An Integrated Three-Long Non-coding RNA Signature Predicts Prognosis in Colorectal Cancer Patients

LINC01852 inhibits the tumorigenesis and chemoresistance in colorectal cancer by suppressing SRSF5-mediated alternative splicing of PKM

BACKGROUND

Colorectal cancer (CRC) is a major cause of cancer-related deaths worldwide, and chemoresistance is a major obstacle in its treatment. Despite advances in therapy, the molecular mechanism underlying chemoresistance in CRC is not fully understood. Recent studies have implicated the key roles of long noncoding RNAs (lncRNAs) in the regulation of CRC chemoresistance.

METHODS

In this study, we investigated the role of the lncRNA LINC01852 in CRC chemoresistance. LINC01852 expression was evaluated in multiple CRC cohorts using quantitative reverse transcription PCR. We conducted in vitro and in vivo functional experiments using cell culture and mouse models. RNA pull-down, RNA immunoprecipitation, chromatin immunoprecipitation, and dual luciferase assays were used to investigate the molecular mechanism of LINC01852 in CRC.

RESULTS

Our findings revealed that a lncRNA with tumor-inhibiting properties, LINC01852, was downregulated in CRC and inhibited cell proliferation and chemoresistance both in vitro and in vivo. Further mechanistic investigations revealed that LINC01852 increases TRIM72-mediated ubiquitination and degradation of SRSF5, inhibiting SRSF5-mediated alternative splicing of PKM and thereby decreasing the production of PKM2. Overexpression of LINC01852 induces a metabolic switch from aerobic glycolysis to oxidative phosphorylation, which attenuates the chemoresistance of CRC cells by inhibiting PKM2-mediated glycolysis.

CONCLUSIONS

Our results demonstrate that LINC01852 plays an important role in repressing CRC malignancy and chemoresistance by regulating SRSF5-mediated alternative splicing of PKM, and that targeting the LINC01852/TRIM72/SRSF5/PKM2 signaling axis may represent a potential therapeutic strategy for CRC.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Long noncoding RNA DLGAP1-AS2 promotes tumorigenesis and metastasis by regulating the Trim21/ELOA/LHPP axis in colorectal cancer

BACKGROUND

Long noncoding RNAs (lncRNAs) have driven research focused on their effects as oncogenes or tumor suppressors involved in carcinogenesis. However, the functions and mechanisms of most lncRNAs in colorectal cancer (CRC) remain unclear.

METHODS

The expression of DLGAP1-AS2 was assessed by quantitative RT-PCR in multiple CRC cohorts. The impacts of DLGAP1-AS2 on CRC growth and metastasis were evaluated by a series of in vitro and in vivo assays. Furthermore, the underlying mechanism of DLGAP1-AS2 in CRC was revealed by RNA pull down, RNA immunoprecipitation, RNA sequencing, luciferase assays, chromatin immunoprecipitation, and rescue experiments.

RESULTS

We discovered that DLGAP1-AS2 promoted CRC tumorigenesis and metastasis by physically interacting with Elongin A (ELOA) and inhibiting its protein stability by promoting tripartite motif containing 21 (Trim21)-mediated ubiquitination modification and degradation of ELOA. In particular, we revealed that DLGAP1-AS2 decreases phospholysine phosphohistidine inorganic pyrophosphate phosphatase (LHPP) expression by inhibiting ELOA-mediated transcriptional activating of LHPP and thus blocking LHPP-dependent suppression of the AKT signaling pathway. In addition, we also demonstrated that DLGAP1-AS2 was bound and stabilized by cleavage and polyadenylation specificity factor (CPSF2) and cleavage stimulation factor (CSTF3).

CONCLUSIONS

The discovery of DLGAP1-AS2, a promising prognostic biomarker, reveals a new dimension into the molecular pathogenesis of CRC and provides a prospective treatment target for this disease.

The ferroptosis-related long non-coding RNAs signature predicts biochemical recurrence and immune cell infiltration in prostate cancer

BACKGROUND

Findings from numerous studies have revealed that ferroptosis is closely related to tumorigenesis and immune cell infiltration. Long non-coding RNAs (lncRNAs) are reportedly involved in the progression of various cancers, including prostate cancer (PCa). This study was designed to establish a ferroptosis-related lncRNA (frlncRNA) signature to predict PCa prognosis.

METHODS

The frlncRNAs were identified by studying their expression by Pearson's correlation analysis. Differentially expressed prognosis related frlncRNAs were identified by the Wilcoxon test and univariate Cox regression analysis. The LASSO Cox regression model was used to build a model to predict biochemical recurrence (BCR) based on frlncRNAs. The GSEA software (version 4.1.0) was used to explore the enriched pathways in high- and low- risk groups. Patients with PCa were clustered into different subgroups by unsupervised clustering based on the frlncRNAs considered in the prognostic model. Real-time PCR and CCK8 assays were performed to verify the expression and function of frlncRNAs.

