A three-long non-coding RNA-expression-based risk score system can better predict both overall and recurrence-free survival in patients with small hepatocellular carcinoma

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

BACKGROUND

Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence.

METHODS

Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted.

RESULTS

A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC.

CONCLUSION

Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

Clinical and Prognostic Value of PPIA, SQSTM1, and CCL20 in Hepatocellular Carcinoma Patients by Single-Cell Transcriptome Analysis

Hepatocellular carcinoma (HCC) is the most malignant and poor-prognosis subtype of primary liver cancer. The scRNA-seq approach provides unique insight into tumor cell behavior at the single-cell level. Cytokine signaling in the immune system plays an important role in tumorigenesis and has both pro-tumorigenic and anti-tumorigenic functions. A biomarker of cytokine signaling in immune-related genes (CSIRG) is urgently required to assess HCC patient diagnosis and treatment. By analyzing the expression profiles of HCC single cells, TCGA, and ICGC data, we discovered that three important CSIRG (PPIA, SQSTM1, and CCL20) were linked to the overall survival of HCC patients. Cancer status and three hub CSIRG were taken into account while creating a risk nomogram. The nomogram had a high level of predictability and accuracy. Based on the CSIRG risk score, a distinct pattern of somatic tumor mutational burden (TMB) was detected between the two groups. The enrichment of the pyrimidine metabolism pathway, purine metabolism pathway, and lysosome pathway in HCC was linked to the CSIRG high-risk scores. Overall, scRNA-seq and bulk RNA-seq were used to create a strong CSIRG signature for HCC diagnosis.

LINC00667 regulates MPP + ‐induced neuronal injury in Parkinson’s disease

Objective

Parkinson’s disease (PD), also known as paralysis tremor, is a chronic disease of the central nervous system. It has been reported that hepatocyte nuclear factor 4 alpha (HNF4A) is upregulated in PD, but its specific function has not been well explored.

Methods

We established an in vitro PD model in SH‐SY5Y cells stimulated with 1‐methyl‐4‐phenylpyridinium (MPP⁺). Meanwhile, the effect of HNF4A on MPP⁺‐treated SH‐SY5Y cell behavior was monitored by functional assays. Mechanism assays were conducted to verify the relationship among LINC00667/miR‐34c‐5p/HNF4A. Rescue experiments validated the regulatory mechanism in PD model.

Results

The results revealed that depletion of HNF4A suppressed cell cytotoxicity and apoptosis caused by MPP⁺. Knockdown of HNF4A recovered MPP⁺‐stimulated oxidative stress and neuroinflammation. Mechanically, HNF4A was targeted and inhibited by miR‐34c‐5p. Furthermore, we found that LINC00667 positively modulated HNF4A expression via sequestering miR‐34c‐5p in MPP⁺‐stimulated SH‐SY5Y cells. Interestingly, the data indicated that HNF4A could transcriptionally activate LINC00667 expression. Rescue experiments presented that miR‐34c‐5p interference or HNF4A overexpression could mitigate the effects of LINC00667 knockdown on cell viability, cytotoxicity, cell apoptosis, oxidative stress, and neuroinflammation in MPP⁺‐treated SH‐SY5Y cells.

Conclusion

Our study first proved LINC00667, miR‐34c‐5p, and HNF4A constructed a positive feedback loop in MPP⁺‐treated SH‐SY5Y cells, enriching our understanding of PD.

Construction of an HCC recurrence model based on the investigation of immune-related lncRNAs and related mechanisms

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and play fundamental roles in immune regulation. Growing evidence suggests that immune-related genes and lncRNAs can serve as markers to predict the prognosis of patients with cancers, including hepatocellular carcinoma (HCC). This study aimed to contract an immune-related lncRNA (IR-lncRNA) signature for prospective assessment to predict early recurrence of HCC. A total of 319 HCC samples under radical resection were randomly divided into a training cohort (161 samples) and a testing cohort (158 samples). In the training dataset, univariate, lasso, and multivariate Cox regression analyses identified a 9-IR-lncRNA signature closely related to disease-free survival. Kaplan-Meier analysis, principal component analysis, gene set enrichment analysis, and nomogram were used to evaluate the risk model. The results were further confirmed in the testing cohort. Furthermore, we constructed a competitive endogenous RNA regulatory network. The results of the present study indicated that this 9-IR-lncRNA signature has important clinical implications for improving predictive outcomes and guiding individualized treatment in HCC patients. These IR-lncRNAs and regulated genes may be potential biomarkers associated with the prognosis of HCC.

LncRNAs Associated with Chemoradiotherapy Response and Prognosis in Locally Advanced Rectal Cancer

BACKGROUND

There are only limited studies on the long non-coding RNAs (lncRNAs) associated with neoadjuvant chemoradiotherapy (NCRT) response and prognosis of locally advanced rectal cancer (LARC) patients. This study identified lncRNAs associated with NCRT response and prognosis in CRC patients and explored their potential predictive mechanisms.

METHODS

The study subjected the LncRNA expression profiles from our previous gene chip data to LASSO and identified a four-lncRNA signature that predicted NCRT response and prognosis. A Cox regression model was subsequently performed to identify the prognostic risk factors. The function of LINC00909, the lncRNA with the most powerful predictive ability, was finally identified in vivo and in vitro using CRC cell lines.

RESULTS

A comparison of the relative lncRNA expression of NCRT-responsive and non-responsive patients revealed four hub lncRNAs: DBET, LINC00909, FLJ33534, and HSD52 with AUC = 0.68, 0.73, 0.73, and 0.70, respectively (all p < 0.05). COX regression analysis further demonstrated that DBET, LINC00909 and FLJ33534 were associated with the DFS in CRC patients. The expression of the four lncRNAs was also significant in LARC patients who had not undergone NCRT (all p < 0.05). A risk score model was subsequently constructed based on the results of the multivariate COX analysis and used to predict NCRT response and prognosis in the CRC and LARC patients. The expression and prognosis of DBET, LINC00909 and FLJ33534 in the CRC tissues were further validated in the R2 platform and Oncomine database. Notably, overexpression of the LINC00909 increased the cell line resistance to the 5-FU and radiotherapy in vivo and in vitro.

