Transcriptome profiling reveals an integrated mRNA-lncRNA signature with predictive value of early relapse in colon cancer

A prognostic mathematical model based on tumor microenvironment-related genes expression for breast cancer patients

Background

Tumor microenvironment (TME) status is closely related to breast cancer (BC) prognosis and systemic therapeutic effects. However, to date studies have not considered the interactions of immune and stromal cells at the gene expression level in BC as a whole. Herein, we constructed a predictive model, for adjuvant decision-making, by mining TME molecular expression information related to BC patient prognosis and drug treatment sensitivity.

Methods

Clinical information and gene expression profiles were extracted from The Cancer Genome Atlas (TCGA), with patients divided into high- and low-score groups according to immune/stromal scores. TME-related prognostic genes were identified using Kaplan-Meier analysis, functional enrichment analysis, and protein-protein interaction (PPI) networks, and validated in the Gene Expression Omnibus (GEO) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct and verify a prognostic model based on TME-related genes. In addition, the patients' response to chemotherapy and immunotherapy was assessed by survival outcome and immunohistochemistry (IPS). Immunohistochemistry (IHC) staining laid a solid foundation for exploring the value of novel therapeutic target genes.

Results

By dividing patients into low- and high-risk groups, a significant distinction in overall survival was found (p < 0.05). The risk model was independent of multiple clinicopathological parameters and accurately predicted prognosis in BC patients (p < 0.05). The nomogram-integrated risk score had high prediction accuracy and applicability, when compared with simple clinicopathological features. As predicted by the risk model, regardless of the chemotherapy regimen, the survival advantage of the low-risk group was evident in those patients receiving chemotherapy (p < 0.05). However, in patients receiving anthracycline (A) therapy, outcomes were not significantly different when compared with those receiving no-A therapy (p = 0.24), suggesting these patients may omit from A-containing adjuvant chemotherapy. Our risk model also effectively predicted tumor mutation burden (TMB) and immunotherapy efficacy in BC patients (p < 0.05).

Conclusion

The prognostic score model based on TME-related genes effectively predicted prognosis and chemotherapy effects in BC patients. The model provides a theoretical basis for novel driver-gene discover in BC and guides the decision-making for the adjuvant treatment of early breast cancer (eBC).

Construction of a Colorectal Cancer Prognostic Risk Model and Screening of Prognostic Risk Genes Using Machine-Learning Algorithms

This study is aimed at constructing a prognostic risk model for colorectal cancer (CRC) using machine-learning algorithms to provide accurate staging and screening of credible prognostic risk genes. We extracted CRC data from GSE126092 and GSE156355 of the Gene Expression Omnibus (GEO) database and datasets from TCGA to analyze the differentially expressed genes (DEGs) using bioinformatics analysis. Among the 330 shared DEGs related to CRC prognosis, we divided the analysis period into different phases and applied univariate COX regression, LASSO, and multivariate COX regression analysis. GO analysis and KEGG analysis revealed that the functions of these DEGs were primarily focused on cell cycle, DNA replication, cell mitosis, and other related functions, and this confirmed our results from a biological perspective. Finally, a prognostic risk model for CRC based on the CHGA, CLU, PLK1, AXIN2, NR3C2, IL17RB, GCG, and AJUBA genes was constructed, and the risk score enabled us to predict the prognosis for CRC. To obtain a comprehensive and accurate model, we used both internal and external evaluations, and the model was able to correctly differentiate patients with CRC into a high-risk group with poor prognosis and a low-risk group with good prognosis. The AUC values of the 3-, 5-, and 10-year survival ROC curves were 0.715, 0.721, and 0.777, respectively, according to the internal evaluation, and the AUC values were 0.606, 0.698, and 0.608, respectively, for the external evaluation using GSE39582 from the GEO database. We determined that CLU, PLK1, and IL17RB could be considered to be independent prognostic factors for CRC with significantly different expression (P < 0.05). Using machine-learning methods, a prognostic risk model comprised of eight genes was constructed. Not only does this model provide improved treatment guidance, but it also provides a novel perspective for analyzing survival conditions at a deeper biological level.

miR-556-3p/Disabled Homolog 2-Interacting Protein (dab2ip) Promotes Cancer Progression by Down-Regulating Bcl-2-Like Protein 11 (BIM) Expression in Colorectal Cancer