RESULTS

We identified 35 differentially expressed prognosis related frlncRNAs based on data on PCa from TCGA. A risk signature based on five frlncRNAs (AP006284.1, AC132938.1, BCRP3, AL360181.4 and AL135999.1), was confirmed to perform well in predicting BCR. The high-risk group had higher disease grades and a greater number of infiltrating immune cells. Besides this, we found that the five frlncRNAs were connected with typical immune checkpoints. With respect to molecular mechanisms, several metabolic pathways were found to enriched in the low-risk group. Furthermore, patients could be classified into different subtypes with different PSA-free times using the five frlncRNAs. Notably, AP006284.1, AC132938.1, BCRP3 and AL135999.1 were upregulated in PCa cells and tissues, whereas AL360181.4 exhibited the opposite trend. The downregulation of BCRP3 and AP006284.1 impaired the proliferation of 22RV1 cells.

CONCLUSION

We generated a prognostic model based on five frlncRNAs, with clinical usefulness, and thus provided a novel strategy for predicting the BCR of patients with PCa.

Transcriptome profiling and co-expression network analysis of lncRNAs and mRNAs in colorectal cancer by RNA sequencing

Background

Long non-coding RNAs (lncRNAs) are widely involved in the pathogenesis of cancers. However, biological roles of lncRNAs in occurrence and progression of colorectal cancer (CRC) remain unclear. The current study aimed to evaluate the expression pattern of lncRNAs and messenger RNAs (mRNAs).

Methods

RNA sequencing (RNA-Seq) in CRC tissues and adjacent normal tissues from 6 CRC patients was performed and functional lncRNA-mRNA co-expression network was constructed afterwards. Gene enrichment analysis was demonstrated using DAVID 6.8 tool. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was used to validate the expression pattern of differentially expressed lncRNAs. Pearson correlation analysis was applied to evaluate the relationships between selected lncRNAs and mRNAs.

Results

One thousand seven hundred and sixteenth differentially expressed mRNAs and 311 differentially expressed lncRNAs were screened out. Among these, 568 mRNAs were up-regulated while 1148 mRNAs down-regulated, similarly 125 lncRNAs were up-regulated and 186 lncRNAs down-regulated. In addition, 1448 lncRNA–mRNA co-expression pairs were screened out from 940,905 candidate lncRNA-mRNA pairs. Gene enrichment analysis revealed that these lncRNA-related mRNAs are associated with cell adhesion, collagen adhesion, cell differentiation, and mainly enriched in ECM-receptor interaction and PI3K-Akt signaling pathways. Finally, RT-qPCR results verified the expression pattern of lncRNAs, as well as the relationships between lncRNAs and mRNAs in 60 pairs of CRC tissues.

Conclusions

In conclusion, these results of the RNA-seq and bioinformatic analysis strongly suggested that the dysregulation of lncRNA is involved in the complicated process of CRC development, and providing important insight regarding the lncRNAs involved in CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09878-6.

GANAB and N-Glycans Substrates Are Relevant in Human Physiology, Polycystic Pathology and Multiple Sclerosis: A Review

Glycans are one of the four fundamental macromolecular components of living matter, and they are highly regulated in the cell. Their functions are metabolic, structural and modulatory. In particular, ER resident N-glycans participate with the Glc3Man9GlcNAc2 highly conserved sequence, in protein folding process, where the physiological balance between glycosylation/deglycosylation on the innermost glucose residue takes place, according GANAB/UGGT concentration ratio. However, under abnormal conditions, the cell adapts to the glucose availability by adopting an aerobic or anaerobic regimen of glycolysis, or to external stimuli through internal or external recognition patterns, so it responds to pathogenic noxa with unfolded protein response (UPR). UPR can affect Multiple Sclerosis (MS) and several neurological and metabolic diseases via the BiP stress sensor, resulting in ATF6, PERK and IRE1 activation. Furthermore, the abnormal GANAB expression has been observed in MS, systemic lupus erythematous, male germinal epithelium and predisposed highly replicating cells of the kidney tubules and bile ducts. The latter is the case of Polycystic Liver Disease (PCLD) and Polycystic Kidney Disease (PCKD), where genetically induced GANAB loss affects polycystin-1 (PC1) and polycystin-2 (PC2), resulting in altered protein quality control and cyst formation phenomenon. Our topics resume the role of glycans in cell physiology, highlighting the N-glycans one, as a substrate of GANAB, which is an emerging key molecule in MS and other human pathologies.

