Protein-coding genes combined with long non-coding RNAs predict prognosis in esophageal squamous cell carcinoma patients as a novel clinical multi-dimensional signature

Targeting metabolic reprogramming in hepatocellular carcinoma to overcome therapeutic resistance: A comprehensive review

Hepatocellular carcinoma (HCC) poses a heavy burden on human health with high morbidity and mortality rates. Systematic therapy is crucial for advanced and mid-term HCC, but faces a significant challenge from therapeutic resistance, weakening drug effectiveness. Metabolic reprogramming has gained attention as a key contributor to therapeutic resistance. Cells change their metabolism to meet energy demands, adapt to growth needs, or resist environmental pressures. Understanding key enzyme expression patterns and metabolic pathway interactions is vital to comprehend HCC occurrence, development, and treatment resistance. Exploring metabolic enzyme reprogramming and pathways is essential to identify breakthrough points for HCC treatment. Targeting metabolic enzymes with inhibitors is key to addressing these points. Inhibitors, combined with systemic therapeutic drugs, can alleviate resistance, prolong overall survival for advanced HCC, and offer mid-term HCC patients a chance for radical resection. Advances in metabolic research methods, from genomics to metabolomics and cells to organoids, help build the HCC metabolic reprogramming network. Recent progress in biomaterials and nanotechnology impacts drug targeting and effectiveness, providing new solutions for systemic therapeutic drug resistance. This review focuses on metabolic enzyme changes, pathway interactions, enzyme inhibitors, research methods, and drug delivery targeting metabolic reprogramming, offering valuable references for metabolic approaches to HCC treatment.

ESCCdb: A Comprehensive Database and Key Regulator Exploring Platform Based on Cross Dataset Comparisons for Esophageal Squamous Cell Carcinoma

Esophageal cancer is the seventh most prevalent and the sixth most lethal cancer. Esophageal squamous cell carcinoma (ESCC) is one of the major esophageal cancer subtypes that accounts for 87 % of the total cases. However, its molecular mechanism remains unclear. Here, we present an integrated database for ESCC called ESCCdb, which includes a total of 56 datasets and published studies from the GEO, Xena or SRA databases and related publications. It helps users to explore a particular gene with multiple graphical and interactive views with one click. The results comprise expression changes across 20 datasets, copy number alterations in 11 datasets, somatic mutations from 12 papers, related drugs derived from DGIdb, related pathways, and gene correlations. ESCCdb enables directly cross-dataset comparison of a gene's mutations, expressions and copy number changes in multiple datasets. This allows users to easily assess the alterations in ESCC. Furthermore, survival analysis, drug-gene relationships, and results from whole-genome CRISPR/Cas9 screening can help users determine the clinical relevance, derive functional inferences, and identify potential drugs. Notably, ESCCdb also enables the exploration of the correlation structure and identification of potential key regulators for a process. Finally, we identified 789 consistently differential expressed genes; we summarized recurrently mutated genes and genes affected by significant copy number alterations. These genes may be stable biomarkers or important players during ESCC development. ESCCdb fills the gap between massive omics data and users' needs for integrated analysis and can promote basic and clinical ESCC research. The database is freely accessible at http://cailab.labshare.cn/ESCCdb.

Tumor-derived OBP2A promotes prostate cancer castration resistance

Androgen deprivation therapy (ADT) is a systemic therapy for advanced prostate cancer (PCa); although most patients initially respond to ADT, almost all cancers eventually develop castration-resistant PCa (CRPC). Currently, most research focuses on castration-resistant tumors, and the role of tumors in remission is almost completely ignored. Here, we report that odorant-binding protein (OBP2A) released from tumors in remission during ADT catches survival factors, such as CXCL15/IL8, to promote PCa cell androgen-independent growth and enhance the infiltration of myeloid-derived suppressor cells (MDSCs) into tumor microenvironment, leading to the emergence of castration resistance. OBP2A knockdown significantly inhibits CRPC and metastatic CRPC development and improves therapeutic efficacy of CTLA-4/PD-1 antibodies. Treatment with OBP2A-binding ligand α-pinene interrupts the function of OBP2A and suppresses CRPC development. Furthermore, α-pinene-conjugated doxorubicin/docetaxel can be specifically delivered to tumors, resulting in improved anticancer efficacy. Thus, our studies establish a novel concept for the emergence of PCa castration resistance and provide new therapeutic strategies for advanced PCa.

Construction of the Six-lncRNA Prognosis Signature as a Novel Biomarker in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is a common malignant gastrointestinal tumor threatening global human health. For patients diagnosed with ESCC, determining the prognosis is a huge challenge. Due to their important role in tumor progression, long non-coding RNAs (lncRNAs) may be putative molecular candidates in the survival prediction of ESCC patients. Here, we obtained three datasets of ESCC lncRNA expression profiles (GSE53624, GSE53622, and GSE53625) from the Gene Expression Omnibus (GEO) database. The method of statistics and machine learning including survival analysis and LASSO regression analysis were applied. We identified a six-lncRNA signature composed of AL445524.1, AC109439.2, LINC01273, AC015922.3, LINC00547, and PSPC1-AS2. Kaplan-Meier and Cox analyses were conducted, and the prognostic ability and predictive independence of the lncRNA signature were found in three ESCC datasets. In the entire set, time-dependent ROC curve analysis showed that the prediction accuracy of the lncRNA signature was remarkably greater than that of TNM stage. ROC and stratified analysis indicated that the combination of six-lncRNA signature with the TNM stage has the highest accuracy in subgrouping ESCC patients. Furthermore, experiments subsequently confirmed that one of the lncRNAs LINC01273 may play an oncogenic role in ESCC. This study suggested the six-lncRNA signature could be a valuable survival predictor for patients with ESCC and have potential to be an auxiliary biomarker of TNM stage to subdivide ESCC patients more accurately, which has important clinical significance.