CONCLUSION

DBET, LINC00909, and FLJ33534 are potential novel biomarkers for predicting NCRT response and prognosis in CRC patients. In particular, LINC00909 is an effective oncogene in CRC that could be used as a novel therapeutic target to enhance NCRT response.

Screening of Significant Biomarkers Related to Prognosis of Cervical Cancer and Functional Study Based on lncRNA-associated ceRNA Regulatory Network

BACKGROUND

Cervical cancer (CESC), which threatens the health of women, has a very high recurrence rate.

PURPOSES

This study aimed to identify the signature long non-coding RNAs (lncRNAs) associated with the prognosis of CESC and predict the prognostic survival rate with the clinical risk factors.

METHODS

The CESC gene expression profiling data were downloaded from TCGA database and NCBI Gene Expression Omnibus. Afterwards, the differentially expressed RNAs (DERs) were screened using limma package of R software. R package "survival" was then used to screen the signature lncRNAs associated with independently recurrence prognosis, and a nomogram recurrence rate model based on these signature lncRNAs was constructed to predict the 3-year and 5-year survival probability of CESC. Finally, a competing endogenous RNAs (ceRNA) regulatory network was proposed to study the functions of these genes.

RESULTS

We obtained 305 DERs significantly associated with prognosis. Afterwards, a risk score (RS) prediction model was established using the screened 5 signature lncRNAs associated with independently recurrence prognosis (DLEU1, LINC01119, RBPMS-AS1, RAD21-AS1 and LINC00323). Subsequently, a nomogram recurrence rate model, proposed with Pathologic N and RS model status, was found to have a good prediction ability for CESC. In ceRNA regulatory network, LINC00323 and DLEU1 were hub nodes which targeted more miRNAs and mRNAs. After that, 15 GO terms and 3 KEGG pathways were associated with recurrence prognosis and showed that the targeted genes PTK2, NRP1, PRKAA1 and HMGCS1 might influence the prognosis of CESC.

CONCLUSION

The signature lncRNAs can help improve our understanding of the development and recurrence of CESC and the nomogram recurrence rate model can be applied to predict the survival rate of CESC patients in clinical practice.

Identification of a nomogram based on an 8-lncRNA signature as a novel diagnostic biomarker for childhood acute lymphoblastic leukemia

Childhood acute lymphoblastic leukemia (cALL) still represents a major cause of disease-related death in children. This study aimed to explore the prognostic value of long non-coding RNAs (lncRNAs) in cALL. We downloaded lncRNA expression profiles from the TARGET and GEO databases. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to identify lncRNA-based signatures. We identified an eight-lncRNA signature (LINC00630, HDAC2-AS2, LINC01278, AL356599.1, AC114490.1, AL132639.3, FUT8.AS1, and TTC28.AS1), which separated the patients into two groups with significantly different overall survival rates. A nomogram based on the signature, BCR ABL1 status and white blood cell count at diagnosis was developed and showed good accuracy for predicting the 3-, 5- and 7-year survival probability of cALL patients. The C-index values of the nomogram in the training and internal validation set reached 0.8 (95% CI, 0.757 to 0.843) and 0.806 (95% CI, 0.728 to 0.884), respectively. The nomogram proposed in this study objectively and accurately predicted the prognosis of cALL. In vitro experiments suggested that LINC01278 promoted the proliferation of leukemic cells and inhibited leukemic cell apoptosis by targeting the inhibition of miR-500b-3p in cALL, and LINC01278 may be a biological target for the treatment of cALL in the future.

Prognostic Model for the Risk Stratification of Early and Late Recurrence in Hepatitis B Virus-Related Small Hepatocellular Carcinoma Patients with Global Histone Modifications

Background and Aim

To assess the profile of global histone modifications in small hepatocellular carcinoma (small HCC) and identify its prognostic value in predicting recurrence.

Methods

The expression profiles of global histone modifications, including H2AK5AC, H2BK20AC, H3K4me2, H3K9AC, H3K18AC, H4K12AC, and H4R3me2, were evaluated with immunohistochemistry in 335 HBV related small HCC patients. Two histone signature classifiers were then developed using least absolute shrinkage and selection operator Cox regression. A nomogram was built using the classifier and independent risk factors. The performances of the classifier and nomogram were assessed by receiver operating characteristic curves.

Results

Histone modifications were more pronounced in tumor tissues than in adjacent liver tissues. In tumor tissues, the risk score built based on the seven-histone signature exhibited satisfactory prediction efficiency, with an AUC = 0.71 (0.63–0.79) for 2-year survival in the training cohort. Patients with a high risk score had shorter recurrence-free survival than those with a low risk score (HR: 1.96, 95% CI: 1.24–3.08, p = 0.004; HR: 1.95, 95% CI: 1.12–3.42, p = 0.019; and HR: 1.97, 95% CI: 1.39–2.80, p < 0.001 for the training, validation and total cohorts, respectively). Furthermore, the statistical nomogram built using the histone classifier for early recurrence had a C-index = 0.68. In non-neoplastic liver tissues, the hepatic signature based on H3K4me2 and H4R3me2 was related to late recurrence (HR: 2.00, 95% CI: 1.15–3.48, p = 0.01).

Conclusion

Global histone modifications in tumor and adjacent liver tissues are novel predictors of early and late recurrence, respectively, in HBV-related small HCC patients.