Ferroptosis-Related Long Noncoding RNAs as Prognostic Marker for Colon Adenocarcinoma

Background

The incidence of colon adenocarcinoma (COAD) has been increasing over time. Although ferroptosis and long noncoding RNAs (lncRNAs) have been extensively reported to participate in the tumorigenesis and development of COAD, few studies have investigated the role of ferroptosis-related lncRNAs in the prognosis of COAD.

Methods

Gene-sequencing and clinical data for COAD were obtained from The Cancer Genome Atlas database. The coexpression network was constructed using known ferroptosis-related genes. Cox and least absolute shrinkage and selection operator regression were used to screen ferroptosis-related lncRNAs with prognostic value and to identify a predictive model of COAD. Patients with COAD were divided into low- and high-risk groups according to their risk score. Cases of COAD in the International Cancer Genome Consortium database were included as the testing cohort.

Results

In total, nine lncRNAs (LINC02381, AC105219.1, AC009283.1, LINC01011, ELFN1-AS1, EIF3J-DT, NKILA, LINC01063, and SNHG16) were considered prognostic factors for COAD. Then, a risk score model was established. The overall survival rate of COAD patients was negatively associated with the risk score. Kaplan-Meier analyses in the original and testing cohorts showed similar results. The expression of the lncRNAs in tissue was consistent with the risk score, and the relationship with tumor mutation burden, immunity, and drug sensitivity presented a marked link between the signature and COAD. A nomogram was established for clinical applications.

Conclusions

Nine ferroptosis-related lncRNAs and the established signature have a certain predictive value for prognosis of COAD patients and can be used as potential research targets for exploring treatment of COAD.

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.

Prediction of Clinical Outcome in Endometrial Carcinoma Based on a 3-lncRNA Signature

Endometrial carcinoma (EC) is one of the common gynecological cancers with increasing incidence and revived mortality recently. Given the heterogeneity of tumors and the complexity of lncRNAs, a panel of lncRNA biomarkers might be more precise and stable for prognosis. In the present study, we developed a new lncRNA model to predict the prognosis of patients with EC. EC-associated differentially expressed long noncoding RNAs (lncRNAs) were identified from The Cancer Genome Atlas (TCGA). Univariate COX regression and least absolute shrinkage and selection operator (LASSO) model were selected to find the 8-independent prognostic lncRNAs of EC patient. Furthermore, the risk score of the 3-lncRNA signature for overall survival (OS) was identified as CTD-2377D24.6 expression × 0.206 + RP4-616B8.5 × 0.341 + RP11-389G6.3 × 0.343 by multivariate Cox regression analysis. According to the median cutoff value of this prognostic signature, the EC samples were divided into two groups, high-risk set (3-lncRNAs at high levels) and low-risk set (3-lncRNAs at low levels), and the Kaplan–Meier survival curves demonstrated that the low-risk set had a higher survival rate than the high-risk set. In addition, the 3-lncRNA signature was closely linked with histological subtype (p = 0.0001), advanced clinical stage (p = 0.011), and clinical grade (p < 0.0001) in EC patients. Our clinical samples also confirmed that RP4-616B8.5, RP11-389G6.3, and CTD-2377D24.6 levels were increased in tumor tissues by qRT-PCR and in situ hybridization. Intriguingly, the p-value of combined 3-lncRNAs was lower than that of each lncRNA, indicating that the 3-lncRNA signature also showed higher performance in EC tissue than paracancerous. Functional analysis revealed that cortactin might be involved in the mechanism of 3-lncRNA signatures. These findings provide the first hint that a panel of lncRNAs may play a critical role in the initiation and metastasis of EC, indicating a new signature for early diagnosis and therapeutic strategy of uterine corpus endometrial carcinoma.

SLCO4A1-AS1 promotes colorectal tumourigenesis by regulating Cdk2/c-Myc signalling

BACKGROUND

SLCO4A1-AS1 was found to be upregulated in several cancer types, including colorectal cancer (CRC). However, the detailed roles of SLCO4A1-AS1 in CRC remain to be elucidated. Therefore, we investigated the functions, mechanism, and clinical significance of SLCO4A1-AS1 in colorectal tumourigenesis.

METHODS

We measured the expression of SLCO4A1-AS1 in CRC tissues using qRT-PCR and determined its correlation with patient prognosis. Promoter methylation analyses were used to assess the methylation status of SLCO4A1-AS1. Gain- and loss-of-function assays were used to evaluate the effects of SLCO4A1-AS1 on CRC growth in vitro and in vivo. RNA pull-down, RNA immunoprecipitation, RNA-seq, luciferase reporter and immunohistochemistry assays were performed to identify the molecular mechanism of SLCO4A1-AS1 in CRC.