Fatty acid transport protein-5 (FATP5) deficiency enhances hepatocellular carcinoma progression and metastasis by reprogramming cellular energy metabolism and regulating the AMPK-mTOR signaling pathway

Aberrant lipid metabolism is an essential feature of hepatocellular carcinoma (HCC). Fatty acid transport protein-5 (FATP5) is highly expressed in the liver and is involved in the fatty acid transport pathway. However, the potential role of FATP5 in the pathogenesis of HCC remains largely unknown. Herein, we showed that FATP5 was downregulated in HCC tissues and even much lower in vascular tumor thrombi. Low expression of FATP5 was correlated with multiple aggressive and invasive clinicopathological characteristics and contributed to tumor metastasis and a poor prognosis in HCC patients. FATP5 inhibited the epithelial–mesenchymal transition (EMT) process and suppressed HCC cell migration and invasion, while silencing FATP5 had the opposite effects. Mechanistically, knockdown of FATP5 promoted cellular glycolytic flux and ATP production, thus suppressing AMP-activated protein kinase (AMPK) and activating its downstream signaling mammalian target of rapamycin (mTOR) to support HCC progression and metastasis. Activation of AMPK using metformin reversed the EMT program and impaired the metastatic capacity of FATP5-depleted HCC cells. Collectively, FATP5 served as a novel suppressor of HCC progression and metastasis partly by regulating the AMPK/mTOR pathway in HCC, and targeting the FATP5-AMPK axis may be a promising therapeutic strategy for personalized HCC treatment.

A Novel Three-LncRNA Signature Predicting Tumor Recurrence in Nonfunctioning Pituitary Adenomas

The nonfunctioning pituitary adenoma (NFPA) recurrence rate is relatively high after surgical resection. Here, we constructed effective long noncoding RNA (lncRNA) signatures to predict NFPA prognosis. LncRNAs expression microarray sequencing profiles were obtained from 66 NFPAs. Sixty-six patients were randomly separated into a training (n = 33) and test group (n = 33). Univariable Cox regression and a machine learning algorithm was used to filter lncRNAs. Time-dependent receiver operating characteristic (ROC) analysis was performed to improve the prediction signature. Three lncRNAs (LOC101927765, RP11-23N2.4 and RP4-533D7.4) were included in a prognostic signature with high prediction accuracy for tumor recurrence, which had the largest area under ROC curve (AUC) value in the training/test group (AUC = 0.87/0.73). The predictive ability of the signature was validated by Kaplan-Meier survival analysis. A signature-based risk score model divied patients into two risk group, and the recurrence-free survival rates of the groups were significantly different (log-rank p < 0.001). In addition, the ROC analysis showed that the lncRNA signature predictive ability was significantly better than that of age in the training/testing/entire group (AUC = 0.87/0.726/0.798 vs. AUC = 0.683/0.676/0.679). We constructed and verified a three-lncRNA signature predictive of recurrence, suggesting potential therapeutic targets for NFPA.

Development of a Prognostic Five-Gene Signature for Diffuse Lower-Grade Glioma Patients

Background: Diffuse lower-grade gliomas (LGGs) are infiltrative and heterogeneous neoplasms. Gene signature including multiple protein-coding genes (PCGs) is widely used as a tumor marker. This study aimed to construct a multi-PCG signature to predict survival for LGG patients.

Methods: LGG data including PCG expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Survival analysis, receiver operating characteristic (ROC) analysis, and random survival forest algorithm (RSFVH) were used to identify the prognostic PCG signature.

Results: From the training (n = 524) and test (n = 431) datasets, a five-PCG signature which can classify LGG patients into low- or high-risk group with a significantly different overall survival (log rank P < 0.001) was screened out and validated. In terms of prognosis predictive performance, the five-PCG signature is stronger than other clinical variables and IDH mutation status. Moreover, the five-PCG signature could further divide radiotherapy patients into two different risk groups. GO and KEGG analysis found that PCGs in the prognostic five-PCG signature were mainly enriched in cell cycle, apoptosis, DNA replication pathways.

Conclusions: The new five-PCG signature is a reliable prognostic marker for LGG patients and has a good prospect in clinical application.

Construction and Analysis of the Dysregulated ceRNA Network and Identification of Risk Long Noncoding RNAs in Breast Cancer

Breast cancer (BRCA) is the second leading cause of cancer-related mortality in women worldwide. However, the molecular mechanism involved in the development of BRCA is not fully understood. In this study, based on the miRNA-mediated long non-coding RNA (lncRNA)-protein coding gene (PCG) relationship and lncRNA-PCG co-expression information, we constructed and analyzed a specific dysregulated lncRNA-PCG co-expression network in BRCA. Then, we performed the random walk with restart (RWR) method to prioritize BRCA-related lncRNAs through comparing their RWR score and significance. As a result, we identified 30 risk lncRNAs for BRCA, which can distinguish normal and tumor samples. Moreover, through gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, we found that these risk lncRNAs mainly synergistically exerted functions related to cell cycle and DNA separation and replication. At last, we developed a four-lncRNA prognostic signature (including AP000851.1, LINC01977, MAFG-DT, SIAH2-AS1) and assessed the survival accuracy of the signature by performing time-dependent receiver operating characteristic (ROC) analysis. The areas under the ROC curve for 1, 3, 5, and 10 years of survival prediction were 0.68, 0.61, 0.62, and 0.63, respectively. The multivariable Cox regression results verified that the four-lncRNA signature could be used as an independent prognostic biomarker in BRCA. In summary, these results have important reference value for the study of diagnosis, treatment, and prognosis evaluation of BRCA.