Identification of LncRNAs Associated With FOLFOX Chemoresistance in mCRC and Construction of a Predictive Model

Oxaliplatin, fluorouracil plus leucovorin (FOLFOX) regimen is the first-line chemotherapy of patients with metastatic colorectal cancer (mCRC). However, studies are limited regarding long non-coding RNAs (lncRNAs) associated with FOLFOX chemotherapy response and prognosis. This study aimed to identify lncRNAs associated with FOLFOX chemotherapy response and prognosis in mCRC patients and to construct a predictive model. We analyzed lncRNA expression in 11 mCRC patients treated with FOLFOX chemotherapy before surgery (four sensitive, seven resistant) by Gene Array Chip. The top eight lncRNAs (AC007193.8, CTD-2008N3.1, FLJ36777, RP11-509J21.4, RP3-508I15.20, LOC100130950, RP5-1042K10.13, and LINC00476) for chemotherapy response were identified according to weighted correlation network analysis (WGCNA). A competitive endogenous RNA (ceRNA) network was then constructed. The crucial functions of the eight lncRNAs enriched in chemotherapy resistance were mitogen-activated protein kinase (MAPK) and proteoglycans signaling pathway. Receiver operating characteristic (ROC) analysis demonstrated that the eight lncRNAs were potent predictors for chemotherapy resistance of mCRC patients. To further identify a signature model lncRNA chemotherapy response and prognosis, the validation set consisted of 196 CRC patients from our center was used to validate lncRNAs expression and prognosis by quantitative PCR (qPCR). The expression of the eight lncRNAs expression between CRC cancerous and adjacent non-cancerous tissues was also verified in the validation data set to determine the prognostic value. A generalized linear model was established to predict the probability of chemotherapy resistance and survival. Our findings showed that the eight-lncRNA signature may be a novel biomarker for the prediction of FOLFOX chemotherapy response and prognosis of mCRC patients.

LINC00667 Sponges miR-4319 to Promote the Development of Nasopharyngeal Carcinoma by Increasing FOXQ1 Expression

Accumulating evidence has indicated that lncRNAs regulate various biological and pathological processes in diverse malignant tumors. The roles of LINC00667 in cancer development have been explored in glioma, hepatocellular carcinoma and non-small cell lung cancer, but not in nasopharyngeal carcinoma (NPC). In the present study, we characterize the role and molecular mechanism of LINC00667 in NPC progression. It was found that LINC00667 was overexpressed in NPC cells compared to normal cells. Silencing LINC00667 suppressed the proliferation, migration, invasion and epithelial mesenchymal transition (EMT) in NPC cells. In addition, bioinformatics analysis revealed that LINC00667 acted as a ceRNA to absorb miR-4319. Further investigations illustrated that miR-4319 had low expression in NPC cells and functioned as a tumor suppressor in the progression of NPC. Mechanistic study identified forkhead box Q1 (FOXQ1) as a functional target of miR-4319. The effect of LINC00667 in NPC development was mediated by the miR-4319/FOXQ1 axis. Analysis on tumorxenograft mouse model demonstrated that knockdown of LINC00667 repressed NPC tumor growth in vivo and confirmed the in vitro results. Our present study suggested that LINC00667 promoted the malignant phenotypes of NPC cells by competitively binding to miR-4319 to up-regulate FOXQ1 expression. Our results reveled that LINC00667 could be a diagnostic and therapeutic target for NPC patients.

15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma

Background

Our study aims to develop a lncRNA-based classifier and a nomogram incorporating the genomic signature and clinicopathologic factors to help to improve the accuracy of recurrence prediction for hepatocellular carcinoma (HCC) patients.

Methods

The lncRNA profiling data of 374 HCC patients and 50 normal healthy controls were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a 15-lncRNA-based classifier and compared our classifier to the existing six-lncRNA signature. Besides, a nomogram incorporating the genomic classifier and clinicopathologic factors was also developed. The predictive accuracy and discriminative ability of the genomic-clinicopathologic nomogram were determined by a concordance index (C-index) and calibration curve and were compared with the TNM staging system by the C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate the clinical value of our nomogram.

Results

Fifteen relapse-free survival (RFS-) related lncRNAs were identified, and the classifier, consisting of the identified 15 lncRNAs, could effectively classify patients into the high-risk and low-risk subgroups. The prediction accuracy of the 15-lncRNA-based classifier for predicting 2-year and 5-year RFS was 0.791 and 0.834 in the training set and 0.684 and 0.747 in the validation set, respectively, which was better than the existing six-lncRNA signature. Moreover, the AUC of genomic-clinicopathologic nomogram in predicting RFS were 0.837 in the training set and 0.753 in the validation set, and the C-index of the genomic-clinicopathologic nomogram was 0.78 (0.72-0.83) in the training set and 0.71 (0.65-0.76) in the validation set, which was better than the traditional TNM stage and 15-lncRNA-based classifier. The decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage and 15-lncRNA-based classifier. The results were confirmed externally.

Conclusion

Compared to the TNM stage, the 15-lncRNAs-based classifier-clinicopathologic nomogram is a more effective and valuable tool to identify HCC recurrence and may aid in clinical decision-making.

An Integrating Immune-Related Signature to Improve Prognosis of Hepatocellular Carcinoma

Growing evidence suggests that the superiority of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) could act as biomarkers for cancer prognosis. However, the prognostic marker for hepatocellular carcinoma with high accuracy and sensitivity is still lacking. In this research, a retrospective, cohort-based study of genome-wide RNA-seq data of patients with hepatocellular carcinoma was carried out, and two protein-coding genes (GTPBP4, TREM-1) and one lncRNA (LINC00426) were sorted out to construct an integrative signature to predict the prognosis of patients. The results show that both the AUC and the C-index of this model perform well in TCGA validation dataset, cross-platform GEO validation dataset, and different subsets divided by gender, stage, and grade. The expression pattern and functional analysis show that all three genes contained in the model are associated with immune infiltration, cell proliferation, invasion, and metastasis, providing further confirmation of this model. In summary, the proposed model can effectively distinguish the high- and low-risk groups of hepatocellular carcinoma patients and is expected to shed light on the treatment of hepatocellular carcinoma and greatly improve the patients' prognosis.