RESULTS

SLCO4A1-AS1 was frequently upregulated in CRC tissues based on multiple CRC cohorts and was associated with poor prognoses. Aberrant overexpression of SLCO4A1-AS1 in CRC is partly attributed to the DNA hypomethylation of its promoter. Ectopic SLCO4A1-AS1 expression promoted CRC cell growth, whereas SLCO4A1-AS1 knockdown repressed CRC proliferation both in vitro and in vivo. Mechanistic investigations revealed that SLCO4A1-AS1 functions as a molecular scaffold to strengthen the interaction between Hsp90 and Cdk2, promoting the protein stability of Cdk2. The SLCO4A1-AS1-induced increase in Cdk2 levels activates the c-Myc signalling pathway by promoting the phosphorylation of c-Myc at Ser62, resulting in increased tumour growth.

CONCLUSIONS

Our data demonstrate that SLCO4A1-AS1 acts as an oncogene in CRC by regulating the Hsp90/Cdk2/c-Myc axis, supporting SLCO4A1-AS1 as a potential therapeutic target and prognostic factor for CRC.

Six mutator-derived lncRNA signature of genome instability for predicting the clinical outcome of colon cancer

Background

Colon adenocarcinoma (COAD) is one of the most common malignancies worldwide. Genomic instability is one of the hallmarks of colon cancer and is associated with prognosis. Nevertheless, the impact of genome instability-associated long non-coding RNAs (lncRNAs) along with their clinical significance in cancers has remained mostly unexplored.

Methods

In this study, a mutator hypothesis-derived computational frame integrating the somatic mutation profiles and lncRNA expression profiles in a tumor genome was developed, which enabled the identification of 137 novel genomic instability-associated lncRNAs in colon cancer. Subsequently, a genome instability-derived lncRNA signature (GILncSig) segregated the patients into low- and high-risk groups with prominent differences in outcomes.

Results

Combined with the overall survival data, we established 6 six lncRNA-based signature to predict prognosis, which were LINC00896, AC007996.1, NKILA, AP003555.2, MIRLET7BHG, and AC009237.14. We found that the expression level of PD-L1 (CD274) and somatic mutations in the high-risk group were higher than those in the low-risk group. This suggests that high-risk patients may be sensitive to immunotherapy. We further found that the prognosis of patients in the high-risk group was significantly lower than that of patients in the low-risk group, and that patients' prognosis was likely to be worse as the patient's risk score increased.

Conclusions

In conclusion, this study explores the role of lncRNAs in genomic instability and cancer prognosis and provides a new idea for the prognostic prediction of colon cancer.

SNHG17 promotes colorectal tumorigenesis and metastasis via regulating Trim23-PES1 axis and miR-339-5p-FOSL2-SNHG17 positive feedback loop

BACKGROUND

Small nucleolar RNA host gene (SNHG) long noncoding RNAs (lncRNAs) are frequently dysregulated in human cancers and involved in tumorigenesis and progression. SNHG17 has been reported as a candidate oncogene in several cancer types, however, its regulatory role in colorectal cancer (CRC) is unclear.

METHODS

SNHG17 expression in multiple CRC cohorts was assessed by RT-qPCR or bioinformatic analyses. Cell viability was evaluated using Cell Counting Kit-8 (CCK-8) and colony formation assays. Cell mobility and invasiveness were assessed by Transwell assays. Tumor xenograft and metastasis models were applied to confirm the effects of SNHG17 on CRC tumorigenesis and metastasis in vivo. Immunohistochemistry staining was used to measure protein expression in cancer tissues. RNA pull-down, RNA immunoprecipitation, chromatin immunoprecipitation, and dual luciferase assays were used to investigate the molecular mechanism of SNHG17 in CRC.

RESULTS

Using multiple cohorts, we confirmed that SNHG17 is aberrantly upregulated in CRC and correlated with poor survival. In vitro and in vivo functional assays indicated that SNHG17 facilitates CRC proliferation and metastasis. SNHG17 impedes PES1 degradation by inhibiting Trim23-mediated ubiquitination of PES1. SNHG17 upregulates FOSL2 by sponging miR-339-5p, and FOSL2 transcription activates SNHG17 expression, uncovering a SNHG17-miR-339-5p-FOSL2-SNHG17 positive feedback loop.

CONCLUSIONS

We identified SNHG17 as an oncogenic lncRNA in CRC and identified abnormal upregulation of SNHG17 as a prognostic risk factor for CRC. Our mechanistic investigations demonstrated, for the first time, that SNHG17 promotes tumor growth and metastasis through two different regulatory mechanisms, SNHG17-Trim23-PES1 axis and SNHG17-miR-339-5p-FOSL2-SNHG17 positive feedback loop, which may be exploited for CRC therapy.