Prognostic Value of an Autophagy-Related Five-Gene Signature for Lower-Grade Glioma Patients

Background: Molecular characteristics can be good indicators of tumor prognosis and have been introduced into the classification of gliomas. The prognosis of patients with newly classified lower-grade gliomas (LGGs, including grade 2 and grade 3 gliomas) is highly heterogeneous, and new molecular markers are urgently needed.

Methods: Autophagy related genes (ATGs) were obtained from Human Autophagy Database (HADb). From the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), gene expression profiles including ATG expression information and patient clinical data were downloaded. Cox regression analysis, receiver operating characteristic (ROC) analysis, Kaplan–Meier analysis, random survival forest algorithm (RSFVH) and stratification analysis were performed.

Results: Through univariate Cox regression analysis, we found a total of 127 ATGs associated with the prognosis of LGG patients from TCGA dataset and a total of 131 survival-related ATGs from CGGA dataset. Using TCGA dataset as the training group (n = 524), we constructed a five-ATG signature (including BAG1, BID, MAP1LC3C, NRG3, PTK6), which could divide LGG patients into two risk groups with significantly different overall survival (Log Rank P < 0.001). Then we confirmed in the independent CGGA dataset that the five-ATG signature had the ability to predict prognosis (n = 431, Log Rank P < 0.001). We further discovered that the predictive ability of the five-ATG signature was better than the existing clinical indicators and IDH mutation status. In addition, the five-ATG signature could further classify patients after receiving radiotherapy or chemotherapy into groups with different prognosis.

Conclusions: We identified a five-ATG signature that could be a reliable prognostic marker and might be therapeutic targets for autophagy therapy for LGG patients.

RNA sequencing reveals the expression profiles of circRNA and identifies a four-circRNA signature acts as a prognostic marker in esophageal squamous cell carcinoma

Background

CircRNAs with tissue-specific expression and stable structure may be good tumor prognostic markers. However, the expression of circRNAs in esophageal squamous cell carcinoma (ESCC) remain unknown. We aim to identify prognostic circRNAs and construct a circRNA-related signature in ESCC.

Methods

RNA sequencing was used to test the circRNA expression profiles of 73 paired ESCC tumor and normal tissues after RNase R enrichment. Bioinformatics methods, such as principal component analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm, unsupervised clustering and hierarchical clustering were performed to analyze the circRNA expression characteristics. Univariate cox regression analysis, random survival forests-variable hunting (RSFVH), Kaplan–Meier analysis, multivariable Cox regression and ROC (receiver operating characteristic) curve analysis were used to screen the prognostic circRNA signature. Real-time quantitative PCR (qPCR) and fluorescence in situ hybridization(FISH) in 125 ESCC tissues were performed.

Results

Compared with normal tissues, there were 11651 differentially expressed circRNAs in cancer tissues. A total of 1202 circRNAs associated with ESCC prognosis (P < 0.05) were identified. Through bioinformatics analysis, we screened a circRNA signature including four circRNAs (hsa_circ_0000005, hsa_circ_0007541, hsa_circ_0008199, hsa_circ_0077536) which can classify the ESCC patients into two groups with significantly different survival (log rank P < 0.001), and found its predictive performance was better than that of the TNM stage(0.84 vs. 0.66; 0.65 vs. 0.62). Through qPCR and FISH experiment, we validated the existence of the screened circRNAs and the predictive power of the circRNA signature.

Conclusion

The prognostic four-circRNA signature could be a new prognostic biomarker for ESCC, which has high clinical application value.

Identification of a novel CpG methylation signature to predict prognosis in lung squamous cell carcinoma

BACKGROUND

DNA methylation plays a vital role in modulating genomic function and warrants evaluation as a biomarker for the diagnosis and treatment of lung squamous cell carcinoma (LUSC).

OBJECTIVE

In this study, we aimed to identify effective potential biomarkers for predicting prognosis and drug sensitivity in LUSC.

METHODS

A univariate Cox proportional hazards regression analysis, a random survival forests-variable hunting (RSFVH) algorithm, and a multivariate Cox regression analysis were adopted to analyze the methylation profile of patients with LUSC included in public databases: The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO).

RESULTS

A methylated region consisting of 3 sites (cg06675147, cg07064331, cg20429172) was selected. Patients were divided into a high-risk group and a low-risk group in the training dataset. High-risk patients had shorter overall survival (OS) (hazard ratio [HR]: 2.72, 95% confidence interval [CI]: 1.82-4.07, P< 0.001) compared with low-risk patients. The accuracy of the prognostic signature was validated in the test and validation cohorts (TCGA, n= 94; GSE56044, n= 23). Gene set variation analysis (GSVA) showed that activity in the cell cycle/mitotic, ERBB, and ERK/MAPK pathways was higher in the high-risk compared with the low-risk group, which may lead to differences in OS.Interestingly, we observed that patients in the high-risk group were more sensitive to gemcitabine and docetaxel than the low-risk group, which is consistent with results of the GSVA.

CONCLUSION

We report novel methylation sites that could be used as powerful tools for predicting risk factors for poorer survival in patients with LUSC.