Identification of a nomogram based on an 8-lncRNA signature as a novel diagnostic biomarker for head and neck squamous cell carcinoma

Long noncoding RNAs (lncRNAs) have been proposed as diagnostic or prognostic biomarkers of head and neck squamous carcinoma (HNSCC). The current study aimed to develop a lncRNA-based prognostic nomogram for HNSCC. LncRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. After the reannotation of lncRNAs, the differential analysis identified 253 significantly differentially expressed lncRNAs in training set TCGA-HNSC (n = 300). The prognostic value of each lncRNA was first estimated in univariate Cox analysis, and 41 lncRNAs with P < 0.05 were selected as seed lncRNAs for Cox LASSO regression, which identified 11 lncRNAs. Multivariate Cox analysis was used to establish an 8-lncRNA signature with prognostic value. Patients in the high-signature score group exhibited a significantly worse overall survival (OS) than those in the low-signature score group, and the area under the receiver operating characteristic (ROC) curve for 3-year survival was 0.74. Multivariable Cox regression analysis among the clinical characteristics and signature scores suggested that the signature is an independent prognostic factor. The internal validation cohort, external validation cohort, and 102 HNSCC specimens quantified by qRT-PCR successfully validate the robustness of our nomogram.

Immune infiltration-associated serum amyloid A1 predicts favorable prognosis for hepatocellular carcinoma

BACKGROUND

Serum amyloid A1 (SAA1) is an acute-phase protein involved in acute or chronic hepatitis. Its function is still controversial. In addition, the effect of the expression of SAA1 and its molecular function on the progression in hepatocellular carcinoma (HCC) is still unclear.

AIM

To demonstrate the expression of SAA1 and its effect on the prognosis in HCC and explain further the correlation of SAA1 and immunity pathways.

METHODS

SAA1 expression in HCC was conducted with The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) in GEPIA tool, and the survival analysis based on the SAA1 expression level was achieved in the Kaplan-Meier portal. The high or low expression group was then drawn based on the median level of SAA1 expression. The correlation of SAA1 and the clinical features were conducted in the UALCAN web-based portal with TCGA-LIHC, including tumor grade, patient disease stage, and the TP53 mutation. The correlation analysis between SAA1 expression and TP53 mutation was subjected to the TCGA portal. The tumor purity score and the immune score were analyzed with CIBERSORT. The correlation of SAA1 expression and tumor-infiltrating lymphocytes was achieved in TISIDB web-based integrated repository portal for tumor-immune system interactions. GSE125336 dataset was used to test the SAA1 expression in the responsive or resistant group with anti-PD1 therapy. Gene set enrichment analysis was applied to evaluate the gene enrichment signaling pathway in HCC. The similar genes of SAA1 in HCC were identified in GEPIA, and the protein-protein interaction of SAA1 was conducted in the Metascape tool. The expression of C-X-C motif chemokine ligand 2, C-C motif chemokine ligand 23, and complement C5a receptor 1 was studied and overall survival analysis in HCC was conducted in GEPIA and Kaplan-Meier portal, respectively.

RESULTS

SAA1 expression was decreased in HCC, and lower SAA1 expression predicted poorer overall survival, progression-free survival, and disease-specific survival. Furthermore, SAA1 expression was further decreased with increased tumor grade and patient disease stage. Also, SAA1 expression was further downregulated in patients with TP53 mutation compared with patients with wild type TP53. SAA1 expression was negatively correlated with the TP53 mutation. Lower SAA1 predicted poorer survival rate, especially in the patients with no hepatitis virus infection, other than those with hepatitis virus infection. Moreover, the SAA1 expression was negatively correlated with tumor purity. In contrast, SAA1 expression was positively correlated with the immune score in HCC, and the correlation analysis between SAA1 expression and tumor-infiltrating lymphocytes also showed a positive correlation in HCC. Decreased SAA1 was closely associated with the immune tolerance of HCC. C-X-C motif chemokine ligand 2 and C-C motif chemokine ligand 23 genes were identified as the hub genes associated with SAA1, which could also serve as favorable prognosis markers for HCC.

CONCLUSION

SAA1 is downregulated in the liver tumor, and it is closely involved in the progression of HCC. Lower SAA1 expression indicates lower survival rate, especially for those patients without hepatitis virus infection. Lower SAA1 expression also suggests lower immune infiltrating cells, especially for those with immune cells exerting anti-tumor immune function. SAA1 expression is closely associated with the anti-tumor immune pathways.

Prognostic Value of the FOXK Family Expression in Patients with Locally Advanced Rectal Cancer Following Neoadjuvant Chemoradiotherapy

Purpose

To assess the role of the expression levels of FOXK family members, FOXK1 and FOXK2, in predicting response to neo-chemoradiotherapy (NCRT) and prognosis in locally advanced rectal cancer (LARC).

Methods

A total of 256 LARC patients who underwent NCRT and radical resection between 2011 and 2017 were enrolled in the present study. The patients were divided into a training dataset (n=169, 2011–2015) and a validation dataset (n=87, 2016–2017). Tumor tissues were collected before NCRT and post-surgery and were used for immunohistochemical analysis.

Results

Oncomine database analysis revealed that FOXK1 and FOXK2 were overexpressed in most cancers especially in colorectal cancer. Additionally, overexpression of FOXK1 and FOXK2 was associated with poorer prognosis by the R2 database. In both our training and validation datasets, the expression of FOXK1 and FOXK2 was lower in the pathological complete response (pCR) group compared with the non-pCR group (P<0.05). Cox regression analysis demonstrated that pathological N stage (HR=1.810, 95% CI 1.159–2.827, P=0.009), FOXK1 expression (HR=5.831, 95% CI 2.925–11.625, P<0.001), and FOXK2 expression (HR=2.390, 95% CI 11.272–4.491, P=0.007) were independent predictors of disease-free survival (DFS). Based on the Cox multivariate analysis, we constructed a risk score model that served as a prognostic biomarker and had a powerful ability to predict pCR in LARC patients upon NCRT in both training and validation groups.

Conclusion

Expression levels of FOXK family members were associated with chemoradiotherapy resistance and prognosis of LARC patients following NCRT and were used to construct a risk score model that is a promising biomarker for LARC.