Colorectal cancer (CRC) is a major threat affecting human health. Studies have shown that miR-556-3p can regulate dab2ip and promote tumor deterioration, and up-regulation of BIM inhibits CRC cell progression. However, the interaction between miR-556-3p/dab2ip and BIM in CRC is unknown. We examined miR-556-3p expression in CRC tissues and cells by RT-qPCR. The impact of miR-556-3p/dab2ip and BIM on CRC cell behaviors were assessed by western blot, transwell and MTT assay. miR-556-3p was highly expressed in CRC and its overexpression increased CRC cell proliferation and migration as well as up-regulated dab2ip and Ki-67 expression. Besides, miR-556-3p could target the BIM and overexpressed miR-556-3p decreased BIM expression. However, silencing of BIM abrogated the impact of overexpressed miR-556-3p on CRC cell proliferation and migration. In conclusion, miR-556-3p/dab2ip promotes cell growth by down-regulating the expression of BIM, thereby promoting the progression of CRC.

Radiomics Assessment of the Tumor Immune Microenvironment to Predict Outcomes in Breast Cancer

Background

The immune microenvironment of tumors provides information on prognosis and prediction. A prior validation of the immunoscore for breast cancer (IS_BC) was made on the basis of a systematic assessment of immune landscapes extrapolated from a large number of neoplastic transcripts. Our goal was to develop a non-invasive radiomics-based IS_BC predictive factor.

Methods

Immunocell fractions of 22 different categories were evaluated using CIBERSORT on the basis of a large, open breast cancer cohort derived from comprehensive information on gene expression. The IS_BC was constructed using the LASSO Cox regression model derived from the Immunocell type scores, with 479 quantified features in the intratumoral and peritumoral regions as observed from DCE-MRI. A radiomics signature [radiomics ImmunoScore (RIS)] was developed for the prediction of IS_BC using a random forest machine-learning algorithm, and we further evaluated its relationship with prognosis.

Results

An IS_BC consisting of seven different immune cells was established through the use of a LASSO model. Multivariate analyses showed that the IS_BC was an independent risk factor in prognosis (HR=2.42, with a 95% CI of 1.49–3.93; P<0.01). A radiomic signature of 21 features of the IS_BC was then exploited and validated (the areas under the curve [AUC] were 0.899 and 0.815). We uncovered statistical associations between the RIS signature with recurrence-free and overall survival rates (both P<0.05).

Conclusions

The RIS is a valuable instrument with which to assess the immunoscore, and offers important implications for the prognosis of breast cancer.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Long noncoding RNAs to predict postoperative recurrence in bladder cancer and to develop a new molecular classification system

BACKGROUND

Reliable molecular markers are much needed for early prediction of recurrence in muscle-invasive bladder cancer (MIBC) patients. We aimed to build a long-noncoding RNA (lncRNA) signature to improve recurrence prediction and lncRNA-based molecular classification of MIBC.

METHODS

LncRNAs of 320 MIBC patients from the Cancer Genome Atlas (TCGA) database were analyzed, and a nomogram was established. A molecular classification system was created, and immunotherapy and chemotherapy response predictions, immune score analysis, immune infiltration analysis, and mutational data analysis were conducted. Survival analysis validation was also performed.

RESULTS

An eight-lncRNA signature classifed the patients into high- and low-risk subgroups, and these groups had significantly different (disease-free survival) DFS. The ability of the eight-lncRNA signature to make an accurate prognosis was tested using a validation dataset from our samples. The nomogram achieved a C-index of 0.719 (95% CI, 0.674-0.764). Time-dependent receiver operating characteristic curve (ROC) analysis indicated the superior prognostic accuracy of nomograms for DFS prediction (0.76, 95% CI, 0.697-0.807). Further, the four clusters (median DFS = 11.8, 15.3, 17.9, and 18.9 months, respectively) showed a high frequency of TTN (cluster 1), fibroblast growth factor receptor-3 (cluster 2), TP53 (cluster 3), and TP53 mutations (cluster 4), respectively. They were enriched with M2 macrophages (cluster 1), CD8⁺ T cells (cluster 2), M0 macrophages (cluster 3), and M0 macrophages (cluster 4), respectively. Clusters 2 and 3 demonstrated potential sensitivity to immunotherapy and insensitivity to chemotherapy, whereas cluster 4 showed potential insensitivity to immunotherapy and sensitivity to chemotherapy.

CONCLUSIONS

The eight-lncRNA signature risk model may be a reliable prognostic signature for MIBC, which provides new insights into prediction of recurrence of MIBC. The model may help clinical decision and eventually benefit patients.