Prognostic value of a five-lncRNA signature in esophageal squamous cell carcinoma

Background

The aim of this study was to identify prognostic long non-coding RNAs (lncRNAs) and develop a multi-lncRNA signature for suvival prediction in esophageal squamous cell carcinoma (ESCC).

Methods

The clinical and gene expression data from Gene Expression Omnibus database (GSE53624, n = 119) were obtianed as training set. A total of 98 paired ESCC tumor and normal tissues were detected by RNA sequencing and used as test set. Another 84 ESCC tissues were used for real-time quantitative PCR(qRT-PCR) and as an independent validation cohort. Survival analysis, Cox regression and Kaplan–Meier analysis were performed.

Results

We screened a prognostic marker of ESCC from the GSE53624 dataset and named it as the five-lncRNA signature including AC007179.1, MORF4L2-AS1, RP11-488I20.9, RP13-30A9.2, RP4-735C1.6, which could classify patients into high- and low-risk groups with significantly different survival(median survival: 1.75 years vs. 4.01 years, log rank P < 0.05). Then test dataset and validation dataset confirmed that the five-lncRNA signature can determine the prognosis of ESCC patients. Predictive independence of the prognostic marker was proved by multivariable Cox regression analyses in the three datasets (P < 0.05). In addition, the signature was found to be better than TNM stage in terms of prognosis.

Conclusion

The five-lncRNA signature could be a good prognostic biomarker for ESCC patients and has important clinical value.

Clinical significance of ring finger protein 2 high expression in skin squamous cell carcinoma

Although ring finger protein 2 (RNF2) serves an important role in the occurrence, development and regulation of various types of cancer, RNF2 expression in skin squamous cell carcinoma (SCC) remains unknown. The aim of the present study was to investigate the role of RNF2 expression in SCC and adjacent tissues from patients. The protein and gene expression levels of RNF2 in SCC and adjacent tissues were detected by immunohistochemistry (IHC), western blot analysis and semi-quantitative reverse transcription (RT) PCR. Single factor analysis was used to study the association between RNF2 expression level and the clinicopathological characteristics of patients with SCC. Multifactor Cox survival analysis was used to examine the association between RNF2 expression and the overall survival rate of postoperative patients with SCC. The results from IHC staining demonstrated that the positive expression rate of RNF2 was 84.68% (210/248) and 56.05% (139/248) in SCC and in adjacent tissues, respectively. Furthermore, results from western blot analysis demonstrated that RNF2 protein expression in SCC tissues was significantly higher compared with that in the adjacent tissues (P<0.05). The positive rate of RNF2 mRNA in SCC was 81.05% (201/248), which was significantly higher compared with that in the adjacent tissues 54.44% (135/248; P<0.05). Furthermore, RNF2 protein and gene expression levels were associated with tumor diameter, tumor stage, tumor metastasis and the degree of tumor differentiation in patients with SCC. Patients exhibiting higher RNF2 protein expression in SCC tissues had a significantly shorter disease-specific survival rate compared with patients with low RNF2 expression. In addition, RNF2 protein expression, tumor diameter, tumors site and tumor stage were independent factors affecting the overall survival rate of postoperative patients. High protein and gene expression levels of RNF2 in SCC tissues may be associated with the occurrence and development of SCC and prognosis of patients. The results form this study may serve the development of novel therapeutic options and diagnostic strategies for patients with SCC.

A five-microRNA signature for individualized prognosis evaluation and radiotherapy guidance in patients with diffuse lower-grade glioma

Despite the prognostic value of IDH and other gene mutations found in diffuse glioma, markers that judge individual prognosis of patients with diffuse lower‐grade glioma (LGG) are still lacking. This study aims to develop an expression‐based microRNA signature to provide survival and radiotherapeutic response prediction for LGG patients. MicroRNA expression profiles and relevant clinical information of LGG patients were downloaded from The Cancer Genome Atlas (TCGA; the training group) and the Chinese Glioma Genome Atlas (CGGA; the test group). Cox regression analysis, random survival forests‐variable hunting (RSFVH) screening and receiver operating characteristic (ROC) were used to identify the prognostic microRNA signature. ROC and TimeROC curves were plotted to compare the predictive ability of IDH mutation and the signature. Stratification analysis was conducted in patients with radiotherapy information. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to explore the biological function of the signature. We identified a five‐microRNA signature that can classify patients into low‐risk or high‐risk group with significantly different survival in the training and test datasets (P < 0.001). The five‐microRNA signature was proved to be superior to IDH mutation in survival prediction (AUCtraining = 0.688 vs 0.607). Stratification analysis found the signature could further divide patients after radiotherapy into two risk groups. GO and KEGG analyses revealed that microRNAs from the prognostic signature were mainly enriched in cancer‐associated pathways. The newly discovered five‐microRNA signature could predict survival and radiotherapeutic response of LGG patients based on individual microRNA expression.