Development and Validation of an Immune-Related lncRNA Signature for Predicting the Prognosis of Hepatocellular Carcinoma

Aim

Immunotherapy is currently being explored as a potential treatment for hepatocellular carcinoma (HCC). This study investigated the prognostic value of immune-related long non-coding RNAs (lncRNAs) in patients with HCC.

Methods

The Wilcoxon test was used to compare differentially expressed lncRNAs between HCC tissue and non-tumor tissue. Moreover, co-expression analysis was used to determine immune-related lncRNA. Univariate cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression were used to identify immune-related prognostic lncRNA. The immune risk score was calculated by the sum of the product from each lncRNA expression and its coefficient. Furthermore, the prognostic significance of the lncRNA signature was determined in the training group, testing group, and the entire group. A prognostic nomogram was established by integrating immune risk score and clinicopathological features.

Results

PRRT3-AS1 and AL031985.3 were identified as immune-related prognostic lncRNAs in HCC patients. HCC patients were divided into high and low-risk groups based on the optimal cutoff value of risk score in the training group. The prognosis of HCC patients in the high-risk group was worse compared with the low-risk group. Besides, the immune-related lncRNA score was regarded as an independent risk factor for the prognosis of HCC patients. The predictive nomogram showed satisfactory discrimination and consistency. Gene enrichment analysis results indicated that the high-risk group was associated with immune-related signaling pathways.

Conclusion

This study screened a 2-lncRNA signature and constructed a nomogram to predict the survival of HCC patients, thereby provided guidelines for undertaking medical decisions.

A prognostic model composed of four long noncoding RNAs predicts the overall survival of Asian patients with hepatocellular carcinoma

Based on accumulating evidence, long noncoding RNAs (lncRNAs) are potential biomarkers and therapeutic targets for many diseases, including tumors. In this study, we consulted The Cancer Genome Atlas (TCGA) database to explore the functions and modulatory mechanisms of lncRNAs as competing endogenous RNAs (ceRNAs) in hepatocellular carcinoma (HCC) in Asian patients and constructed a risk scoring system composed of four lncRNAs (SNHG1, STEAP3-AS1, RUSC1-AS1, and SNHG3) to predict the outcomes of Asian patients with HCC. The prognostic value of this risk model was validated in the internal validation cohort (n = 157). The stratified survival analysis revealed good performance for the risk model in stratifying clinical features. According to the Cox proportional hazard regression analysis, the four-lncRNA risk model is an independent prognostic model for Asian patients with HCC. Finally, we developed a nomogram that integrates prognostic signals and other clinical information to predict 1-, 3-, and 5-year overall survival rates. In conclusion, the prognostic lncRNAs identified in our study exerted potential biological effects on the development of HCC. The risk scoring model based on four lncRNAs may be an effective classification tool for assessing the prognosis of Asian patients with HCC.

Risk Scoring System based on lncRNA Expression for Predicting Survival in Hepatocellular Carcinoma with Cirrhosis

Objective:

This study aims to explore the roles of long non-coding RNAs (lncRNAs) for predicting survival in hepatocellular carcinoma (HCC) patients with cirrhosis.

Methods:

A set of lncRNAs differentially expressed between HCC patients with or without cirrhosis was identified using expression profiles of The Cancer Genome Atlas database, and these lncRNAs were screened for their risk scoring system to predict recurrence-free survival (RFS) or overall survival (OS). Predictive ability of risk scoring systems was confirmed using uni- and multivariate Cox analyses while adjusting for clinical features. Predictive lncRNAs were analyzed by functional enrichment analysis.

Results:

Our screen identified 22 lncRNAs that were upregulated in the presence of cirrhosis and 59 that were downregulated. To predict OS of HCC patients with cirrhosis, a risk scoring system was developed with four lncRNAs (LINC02086, LINC00880, LINC01549 and AC136475.3); to predict RFS in these patients, the risk scoring system contained five lncRNAs (SH3RF3-AS1, AC104117.3, AC136475.3, LINC00239 and MRPL23-AS1). All risk scoring systems were associated with an area under the receiver operating characteristic curve > 0.7. Based on uni- and multivariate Cox analyses, the risk scoring system could serve as a significant independent predictor for OS in HCC patients with cirrhosis. Functional enrichment analysis suggested that the lncRNAs in the risk scoring systems are involved primarily in the pathway of Wnt signal and cytokine-cytokine receptor interaction.

Conclusion:

Risk scoring systems based on lncRNAs can effectively predict OS of HCC patients with cirrhosis. The system should be further developed and validated in larger, preferably multi-site patient populations.

A four-long noncoding RNA signature predicts survival of hepatocellular carcinoma patients

Background

Hepatocellular carcinoma (HCC) is a common neoplasm located in the liver. Accumulating evidence has highlighted that long noncoding RNAs (lncRNAs) are correlated with the survival of HCC patients. This study focuses on finding a lncRNA signature to predict the prognostic risk of HCC patients.

Methods

Statistical and machine learning analyses were conducted to analyze the lncRNA expression data and corresponding clinical data of 180 HCC patients collected from the public online Tanric and The Cancer Genome Atlas (TCGA) databases.

Results

From the training dataset, we obtained the four‐lncRNA model comprising RP11‐495K9.6, RP11‐96O20.2, RP11‐359K18.3, and LINC00556 which can divide HCC patients into two different groups with significantly different prognosis (n = 90, median 1.81, 95% confidence interval [CI]: 1.50‐4.91 vs 8.56 years, 95% CI: 6.96‐9.97, log‐rank test P < .001). The test dataset confirmed the prognostic ability of the signature (n = 90, median 1.95, 95% CI: 1.14‐4.08 vs 5.80 years, 95% CI: 3.11‐6.82, log‐rank test P = .007). Receiver operating characteristic curve displayed the better prediction efficiency of the four‐lncRNA signature than the tumor/node/metastasis stage. Cox analysis showed the four‐lncRNA signature was an independent predictor of HCC prognosis.