A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs

Introduction

Colorectal cancer (CRC) is the most common gastrointestinal cancer and has a low overall survival rate. Tumor–node–metastasis staging alone is insufficient to predict patient prognosis. Autophagy and long noncoding RNAs play important roles in regulating the biological behavior of CRC. Therefore, establishing an autophagy-related lncRNA (ARlncRNA)-based bioinformatics model is important for predicting survival and facilitating clinical treatment.

Methods

CRC data were retrieved from The Cancer Genome Atlas. The database was randomly divided into train set and validation set; then, univariate and multivariate Cox regression analyses were performed to screen prognosis-related ARlncRNAs for prediction model construction. Interactive network and Sankey diagrams of ARlncRNAs and messenger RNAs were plotted. We analyzed the survival rate of high- and low-risk patients and plotted survival curves and determined whether the risk score was an independent predictor of CRC. Receiver operating characteristic curves were used to evaluate model sensitivity and specificity. Then, the expression level of lncRNA was detected by quantitative real-time polymerase chain reaction, and the location of lncRNA was observed by fluorescence in situ hybridization. Additionally, the protein expression was detected by Western blot.

Results

A prognostic prediction model of CRC was built based on nine ARlncRNAs (NKILA, LINC00174, AC008760.1, LINC02041, PCAT6, AC156455.1, LINC01503, LINC00957, and CD27-AS1). The 5-year overall survival rate was significantly lower in the high-risk group than in the low-risk group among train set, validation set, and all patients (all p < 0.001). The model had high sensitivity and accuracy in predicting the 1-year overall survival rate (area under the curve = 0.717). The prediction model risk score was an independent predictor of CRC. LINC00174 and NKILA were expressed in the nucleus and cytoplasm of normal colonic epithelial cell line NCM460 and colorectal cancer cell lines HT29. Additionally, LINC00174 and NKILA were overexpressed in HT29 compared with NCM460. After autophagy activation, LINCC00174 expression was significantly downregulated both in NCM460 and HT29, while NKILA expression was significantly increased.

Conclusion

The new ARlncRNA-based model predicts CRC patient prognosis and provides new research ideas regarding potential mechanisms regulating the biological behavior of CRC. ARlncRNAs may play important roles in personalized cancer treatment.

Decreased Tumoral Expression of Colon-Specific Water Channel Aquaporin 8 Is Associated With Reduced Overall Survival in Colon Adenocarcinoma

BACKGROUND

Colon cancer survival is dependent on metastatic potential and treatment. Large RNA-sequencing data sets may assist in identifying colon cancer-specific biomarkers to improve patient outcomes.

OBJECTIVE

This study aimed to identify a highly specific biomarker for overall survival in colon adenocarcinoma by using an RNA-sequencing data set.

DESIGN

Raw RNA-sequencing and clinical data for patients with colon adenocarcinoma (n = 271) were downloaded from The Cancer Genome Atlas. A binomial regression model was used to calculate differential RNA expression between paired colon cancer and normal epithelium samples (n = 40). Highly differentially expressed RNAs were examined.

SETTINGS

This study was conducted at the University of Louisville using data acquired by The Cancer Genome Atlas.

PATIENTS

Patients from US accredited cancer centers between 1998 and 2013 were analyzed.

MAIN OUTCOME MEASURES

The primary outcome measures were recurrence-free and overall survival.

RESULTS

The median age was 66 years (147/271 men, 180/271 White patients). Thirty RNAs were differentially expressed in colon adenocarcinoma compared with paired normal epithelium, using a log-fold change cutoff of ±6. Using median expression as a cutoff, 4 RNAs were associated with worse overall survival: decreased ZG16 (log-rank = 0.023), aquaporin 8 (log-rank = 0.023), and SLC26A3 (log-rank = 0.098), and increased COL1A1 (log-rank = 0.105). On multivariable analysis, low aquaporin 8 expression (HR, 1.748; 95% CI, 1.016-3.008; p = 0.044) was a risk factor for worse overall survival. Our final aquaporin 8 model had an area under the curve of 0.85 for overall survival. On subgroup analysis, low aquaporin 8 was associated with worse overall survival in patients with high microsatellite instability and in patients with stage II disease. Low aquaporin 8 expression was associated with KRAS and BRAF mutations. Aquaporin 8 immunohistochemistry was optimized for clinical application.

LIMITATIONS

This was a retrospective study.

CONCLUSION

Aquaporin 8 is a water channel selectively expressed in normal colon tissue. Low aquaporin 8 expression is a risk factor for worse overall survival in patients who have colon cancer. Aquaporin 8 measurement may have a role as a colon-specific prognostic biomarker and help in patient risk stratification for increased surveillance. See Video Abstract at http://links.lww.com/DCR/B603.