Macrophages-induced long noncoding RNA H19 up-regulation triggers and activates the miR-193b/MAPK1 axis and promotes cell aggressiveness in hepatocellular carcinoma

Dysregulation of long noncoding RNA (lncRNA) H19 has been implicated in hepatocellular carcinoma (HCC), but the concrete regulatory mechanism is lack of research. We mined gene expression profiles of 457 HCC samples from TCGA and TJMUCH cohorts and further validated in 64 FFPE HCC tissues. LncRNA H19 overexpression in situ was significantly correlated with poor prognosis of HCC patients, which induced EMT, promoted stemness and accelerated invasion of HCC cells in vitro. Co-expression network analysis indicated lncRNA H19 negatively correlated with miR-193b and positively correlated with MAPK1 gene, which implicated that lncRNA H19 served as a sponge molecule to hijack miR-193b and protect MAPK1. Forced overexpression of H19 attenuated miR-193b-mediated inhibition on multiple driver oncogenes (EGFR, KRAS, PTEN and IGF1R) and MAPK1 gene, thus triggered EMT and stem cell transformation in HCC. LncRNA H19 positively correlated with CD68 ⁺ TAMs in situ. TAMs-induced lncRNA H19 promotes HCC aggressiveness via triggering and activating the miR-193b/MAPK1 axis, mediates the crosstalk between HCC and immunological microenvironment, and causes poor clinical outcomes. LncRNA H19 is a valuable predictive biomarker and potential therapeutic target in HCC.

Prognostic Significance of CIP2A in Esophagogastric Junction Adenocarcinoma: A Study of 65 Patients and a Meta-Analysis

Background

The expression of the cancerous inhibitor protein phosphatase 2A (CIP2A) appears to be predictive of the prognosis of various solid tumors. However, the association between this protein and the risk of esophagogastric junction adenocarcinoma (EGJA) remains unclear. We investigated CIP2A expression and its clinical significance in EGJA and conducted a meta-analysis to explore the relationship between CIP2A and the prognosis of patients with solid tumors.

Methods

Immunohistochemistry (IHC) was performed to detect the expression of CIP2A in EGJA. Kaplan-Meier estimation, Cox analysis, and ROC curves were performed to analyze the survival of patients and the prognostic factors. In the meta-analysis, we searched relevant publications in several widely used databases and used 15 studies (2348 patients).

Results

IHC demonstrated that CIP2A was elevated in EGJA and correlated with poor survival as an independent indicator. It could forecast the survival more precisely when combined with the grade, which is another independent prognosis marker of EGJA. Meta-analysis demonstrated that the associations between the expression of CIP2A and the prognosis were detected for overall survival (HR = 1.98, 95%CI = 1.69‐2.32), disease-specific survival (HR = 1.72, 95%CI = 1.50‐1.97), and time to tumor progression (pooled HR = 1.95, 95%CI = 1.56‐2.43).

Conclusion

High expression of CIP2A was a poor indicator of the prognosis of EGJA, and CIP2A may be a new biomarker for the diagnosis and treatment of EGJA. The meta-analysis suggested that CIP2A expression can be a predictive marker of overall survival, disease-specific survival, and time to tumor progression in patients with solid tumors.

Protein interaction disruption in cancer

Background

Most methods that integrate network and mutation data to study cancer focus on the effects of genes/proteins, quantifying the effect of mutations or differential expression of a gene and its neighbors, or identifying groups of genes that are significantly up- or down-regulated. However, several mutations are known to disrupt specific protein-protein interactions, and network dynamics are often ignored by such methods. Here we introduce a method that allows for predicting the disruption of specific interactions in cancer patients using somatic mutation data and protein interaction networks.

Methods

We extend standard network smoothing techniques to assign scores to the edges in a protein interaction network in addition to nodes. We use somatic mutations as input to our modified network smoothing method, producing scores that quantify the proximity of each edge to somatic mutations in individual samples.

Results

Using breast cancer mutation data, we show that predicted edges are significantly associated with patient survival and known ligand binding site mutations. In-silico analysis of protein binding further supports the ability of the method to infer novel disrupted interactions and provides a mechanistic explanation for the impact of mutations on key pathways.

Conclusions

Our results show the utility of our method both in identifying disruptions of protein interactions from known ligand binding site mutations, and in selecting novel clinically significant interactions.Supporting website with software and data: https://www.cs.cmu.edu/~mruffalo/mut-edge-disrupt/.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5532-5) contains supplementary material, which is available to authorized users.

ReCirc: prediction of circRNA expression and function through probe reannotation of non-circRNA microarrays

Growing evidence shows that circular RNAs (circRNAs) play important roles in physiological and pathological processes, but our knowledge about the function of circRNAs in diseases is still limited. CircRNA functions are closely related to their expression levels. We developed a probe reannotating program named ReCirc, which is based on sequence alignment between microarray probes and circRNAs, to reannotate circRNAs from non-circRNA microarrays (any microarray that was not designed to profile circRNAs) with microarray probe sequences that were aligned to the body and back-spliced junction sequences of circRNAs to identify circRNAs. Through ReCirc, we obtained 39 818 reannotated probe set-circRNA pairs, which involved 5388 circRNAs, from an Affymetrix human exon array. We evaluated our method by comparing circRNAs obtained by us with golden standard RNase R-resistant (RNase R+) circRNAs, predicted by an RNA-seq-based method find_circ, in the HeLa cell line. The results showed that ReCirc circRNAs, especially those with higher expression level, were partially present in RNase R+ data. In addition to RNA-seq, a circRNA microarray, such as the Agilent-069978 Arraystar Human CircRNA microarray, was also applied to predict and profile circRNAs. Thus, we compared the circRNA profile obtained from ReCirc with that from the circRNA microarray. The results showed that circRNA expression is similar between ReCirc and circRNA microarray in samples from the same tissue. We also evaluated ReCirc, by comparing ReCirc with the find_circ program, in their abilities to compute circRNA expression variation in multiple cell lines and performed molecular verification in the HeLaS3 cell line for those circRNAs that got good performance. As a result, 5 of the 9 randomly selected circRNAs were successfully verified. Functional analysis of identified circRNAs in 4 different cancers indicated that circRNAs may be crucial biomarkers for cancer diagnosis and prognosis. Thus, ReCirc allows us to identify circRNAs from any non-circRNA microarray, and to back-annotate old microarray data from public data sets, which would facilitate re-utilization of the wealth of microarray data sets, to enable the characterization of circRNAs in tissues and cell lines. Here we state that our method is designed only for microarrays and cannot be used for RNAseq data.