Conclusion

The four‐lncRNA signature can be used as an independent biomarker for HCC patients to predict the prognostic risk.

Prognostic value of small nucleolar RNAs (snoRNAs) for colon adenocarcinoma based on RNA sequencing data

Although the molecular studies of single gastrointestinal tumors have been widely reported by media, it is not clear about the function of small nucleolar RNA (snoRNA) in the progression, development and prognostic significance in colon adenocarcinoma, and its certain molecular mechanisms and functions remain to be studied. This study aims to dig out the gene expression data profile of colon adenocarcinoma and construct the prognostic molecular pathology prediction-evaluation, ultimately revealing the clinical prognostic value of snoRNA in colon adenocarcinoma. 932 differentially expressed snoRNAs of the colon adenocarcinoma were obtained by edgeR R package. Only 4 prognostically-significant snoRNAs (SNORD14E, SNORD67, SNORD12C, and SNORD17) (P < 0.05) were discovered after univariate COX regression mode analysis. Moreover, through multivariate COX regression mode analysis, 2 prognostically-significant snoRNAs (SNORD14E and SNORD67) (P < 0.05) were obtained. Using the above 473 COAD samples, a prognostic model of risk score was constructed. The inflection point of the prognostic risk score acted as a boundary to divide the patients into high-risk and low-risk groups. The K-M survival curve of the prognostic model of risk score revealed that high risk group has a lower survival rate (P < 0.05). The research has successfully provided valuable prognostic factors and prognostic models for patients with malignant colon tumor.

Risk scoring based on expression of long non‑coding RNAs can effectively predict survival in hepatocellular carcinoma patients with or without fibrosis

Patients with hepatocellular carcinoma (HCC) have different prognoses depending on whether or not they also have fibrosis. Since long non-coding RNAs (lncRNAs) affect tumor formation and progression, the present study aimed to investigate whether their expression might help predict the survival of patients with HCC. Expression profiles downloaded from The Cancer Genome Atlas database were examined to identify lncRNAs differentially expressed (DElncRNAs) between HCC patients with or without fibrosis. These DElncRNAs were then used to develop a risk scoring system to predict overall survival (OS) or recurrence-free survival (RFS). A total of 142 significant DElncRNAs were identified using data from 135 patients with fibrosis and 72 without fibrosis. For HCC patients with fibrosis, a risk scoring system to predict OS was constructed based on five lncRNAs (AL359853.1, Z93930.3, HOXA-AS3, AL772337.1 and AC012640.3), while the risk scoring system to predict RFS was based on 12 lncRNAs (PLCE1-AS1, Z93930.3, LINC02273, TRBV11-2, HHIP-AS1, AC004687.1, LINC01857, AC004585.1, AP000808.1, CU638689.4, AC090152.1 and AL357060.1). For HCC patients without fibrosis, the risk scoring system to predict OS was established based on seven lncRNAs (LINC00239, AC104971.4, AP006285.2, HOXA-AS3, AC079834.2, NRIR and LINC01929), and the system to predict RFS was based on five lncRNAs (AC021744.1, NRIR, LINC00487, AC005858.1 and AC107398.3). Areas under the receiver operating characteristic curves for all risk scoring systems exceeded 0.7. Uni- and multivariate Cox analyses showed that the risk scoring systems were significant independent predictors of OS for HCC patients with fibrosis, or of OS and RFS for HCC patients without fibrosis, after adjusting for clinical factors. Functional enrichment analysis suggested that, depending on the risk scoring system, highly associated genes were involved in pathways mainly associated with the cell cycle, chemokines, Th17 cell differentiation or thermogenesis. The findings of the present study indicate that risk scoring systems based on lncRNA expression can effectively predict the OS of HCC patients with fibrosis as well as the OS or RFS of HCC patients without fibrosis.

A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

BACKGROUND

Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC.

METHODS

We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice.

RESULTS

Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice.

CONCLUSION

This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.

Bioinformatics analysis of esophageal cancer unveils an integrated mRNA-lncRNA signature for predicting prognosis

Esophageal cancer (ESCA) carries a poor prognosis among gastrointestinal malignancies. The present study developed a signature based on mRNAs and long non-coding RNAs (lncRNAs) to predict prognosis in ESCA by using The Cancer Genome Atlas database. By using least absolute shrinkage and selection operator penalized regression, a set of RNAs (three mRNAs and two lncRNAs) was identified and used to build a risk score system of ESCA prognosis, which was used to stratify patients having considerable diverse survival in the training set [hazard ratio (HR), 3.932; 95% CI, 1.555–9.944; P<0.002] into high- and low-risk groups. The authentication of the results was achieved through the test set (HR, 3.150; 95% CI, 1.113–8.918; P<0.02) and the entire set (HR, 3.181; 95% CI, 1.686–6.006; P<0.0002). The results from multivariate Cox proportional hazard regression analysis in the entire set suggested that the prognostic significance of this signature may be independent of patients' clinicopathological characteristics. Furthermore, this signature was associated with several molecular signaling pathways of cancer according to Gene Set Enrichment Analysis. In addition, a nomogram was built and the risk score and TNM stage were integrated to estimate the 1- and 3-year overall survival rates. The results from the present study demonstrated that the integrated mRNA-lncRNA signature may be considered as a novel biomarker for the prognosis of ESCA.

RRM2 Regulated By LINC00667/miR-143-3p Signal Is Responsible For Non-Small Cell Lung Cancer Cell Progression

Background

Non-small cell lung cancer (NSCLC) is a common and fatal cancer worldwide with a very low 5-year overall survival rate. Ribonucleotide reductase M2 subunit (RRM2), a small subunit of the ribonucleotide reductase complex, has been found to be an oncogenic role in a variety of tumors including NSCLC. However, the regulatory mechanism of RRM2 in NSCLC is not clear. Increasing evidence suggests that non-coding RNAs (ncRNAs) including miRNAs and lincRNAs may promote or inhibit tumor initiation and development through regulating the expression of oncogenic genes. It is interesting to find ncRNAs which play important role in regulating RRM2 expression.