LA DISMINUCIN DE LA EXPRESIN TUMORAL DE LA ACUAPORINA DEL CANAL DE AGUA ESPECFICO DEL COLON SE ASOCIA CON UNA REDUCCIN DE LA SUPERVIVENCIA GENERAL EN EL ADENOCARCINOMA DE COLON

ANTECEDENTES:La supervivencia del cáncer de colon depende del potencial metastásico y del tratamiento. Grandes conjuntos de datos de secuenciación de ARN pueden ayudar a identificar biomarcadores específicos del cáncer de colon para mejorar los resultados de los pacientes.OBJETIVO:Identificar un biomarcador altamente específico para la supervivencia general en el adenocarcinoma de colon utilizando un conjunto de datos de secuenciación de ARN.DISEÑO:La secuenciación de ARN sin procesar y los datos clínicos para pacientes con adenocarcinoma de colon (n = 271) se descargaron de The Cancer Genome Atlas. Se utilizó un modelo de regresión binomial para calcular la expresión diferencial de ARN entre muestras de cáncer de colon emparejadas y muestras de epitelio normal (n = 40). Se examinaron los ARN expresados de forma altamente diferencial.ENTORNO CLINICO:Este estudio se realizó en la Universidad de Louisville utilizando datos adquiridos por The Cancer Genome Atlas.PACIENTES:Se analizaron pacientes de centros oncológicos acreditados en Estados Unidos entre 1998-2013.PRINCIPALES MEDIDAS DE VALORACION:Las principales medidas de valoración fueron la supervivencia general y libre de recurrencia.RESULTADOS:La mediana de edad fue de 66 años (147/271 hombres, 180/271 caucásicos). Treinta ARN se expresaron diferencialmente en el adenocarcinoma de colon en comparación con el epitelio normal emparejado, utilizando un límite de cambio logarítmico de ± 6. Utilizando la expresión mediana como punto de corte, cuatro ARN se asociaron con una peor supervivencia general: disminución de ZG16 (rango logarítmico = 0,023), acuaporina8 (rango logarítmico = 0,023) y SLC26A3 (rango logarítmico = 0,098) y aumento de COL1A1 (log -rango = 0,105). En el análisis multivariable, la baja expresión de acuaporina8 (HR = 1,748, IC del 95%: 1,016-3,008, p = 0,044) fue un factor de riesgo para una peor supervivencia global. Nuestro modelo de aquaporin8 final tuvo un AUC de 0,85 para la supervivencia global. En el análisis de subgrupos, la acuaporina8 baja se asoció con una peor supervivencia general en pacientes con MSI-H y en pacientes en estadio II. La baja expresión de acuaporina8 se asoció con mutaciones de KRAS y BRAF. La inmunohistoquímica de aquaporina8 se optimizó para su aplicación clínica.LIMITACIONES:Este fue un estudio retrospectivo.CONCLUSIÓN:La acuaporina8 es un canal de agua expresado selectivamente en el tejido normal del colon. La baja expresión de AQP8 es un factor de riesgo de peor supervivencia global en pacientes con cáncer de colon. La medición de aquaporina8 puede tener un papel como un biomarcador de pronóstico específico del colon y ayudar en la estratificación del riesgo del paciente para una mayor vigilancia. Consulte Video Resumen en http://links.lww.com/DCR/B603.

Comprehensive analyses of correlation and survival reveal informative lncRNA prognostic signatures in colon cancer

BACKGROUND

Colon cancer is a commonly worldwide cancer with high morbidity and mortality. Long non-coding RNAs (lncRNAs) are involved in many biological processes and are closely related to the occurrence of colon cancer. Identification of the prognostic signatures of lncRNAs in colon cancer has great significance for its treatment.

METHODS

We first identified the colon cancer-related mRNAs and lncRNAs according to the differential analysis methods using the expression data in TCGA. Then, we performed correlation analysis between the identified mRNAs and lncRNAs by integrating their expression values and secondary structure information to estimate the co-regulatory relationships between the cancer-related mRNAs and lncRNAs. Besides, the competing endogenous RNA regulation network based on co-regulatory relationships was constructed to reveal cancer-related regulatory patterns. Meanwhile, we used traditional regression analysis (univariate Cox analysis, random survival forest analysis, and lasso regression analysis) to screen the cancer-related lncRNAs. Finally, by combining the identified colon cancer-related lncRNAs according to the above analyses, we constructed a risk prognosis model for colon cancer through multivariate Cox analysis and also validated the model in the colon cancer dataset in TCGA cohorts.