Identification of an eight-gene signature for survival prediction for patients with hepatocellular carcinoma based on integrated bioinformatics analysis

Background

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related death worldwide. Despite recent advances in imaging techniques and therapeutic intervention for HCC, the low overall 5-year survival rate of HCC patients remains unsatisfactory. This study aims to find a gene signature to predict clinical outcomes in HCC.

Methods

Bioinformatics analysis including Cox’s regression analysis, Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analysis and the random survival forest algorithm were performed to mine the expression profiles of 553 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public database.

Results

We selected a signature comprising eight protein-coding genes (DCAF13, FAM163A, GPR18, LRP10, PVRIG, S100A9, SGCB, and TNNI3K) in the training dataset (AUC = 0.77 at five years, n = 332). The signature stratified patients into high- and low-risk groups with significantly different survival in the training dataset (median 2.20 vs. 8.93 years, log-rank test P < 0.001) and in the test dataset (median 2.68 vs. 4.24 years, log-rank test P = 0.004, n = 221, GSE14520). Further multivariate Cox regression analysis showed that the signature was an independent prognostic factor for patients with HCC. Compared with TNM stage and another reported three-gene model, the signature displayed improved survival prediction power in entire dataset (AUC signature = 0.66 vs. AUC TNM = 0.64 vs. AUC gene model = 0.60, n = 553). Stratification analysis shows that it can be used as an auxiliary marker for many traditional staging models.

Conclusions

We constructed an eight-gene signature that can be a novel prognostic marker to predict the survival of HCC patients.

A Novel Clinical Six-Flavoprotein-Gene Signature Predicts Prognosis in Esophageal Squamous Cell Carcinoma

Flavoproteins and their interacting proteins play important roles in mitochondrial electron transport, fatty acid degradation, and redox regulation. However, their clinical significance and function in esophageal squamous cell carcinoma (ESCC) are little known. Here, using survival analysis and machine learning, we mined 179 patient expression profiles with ESCC in GSE53625 from the Gene Expression Omnibus (GEO) database and constructed a signature consisting of two flavoprotein genes (GPD2 and PYROXD2) and four flavoprotein interacting protein genes (CTTN, GGH, SRC, and SYNJ2BP). Kaplan-Meier analysis revealed the signature was significantly associated with the survival of ESCC patients (mean survival time: 26.77 months in the high-risk group vs. 54.97 months in the low-risk group, P < 0.001, n = 179), and time-dependent ROC analysis demonstrated that the six-gene signature had good predictive ability for six-year survival for ESCC (AUC = 0.86, 95% CI: 0.81-0.90). We then validated its prediction performance in an independent set by RT-PCR (mean survival: 15.73 months in the high-risk group vs. 21.1 months in the low-risk group, P=0.032, n = 121). Furthermore, RNAi-mediated knockdown of genes in the flavoprotein signature led to decreased proliferation and migration of ESCC cells. Taken together, CTTN, GGH, GPD2, PYROXD2, SRC, and SYNJ2BP have an important clinical significance for prognosis of ESCC patients, suggesting they are efficient prognostic markers and potential targets for ESCC therapy.

A six-mRNA prognostic model to predict survival in head and neck squamous cell carcinoma

BACKGROUND

Transcriptional dysregulation is one of the most important features of cancer genesis and progression. Applying gene expression dysregulation information to predict the development of cancers is useful for cancer diagnosis. However, previous studies mainly focused on the relationship between a single gene and cancer. Prognostic prediction using combined gene models remains limited.

MATERIALS AND METHODS

Gene expression profiles were downloaded from The Cancer Genome Atlas and the data sets were randomly divided into training data sets and test data sets. A six-gene signature associated with head and neck squamous cell carcinoma (HNSCC) and overall survival (OS) was identified according to a training cohort by using weighted gene correlation network analysis and least absolute shrinkage and selection operator Cox regression. The test data set and gene expression omnibus (GEO) data set were used to validate this signature.

RESULTS

We identified six candidate genes, namely, FOXL2NB, PCOLCE2, SPINK6, ULBP2, KCNJ18, and RFPL1, and, using a six-gene model, predicted the risk of death of head and neck squamous cell carcinoma in The Cancer Genome Atlas. At a selected cutoff, patients were clustered into low- and high-risk groups. The OS curves of the two groups of patients had significant differences, and the time-dependent receiver operating characteristics of OS, disease-specific survival (DSS), and progression-free survival (PFS) were as high as 0.766, 0.731, and 0.623, respectively. Then, the test data set and the GEO data set were used to evaluate our model, and we found that the OS time in the high-risk group was significantly shorter than in the low-risk group in both data sets, and the receiver operating characteristics of test data set were 0.669, 0.675, and 0.614, respectively. Furthermore, univariate and multivariate Cox regression analyses showed that the risk score was independent of clinicopathological features.

CONCLUSION

The six-gene model could predict the OS of HNSCC patients and improve therapeutic decision-making.