Materials and methods

The expression levels of RRM2, LINC0066 and miR-143-3p in NSCLC tumor tissues and cell lines were detected using qRT-PCR. The regulatory relationships among RRM2, LINC0066 and miR-143-3p were predicted using database analysis and verified by luciferase reporter assay and RIP analysis. The proliferation ability of NSCLC cells was assessed using CCK8 and colony formation assays. The expression of related proteins was determined by Western blot. In vivo effect of RRM2, LINC0066 and miR-143-3p to NSCLC were detected through xenograft experiments.

Results

In this study, we found RRM2 was upregulated in NSCLC tumor and cell lines, and the aberrant upregulation predicted a poor prognosis. Then, we predicted and confirmed that RRM2 was negatively regulated by miR-143-3p. Further study implied that LINC00667 acted as a ceRNA by sponging miR-143-3p and regulated RRM2 expression indirectly. Moreover, we found that the growth of NSCLC was regulated by LINC00667/miR-143-3p/RRM2 signal pathway both in vitro and in vivo. LINC00667 and RRM2 promoted the tumor growth while miR-143-3p inhibited it.

Conclusion

Our study revealed a LINC00667/miR-143-3p/RRM2 signal pathway that played an important role in the progress of NSCLC, which might be potential therapeutic targets for NSCLC.

A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma

Liver resection surgery is the most commonly used treatment strategy for patients diagnosed with hepatocellular carcinoma (HCC). However, there is still a chance for recurrence in these patients despite the survival benefits of this procedure. This study aimed to explore recurrence-related genes (RRGs) and establish a genomic-clinical nomogram for predicting postoperative recurrence in HCC patients. A total of 123 differently expressed genes and three RRGs (PZP, SPP2, and PRC1) were identified from online databases via Cox regression and LASSO logistic regression analyses and a gene-based risk model containing RRGs was then established. The Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves showed that the model performed well. Finally, a genomic-clinical nomogram incorporating the gene-based risk model, AJCC staging system, and Eastern Cooperative Oncology Group performance status was constructed to predict the 1-, 2-, and 3-year recurrence-free survival rates (RFS) for HCC patients. The C-index, ROC analysis, and decision curve analysis were good indicators of the nomogram’s performance. In conclusion, we identified three reliable RRGs associated with the recurrence of cancer and constructed a nomogram that performed well in predicting RFS for HCC patients. These findings could enrich our understanding of the mechanisms for HCC recurrence, help surgeons predict patients’ prognosis, and promote HCC treatment.

Identification of a novel four-lncRNA signature as a prognostic indicator in cirrhotic hepatocellular carcinoma

Background

Many studies have shown that long noncoding RNAs (lncRNA) are closely associated with the occurrence and development of various tumors and have the potential to be prognostic markers. Moreover, cirrhosis is an important prognostic risk factors in patients with liver cancer. Some studies have reported that lncRNA-related prognostic models have been used to predict overall survival (OS) and recurrence-free survival (RFS) in patients with hepatocellular carcinoma (HCC). However, no one has constructed a prognostic lncRNA model only in patients with cirrhotic HCC. Thus, it is necessary to screen novel potential lncRNA markers for improve the prognosis of cirrhotic HCC patients.

Methods

The probe expression profile dataset (GSE14520–GPL3921) from the Gene Expression Omnibus (GEO), which included 204 cirrhotic HCC samples, was reannotated and the lncRNA and mRNA expression dataset was obtained. The patients were randomly assigned to either the training set (n = 103) and testing set (n = 100). Univariate cox regression and the least absolute shrinkage and selection operator (LASSO) model were applied to screen lncRNAs linked to the OS of cirrhotic HCC in the training set. The lncRNAs having significant correlation with OS were then selected and the multivariate Cox regression model was implemented to construct the prognostic score model. Whether or not this model was related to RFS in the training set was simultaneously determined. The testing set was used to validate the lncRNA risk score model. A risk score based on the lncRNA signature was used for stratified analysis of different clinical features to test their prognostic performance. The prognostic lncRNA-related protein genes were identified by the co-expression matrix of lncRNA-mRNA, and the function of these lncRNAs was predicted through the enrichment of these co-expression genes.

Results

The signature consisted of four lncRNAs:AC093797.1,POLR2J4,AL121748.1 and AL162231.4. The risk model was closely correlated with the OS of cirrhotic HCC in the training cohort, with a hazard ratio (HR) of 3.650 (95% CI [1.761–7.566]) and log-rank P value of 0.0002. Moreover, this model also showed favorable prognostic significance for RFS in the training set (HR: 2.392, 95% CI [1.374–4.164], log-rank P = 0.0015). The predictive performance of the four-lncRNA model for OS and RFS was verified in the testing set. Furthermore, the results of stratified analysis revealed that the four-lncRNA model was an independent factor in the prediction of OS and RFS of patients with clinical characteristics such as TNM (Tumor, Node, Metastasis system) stages I–II, Barcelona Clinic Liver Cancer (BCLC) stages 0–A, and solitary tumors in both the training set and testing set. The results of functional prediction showed that four lncRNAs may be potentially involve in multiple metabolic processes, such as amino acid, lipid, and glucose metabolism in cirrhotic HCC.

Long non-coding RNA MIAT promotes papillary thyroid cancer progression through upregulating LASP1

Background

Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play an important role in initiation and development of thyroid cancer. However, the underlying molecular mechanism remains elusive.

Methods

To explore potential oncogenic and tumor suppressive lncRNAs in papillary thyroid cancer (PTC), we performed RNA sequencing to discover differentially expression lncRNAs between PTC tissues and matched normal tissues. RT-qPCR was used to validate differentially expressed lncRNAs. Bioinformatic analysis was performed to predicted potential miRNA and gene which might be regulated by MIAT. Cell proliferation, invasion and cycle assay were conducted to study the function of MIAT and LASP1 in PTC.