RESULTS

Six lncRNAs were found highly correlated with the overall survival of colon cancer patients, and a risk prognosis model based on them was constructed to predict the overall survival of colon cancer patients. In particular, EVX1-AS, ZNF667-AS1, CTC-428G20.6, and CTC-297N7.9 were first reported to be related to colon cancer by using our model, among which EVX1-AS and ZNF667-AS1 have been predicted to be related to colon cancer in LncRNADisease database.

CONCLUSIONS

This study identified the potential regulatory relationships between lncRNAs and mRNAs by integrating their expression values and secondary structure information and presented a significant 6-lncRNA risk prognosis model to predict the overall survival of colon cancer patients.

Long noncoding RNA MCM3AP-AS1 enhances cell proliferation and metastasis in colorectal cancer by regulating miR-193a-5p/SENP1

BACKGROUND

Accumulating evidences have shown that long noncoding RNAs (lncRNAs) play key roles in many diseases, including cancer. Several studies reported that MCM3AP antisense RNA 1 (MCM3AP-AS1) was associated with the tumorigenesis and progression. However, the specific function and mechanism of MCM3AP-AS1 in colorectal cancer (CRC) have not been fully understood.

METHODS

The expression of MCM3AP-AS1 was detected by quantitative reverse transcription PCR (RT-qPCR) in CRC tissues and matched noncancerous tissues (NCTs). CCK-8 assay, colony formation assay, transwell assay, xenograft and lung metastasis mouse models were used to examine the tumor-promoting function of MCM3AP-AS1 in vitro and in vivo. The binding relationship between MCM3AP-AS1, miR-193a-5p and sentrin-specific peptidase 1 (SENP1) were screened and identified by databases, RT-qPCR, dual luciferase reporter assay and western blot.

RESULTS

In the present study, we got that the expression of MCM3AP-AS1 was higher in CRC tissues than in paired NCTs, and increased MCM3AP-AS1 expression was associated with adverse outcomes in CRC patients. Functional experiments in vitro revealed that silencing of MCM3AP-AS1 could inhibit the proliferation, colony formation, migratory, and invasive abilities of CRC cells. The mouse models of xenograft and lung metastasis further confirmed that in vivo silencing MCM3AP-AS1 could significantly inhibit the growth and metastasis of CRC. Further mechanism studies indicated that MCM3AP-AS1 could sponge miR-193a-5p and inhibit the activity of it. What is more, SENP1 was proved to be a novel target of miR-193a-5p and could be upregulated by MCM3AP-AS1. At last, we observed that SENP1 overexpression in CRC tissues was closely related to unfavorable prognosis.

CONCLUSION

Taken together, we identified in CRC the MCM3AP-AS1/miR-193a-5p/SENP1 regulatory axis, which affords a therapeutic possibility for CRC.

Integrated Analysis of Distant Metastasis-Associated Genes and Potential Drugs in Colon Adenocarcinoma

Background: Most colon adenocarcinoma (COAD) patients die of distant metastasis, though there are some therapies for metastatic COAD. However, the genes exclusively expressed in metastatic COAD remain unclear. This study aims to identify prognosis-related genes associated with distant metastasis and develop therapeutic strategies for COAD patients.

Methods: Transcriptomic data from The Cancer Genome Atlas (TCGA; n = 514) cohort were analyzed as a discovery dataset. Next, the data from the GEPIA database and PROGgeneV2 database were used to validate our analysis. Key genes were identified based on the differential miRNA and mRNA expression with respect to M stage. The potential drugs targeting candidate differentially expressed genes (DEGs) were also investigated.

Results: A total of 127 significantly DEGs in patients with distant metastasis compared with patients without distant metastasis were identified. Then, four prognosis-related genes (LEP, DLX2, CLSTN2, and REG3A) were selected based on clustering analysis and survival analysis. Finally, three compounds targeting the candidate DEGs, including ajmaline, TTNPB, and dydrogesterone, were predicted to be potential drugs for COAD.

Conclusions: This study revealed that distant metastasis in COAD is associated with a specific group of genes, and three existing drugs may suppress the distant metastasis of COAD.