Integrative analysis of differential genes and identification of a "2-gene score" associated with survival in esophageal squamous cell carcinoma

Background

Developments in high‐throughput genomic technologies have led to improved understanding of the molecular underpinnings of esophageal squamous cell carcinoma (ESCC). However, there is currently no model that combines the clinical features and gene expression signatures to predict outcomes.

Methods

We obtained data from the GSE53625 database of Chinese ESCC patients who had undergone surgical treatment. The R packages, Limma and WGCNA, were used to identify and construct a co‐expression network of differentially expressed genes, respectively. The Cox regression model was used, and a nomogram prediction model was constructed.

Results

A total of 3654 differentially expressed genes were identified. Bioinformatics enrichment analysis was conducted. Multivariate analysis of the clinical cohort revealed that age and adjuvant therapy were independent factors for survival, and these were entered into the clinical nomogram. After integrating the gene expression profiles, we identified a “2‐gene score” associated with overall survival. The combinational model is composed of clinical data and gene expression profiles. The C‐index of the combined nomogram for predicting survival was statistically higher than the clinical nomogram. The calibration curve revealed that the combined nomogram and actual observation showed better prediction accuracy than the clinical nomogram alone.

Conclusions

The integration of gene expression signatures and clinical variables produced a predictive model for ESCC that performed better than those based exclusively on clinical variables. This approach may provide a novel prediction model for ESCC patients after surgery.

A two‑circRNA signature predicts tumour recurrence in clinical non‑functioning pituitary adenoma

Clinical non‑functioning pituitary adenoma (NFPA) accounts for >30% of all pituitary adenomas, and the recurrence rate is notably high. The ability to predict tumour recurrence during initial surgery will aid in determining if adjunctive therapy is required to reduce recurrence. With the aim of developing a circular RNA (circRNA) signature to improve prognosis prediction in NFPA, the present study examined the circRNA expression profiles in 73 patients with NFPA from Beijing Tiantan Hospital using high‑throughput RNA chip technology. The dataset was randomly separated into a training group and a test group using an R program. In the training group (n=37), a Cox proportional hazards regression model was used to analyse the genes associated with the recurrence and progression‑free survival (PFS) of patients with NFPA. Meanwhile, a random survival forest algorithm, Kaplan‑Meier and receiver operating characteristic curve (ROC) analyses were used to determine the multi‑circRNA signature with the largest area under the ROC curve (AUROC) and verify its efficacy in the test group (n=36). In the training and test groups, the signatures of two circRNAs (hsa_circ_0000066 and hsa_circ_0069707) were specifically associated with the PFS of patients with NFPA (log‑rank P<0.05). Furthermore, the two‑circRNA signature had a high prediction accuracy for tumour recurrence, with an AUROC of 0.87 and 0.67 in the training and test groups, respectively; and the discriminative power of the signature was greater compared with that of age. The present study is the first to suggest a circRNA signature with a clinical application value for predicting recurrence/progression in patients with NFPA.

Expression of UCA1 and MALAT1 long-chain non-coding RNAs in esophageal squamous cell carcinoma tissues is predictive of patient prognosis

INTRODUCTION

The long non-coding RNAs (lncRNAs) urothelial cancer associated 1 (UCA1) and metastasis associated lung adenocarcinoma transcript 1 (MALAT1) are known to impact cancer cell regulation. The aim of the present study was to determine the relationship between the expression of these lncRNAs in esophageal squamous cell carcinoma (ESCC) tissues and disease prognosis.

MATERIAL AND METHODS

The expression of UCA1 and MALAT1 lncRNAs was assessed in ESCC and adjacent carcinoma tissues (5 cm away from the tumor) and evaluated in relation to overall survival (OS) and disease-free survival (DFS) of patients. This prospective study included 100 ESCC patients who were admitted to the First Hospital of Yulin City between January 2007 and January 2014.

RESULTS

The expression levels of UCA1 and MALAT1 lncRNAs in ESCC tissues were significantly higher than those in adjacent carcinoma tissues, and there were statistically significant differences in TNM staging between the patients with high lncRNA expression and low lncRNA expression. The OS and DFS of patients with high UCA1 and MALAT1 lncRNA expression levels were significantly shorter than those with low expression levels. Furthermore, the OS and DFS of ESCC patients appeared to be correlated with TNM staging.

CONCLUSIONS

These results imply that the up-regulation of UCA1 and MALAT1 lncRNAs in ESCC tissues can impact the degree of tumor progression and is predictive of postoperative survival. Therefore, the expression levels of these lncRNAs can be used as measurement indexes to determine the prognosis of ESCC patients.

Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal carcinoma in China. This study was to develop a staging model to predict outcomes of patients with ESCC.

METHODS

Using Cox regression analysis, principal component analysis (PCA), partitioning clustering, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, and classification and regression tree (CART) analysis, we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 dataset.

RESULTS

Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes (PCGs) and 635 long non-coding RNAs (lncRNAs) were associated with the survival of patients with ESCC. PCA categorized these PCGs and lncRNAs into three principal components (PCs), which were used to cluster the patients into three groups. ROC analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging (area under ROC curve [AUC]: 0.69 vs. 0.65, P < 0.05). Accordingly, we constructed a molecular disaggregated model comprising one lncRNA and two PCGs, which we designated as the LSB staging model using CART analysis in the GSE63624 dataset. This LSB staging model classified the GSE63622 dataset of patients into three different groups, and its effectiveness was validated by analysis of another cohort of 105 patients.

CONCLUSIONS

The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three-gene staging microarray.