Results

Through analysis of RNA sequencing, we observed that lncRNA-MIAT was overexpressed in PTC tissues. The upregulation of MIAT was further confirmed in 40 pairs of PTC tissues and normal tissues we collected. In the function assays, results suggested that MIAT silencing led to inhibition of cell proliferation, invasion and disruption of cell cycle progression in PTC cells. Mechanistically, MIAT directly bound to miR-324-3p and upregulated LASP1 expression in PTC cells. In addition, expression of MIAT was positively correlated with LASP1 mRNA expression in samples collected from patients with PTC. More importantly, transfection of recombinant LASP1 attenuated MIAT silencing induced inhibition of cell proliferation, invasion and disruption of cell cycle progression in PTC cells.

Conclusions

In conclusion, the findings suggest that lncRNA-MIAT may promote PTC proliferation and invasion through physically binding miR-324-3p and upregulation of LASP1.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0913-z) contains supplementary material, which is available to authorized users.

Identification of pathological grade and prognosis-associated lncRNA for ovarian cancer

Ovarian carcinoma (OC) is one of the most common malignant tumors in female genitals. In recent years, the therapeutic effect of OC has been significantly improved through the application of effective chemotherapy regimen. However, the 5-year survival rate is also lower than 30% due to high rate of relapse. So, it is needed to screen reliable predictive and prognostic markers of OC. Ovarian cancer gene expression data and corresponding clinical data used were downloaded from Gene Expression Omnibus database. Weighted gene expression network analysis (WGCNA) and Cox proportional hazards regression (PHR) were used to screen Pathological Grade and Prognosis-associated long noncoding RNA (lncRNA). Kaplan-Meier analysis and receiver operating characteristic curves analysis were performed to evaluate the predictive ability of the selected lncRNA. Gene Ontology (GO) enrichment and Gene Set Enrichment Analysis (GSEA) enrichment analysis methods were used to explore the possible mechanisms of the selected lncRNA affecting the development of OC. Five reliably lncRNAs (LINC00664, LINC00667, LINC01139, LINC01419, and LOC286437) was identified through a series of bioinformatics methods. In testing cohorts, we found that the five lncRNAs in predicting the risk of OC recurrence is robustness, and multivariate Cox PHR analysis indicate that the five lncRNAs is an independent risk factor for OC recurrence. Moreover, GO and GSEA enrichment analysis showed that the five lncRNAs are involved in multiple ovarian cancer occurrence mechanism. In summary, all these findings indicated that the five lncRNAs can effectively predict the risk of recurrence of ovarian cancer.

Integrated analysis of a competing endogenous RNA network reveals key long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

Growing evidence has revealed that long noncoding RNAs (lncRNAs) have an important impact on tumorigenesis and tumor progression via a mechanism involving competing endogenous RNAs (ceRNAs). However, their use in predicting the survival of a patient with hepatocellular carcinoma (HCC) remains unclear. The aim of this study was to develop a novel lncRNA expression-based risk score system to accurately predict the survival of patients with HCC. In our study, using expression profiles downloaded from The Cancer Genome Atlas database, the differentially expressed messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs) were explored in patients with HCC and normal liver tissues, and then a ceRNA network constructed. A risk score system was established between lncRNA expression of the ceRNA network and overall survival (OS) or recurrence-free survival (RFS); it was further analyzed for associations with the clinical features of patients with HCC. In HCC, 473 differentially expressed lncRNAs, 63 differentially expressed miRNAs, and 1417 differentially expressed mRNAs were detected. The ceRNA network comprised 41 lncRNA nodes, 12 miRNA nodes, 24 mRNA nodes, and 172 edges. The lncRNA expression-based risk score system for OS was constructed based on six lncRNAs (MYLK-AS1, AL359878.1, PART1, TSPEAR-AS1, C10orf91, and LINC00501), while the risk score system for RFS was based on four lncRNAs (WARS2-IT1, AL359878.1, AL357060.1, and PART1). Univariate and multivariate Cox analyses showed the risk score systems for OS or RFS were significant independent factors adjusted for clinical factors. Receiver operating characteristic curve analysis showed the area under the curve for the risk score system was 0.704 for OS, and 0.71 for RFS. Our result revealed a lncRNA expression-based risk score system for OS or RFS can effectively predict the survival of patients with HCC and aid in good clinical decision-making.

Amino acid metabolism-related gene expression-based risk signature can better predict overall survival for glioma

Metabolic reprogramming has been proposed to be a hallmark of cancer. Aside from the glycolytic pathway, the metabolic changes of cancer cells primarily involve amino acid metabolism. However, in glioma, the characteristics of the amino acid metabolism‐related gene set have not been systematically profiled. In the present study, RNA sequencing expression data from 309 patients in the Chinese Glioma Genome Atlas database were included as a training set, while another 550 patients within The Cancer Genome Atlas database were used to validate. Consensus clustering of the 309 samples yielded two robust groups. Compared with Cluster1, Cluster2 correlated with a better clinical outcome. We then developed an amino acid metabolism‐related risk signature for glioma. Our results showed that patients in the high‐risk group had dramatically shorter overall survival than low‐risk counterparts in any subgroup, stratified by isocitrate dehydrogenase and 1p/19q status based on the 2016 World Health Organization classification guidelines. The 30‐gene signature showed better prognostic value than the traditional factors “age” and “grade” by analyzing the receiver operating characteristic curve with areas under curve of 0.966, 0.692, 0.898 and 0.975, 0.677, 0.885 for 3‐ and 5‐year survival, respectively. Moreover, univariate and multivariate analysis showed that the 30‐gene signature was an independent prognostic factor for glioma. Furthermore, Gene Ontology analysis and Gene Set Enrichment Analysis showed that tumors with a high risk score correlated with various aspects of the malignancy of glioma. In summary, we demonstrated a novel amino acid metabolism‐related risk signature for predicting prognosis for glioma.