Comprehensive ceRNA network analysis and experimental studies identify an IGF2-AS/miR-150/IGF2 regulatory axis in colorectal cancer

Recently, a growing body of studies has demonstrated that long non-coding RNA (lncRNA) can act as microRNA (miRNA) sponges to regulate protein-coding gene expression and play essential roles in tumor initiation and progression. In the present study, we constructed a competitive endogenous RNA (ceRNA) network and identified potential regulatory axes in colorectal cancer (CRC) through both bioinformation and experimental validation. Firstly, we obtained differentially expressed (DE) lncRNAs, miRNAs, and mRNAs by analyzing the RNA expression profiles of CRC retrieved from The Cancer Genome Atlas (TCGA) database and CRC patients' data from affiliated Hospital of Jiangnan University, respectively. Then, we established a ceRNA regulatory network of CRC that includes 23 lncRNAs, 7 miRNAs and 244 mRNAs. To further identify these lncRNA-miRNA-mRNA regulatory axes which might play vital roles in CRC tumorigenesis and prognosis, we performed additional analyses using comprehensive bioinformatic methods. Several ceRNA regulatory axes, which consist of 2 lncRNAs, 2 miRNAs and 5 mRNAs, were obtained from the network. Finally, the interactions and correlations among these ceRNA networks were validated by experiments on CRC cell lines and clinical tumor tissues, and a potential IGF2-AS/miR-150/IGF2 axis that perfectly conform to the ceRNA theory was determined. According to the qRT-PCR results, miR-150 overexpression remarkably decreased IGF2-AS and IGF2 expression. Meanwhile, IGF2-AS expression was positively correlated with IGF2 expression in tumor tissue of CRC patients. Besides, dual luciferase reporter assays indicated that miR-150 could bound to IGF2-AS and the 3'UTR of and IGF2. In general, the constructed novel IGF2-AS/miR-150/IGF2 network might provide potential mechanisms of CRC development, and could act as a promising target for CRC treatment.

Identification of a Novel Tumor Microenvironment-Associated Eight-Gene Signature for Prognosis Prediction in Lung Adenocarcinoma

Background

Lung cancer has become the most common cancer type and caused the most cancer deaths. Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Accumulating evidence suggests the tumor microenvironment is correlated with the tumor progress and the patient's outcome. This study aimed to establish a gene signature based on tumor microenvironment that can predict patients' outcomes for LUAD.

Methods

Dataset TCGA-LUAD, downloaded from the TCGA portal, were taken as training cohort, and dataset GSE72094, obtained from the GEO database, was set as validation cohort. In the training cohort, ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to preliminarily screen prognostic genes. Besides, the LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan-Meier, Cox, and receiver operating characteristic curve (ROC) analyses. In addition, the correlation between tumor mutational burden (TMB) and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment.

Results

An eight-gene signature was built, and it was examined by Kaplan-Meier analysis, revealing that a significant overall survival difference was seen. The eight-gene signature was further proven to be independent of other clinico-pathologic parameters via the Cox regression analyses. Moreover, the ROC analysis demonstrated that this signature owned a better predictive power of LUAD prognosis. The eight-gene signature was correlated with TMB. Furthermore, GSEA and immune infiltrating analyses showed that the exact pathways related to the characteristics of eight-genes signature, and identified the vital roles of Mast cells resting and B cells naive in the prognosis of the eight-gene signature.

Conclusion

Identifying the eight-gene signature (INSL4, SCN7A, STAP1, P2RX1, IKZF3, MS4A1, KLRB1, and ACSM5) could accurately identify patients' prognosis and had close interactions with Mast cells resting and B cells naive, which may provide insight into personalized prognosis prediction and new therapies for LUAD patients.

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 Robust 6-lncRNA Prognostic Signature for Predicting the Prognosis of Patients With Colorectal Cancer Metastasis

Objective: Our study aimed to construct a robust long non-coding RNA (lncRNA) prognostic signature for colorectal cancer (CRC) metastasis.

Methods: Differentially expressed lncRNAs were identified between metastatic CRC and non-metastatic CRC samples from The Cancer Genome Atlas Database (TCGA) using the edgeR package. The differentially expressed lncRNAs with prognosis of patients with CRC metastasis were identified by univariate Cox regression analysis, followed by a stepwise multivariate Cox regression model. The survminer package in R was used to identify the optimal cutoff point for high-risk and low-risk groups. The receiver operating characteristic (ROC) curves were plotted to assess this signature. To explore potential signaling pathways associated with these lncRNAs, Gene Set Enrichment Analysis (GSEA) was performed.

Results: A 6-lncRNA signature was built based on the lncRNA expression profile for CRC metastasis. The optimal cutoff value was used to classify high-risk and low-risk groups using the survminer package. The high-risk groups could have poorer survival time than the low-risk groups. ROC curve result indicated that this lncRNA signature had high sensitivity and accuracy. GSEA analysis results showed that the six lncRNAs were significantly enriched in several CRC metastasis-related signaling pathways such as “cell cycle,” “DNA replication,” “mismatch repair,” “oxidative phosphorylation,” “regulation of autophagy,” and “insulin signaling pathway.”

Conclusion: Our study constructed a 6-lncRNA model for predicting the survival outcomes of patients with CRC metastasis, which could become potential prognostic biomarkers, and therapeutic targets for CRC metastasis.

Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.