A three-lncRNA signature predicts overall survival and disease-free survival in patients with esophageal squamous cell carcinoma

BACKGROUND

Increasing evidence shows that dysregulated long non-coding RNAs (lncRNAs) can serve as potential biomarkers for cancer prognosis. However, lncRNA signatures, as potential prognostic biomarkers for esophageal squamous cell carcinoma (ESCC), have been seldom reported.

METHODS

Based on our previous transcriptome RNA sequencing analysis from 15 paired ESCC tissues and adjacent normal tissues, we selected 10 lncRNAs with high score rank and characterized the expression of those lncRNAs, by qRT-PCR, in 138 ESCC and paired adjacent normal samples. These 138 patients were divided randomly into training (n = 77) and test (n = 59) groups. A prognostic signature of lncRNAs was identified in the training group and validated in the test group and in an independent cohort (n = 119). Multivariable Cox regression analysis evaluated the independence of the signature in overall survival (OS) and disease-free survival (DFS) prediction. GO and KEGG pathway analysis, combined with cell transwell and proliferation assays, are applied to explore the function of the three lncRNAs.

RESULTS

A novel three-lncRNA signature, comprised of RP11-366H4.1.1 (ENSG00000248370), LINC00460 (ENSG00000233532) and AC093850.2 (ENSG00000230838), was identified. The signature classified patients into high-risk and low-risk groups with different overall survival (OS) and disease-free survival (DFS). For the training group, median OS: 23.1 months vs. 39.1 months, P < 0.001; median DFS: 15.2 months vs. 33.3 months, P < 0.001. For the test group, median OS: 23 months vs. 59 months, P < 0.001; median DFS: 16.4 months vs. 50.8 months, P < 0.001. For the independent cohort, median OS: 22.4 months vs. 60.4 months, P < 0.001). The signature indicates that patients in the high-risk group show poor OS and DFS, whereas patients with a low-risk group show significantly better outcome. The independence of the signature was validated by multivariable Cox regression analysis. GO and KEGG pathway analysis for 588 protein-coding genes-associated with the three lncRNAs indicated that the three lncRNAs were involved in tumorigenesis. In vitro assays further demonstrated that the three lncRNAs promoted the migration and proliferation of ESCC cells.

CONCLUSIONS

The three-lncRNA signature is a novel and potential predictor of OS and DFS for patients with ESCC.

Long non‑coding RNA regulates hair follicle stem cell proliferation and differentiation through PI3K/AKT signal pathway

Long non-coding RNAs (lncRNAs) are defined as non-coding transcripts (>200 nucleotides) that serve important roles in the proliferation and differentiation of stem cells. Hair follicle stem cells (HFTs) have multidirectional differentiation potential and are able to differentiate into skin, hair follicles and sebaceous glands, serving a role in skin wound healing. The aim of the present study was to analyze the regulatory role of lncRNA AK015322 (IncRNA5322) in HFTs and the potential mechanism of IncRNA5322‑mediated differentiation of HFTs. The results demonstrated that lncRNA5322 transfection promoted proliferation and differentiation in HFTs. It was identified that lncRNA5322 transfection upregulated the expression and phosphorylation of phosphoinositide 3‑kinase (PI3K) and protein kinase B (AKT) in HFTs. It was also observed that lncRNA5322 transfection upregulated microRNA (miR)‑21 and miR‑21 agonist (agomir‑21) eliminated lncRNA5322‑induced expression and phosphorylation of PI3K and AKT. The present study also demonstrated that agomir‑21 blocked IncRNA5322‑induced expression and phosphorylation of PI3K and AKT in HFTs. The results indicated that agomir‑21 transfection also suppressed the IncRNA5322‑induced proliferation and differentiation of HFTs. In conclusion, the results of the present study suggest that lncRNA5322 is able to promote the proliferation and differentiation of HFTs by targeting the miR‑21‑mediated PI3K‑AKT signaling pathway in HFTs.

Protein-coding genes, long non-coding RNAs combined with microRNAs as a novel clinical multi-dimension transcriptome signature to predict prognosis in ovarian cancer

Ovarian cancer is prevalent in women which is usually diagnosed at an advanced stage with a high mortality rate. The aim of this study is to investigate protein-coding gene, long non-coding RNA, and microRNA associated with the prognosis of patients with ovarian serous carcinoma by mining data from TCGA (The Cancer Genome Atlas) public database. The clinical data of ovarian serous carcinoma patients was downloaded from TCGA database in September, 2016. The mean age and survival time of 407 patients with ovarian serous carcinoma were 59.71 ± 11.54 years and 32.98 ± 26.66 months. Cox's proportional hazards regression analysis was conducted to analyze genes that were significantly associated with the survival of ovarian serous carcinoma patients in the training group. Using the random survival forest algorithm, Kaplan-Meier and ROC analysis, we kept prognostic genes to construct the multi-dimensional transcriptome signature with max area under ROC curve (AUC) (0.69 in the training group and 0.62 in the test group). The selected signature composed by VAT1L, CALR, LINC01456, RP11-484L8.1, MIR196A1 and MIR148A, separated the training group patients into high-risk or low-risk subgroup with significantly different survival time (median survival: 35.3 months vs. 64.9 months, P < 0.001). The signature was validated in the test group showing similar prognostic values (median survival: 41.6 months in high-risk vs. 57.4 months in low-risk group, P=0.018). Chi-square test and multivariable Cox regression analysis showed that the signature was an independent prognostic factor for patients with ovarian serous carcinoma. Finally, we validated the expression of the genes experimentally.