the progression of malignancies. We aimed to build a prognostic signature by exploring the expression pattern of macrophage phenotypic switch related genes (MRGs) in the Cancer Genome Atlas (TCGA)—pancreatic adenocarcinoma (PAAD), Genotype-Tissue Expression (GTEx)-Pancreas, and Gene Expression Omnibus (GEO) databases.MethodsWe identified the differentially expressed genes between the PAAD and normal tissues. We used single factor Cox proportional risk regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) analysis, and multivariate Cox proportional hazard regression analysis to establish the prognosis risk score by the MRGs. The relationships between the risk score and immune landscape, “key driver” mutations and clinicopathological factors were also analyzed. Gene-set enrichment analysis (GSEA) analysis was also performed.ResultsWe detected 198 differentially expressed MRGs. The risk score was constructed based on 9 genes (KIF23, BIN1, LAPTM4A, ERAP2, ATP8B2, FAM118A, RGS16, ELMO1, RAPGEFL1). The median overall survival time of patients in the low-risk group was significantly longer than that of patients in the high-risk group (P < 0.001). The prognostic value of the risk score was validated in GSE62452 dataset. The prognostic performance of nomogram based on risk score was superior to that of TNM stage. And GSEA analysis also showed that the risk score was closely related with P53 signaling pathway, pancreatic cancer and T cell receptor signaling pathway. qRT-PCR assay showed that the expressions of the 9 MRGs in PDAC cell lines were higher than those in human pancreatic ductal epithelium cell line.ConclusionsThe nine gene risk score could be used as an independent prognostic index for PAAD patients. Further studies validating the prognostic value of the risk score are warranted.

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