Immune-Related Gene Expression Analysis Revealed Three lncRNAs as Prognostic Factors for Colon Cancer

Colorectal cancer (CRC) is one of the most common cancers. Almost 80% of CRC cases are colon adenocarcinomas (COADs). Several studies have indicated the role of immunotherapy in the treatment of various cancers. Our study aimed to identify immune-related long non-coding RNAs (lncRNAs) and to use them to construct a risk assessment model for evaluating COAD prognosis. Using differential expression, correlation, and Cox regression analyses, we identified three immune-related differentially expressed lncRNAs (IR-DELs) and used them to construct a risk assessment model. The area under the curve (AUC) for each receiver operating characteristic (ROC) curve at 3-, 5-, and 10-years were greater than 0.6. In addition, the risk assessment model was correlated with several immune cells and factors. The three IR-DELs (AC124067.4, LINC02604, and MIR4435-2HG) identified in this study can be used to predict outcomes for patients with COAD and might help in identifying those who can benefit from anti-tumor immunotherapy.

Discovery and Validation of Key Biomarkers Based on Immune Infiltrates in Alzheimer's Disease

BACKGROUND

As the most common neurodegenerative disease, Alzheimer's disease (AD) leads to progressive loss of cognition and memory. Presently, the underlying pathogenic genes of AD patients remain elusive, and effective disease-modifying therapy is not available. This study explored novel biomarkers that can affect diagnosis and treatment in AD based on immune infiltration.

METHODS

The gene expression profiles of 139 AD cases and 134 normal controls were obtained from the NCBI GEO public database. We applied the computational method CIBERSORT to bulk gene expression profiles of AD to quantify 22 subsets of immune cells. Besides, based on the use of the Least Absolute Shrinkage Selection Operator (LASSO), this study also applied SVM-RFE analysis to screen key genes. GO-based semantic similarity and logistic regression model analyses were applied to explore hub genes further.

RESULTS

There was a remarkable significance in the infiltration of immune cells between the subgroups. The proportions for monocytes, M0 macrophages, and dendritic cells in the AD group were significantly higher than those in the normal group, while the proportion of some cells was lower than that of the normal group, such as NK cell resting, T-cell CD4 naive, T-cell CD4 memory activation, and eosinophils. Additionally, seven genes (ABCA2, CREBRF, CD72, CETN2, KCNG1, NDUFA2, and RPL36AL) were identified as hub genes. Then we performed the analysis of immune factor correlation, gene set enrichment analysis (GSEA), and GO based on seven hub genes. The AUC of ROC prediction model in test and validation sets were 0.845 and 0.839, respectively. Eventually, the mRNA expression analysis of ABCA2, NDUFA2, CREBRF, and CD72 revealed significant differences among the seven hub genes and then was confirmed by RT-PCR.

CONCLUSION

A model based on immune cell infiltration might be used to forecast AD patients' diagnosis, and it provided a new perspective for AD treatment targets.

Association of the Collagen Signature in the Tumor Microenvironment With Recurrence and Survival of Patients With T4N0M0 Colon Cancer

BACKGROUND

The current clinicopathological risk factors do not accurately predict disease recurrence in patients with T4N0M0 colon cancer. We hypothesized that the collagen signature combined with clinicopathological risk factors (new model) had a better prognostic value than clinicopathological risk factors alone (clinicopathological model).

OBJECTIVE

This study aimed to establish a collagen signature in the tumor microenvironment and to validate its role in predicting the recurrence of T4N0M0 colon cancer.

DESIGN

This was a retrospective study.

SETTINGS

This study took place at a tertiary medical center.

PATIENTS

Patients with T4N0M0 colon cancer who underwent surgery at our center between 2009 and 2015 (n = 416) were included.

INTERVENTION

A total of 142 collagen features were analyzed in the tumor microenvironment in specimens of colon cancer by using second-harmonic generation imaging. A collagen signature was constructed using a least-absolute shrinkage and selection operator Cox regression model.

MAIN OUTCOME MEASURES

The primary outcomes measured were disease-free survival and overall survival.

RESULTS

The training and testing cohorts consisted of 291 and 125 randomly assigned samples, with recurrence rates of 19.9% and 22.4%. A 3-feature-based collagen signature predicted the recurrence risk at 1, 3, and 5 years, with the area under the receiver-operating characteristic curves of 0.808, 0.832, and 0.791 in the training cohort and 0.836, 0.807, and 0.794 in the testing cohort. Multivariate analysis revealed that the collagen signature could independently predict the disease-free survival (HR = 7.17, p < 0.001) and overall survival rates (HR = 5.03, p < 0.001). The new model had a better prognostic value than the clinicopathological model, which included 4 clinicopathological risk factors: obstruction or perforation, lymphovascular invasion, tumor budding, and no chemotherapy.

LIMITATIONS

This study was limited by its retrospective design.

CONCLUSIONS

The collagen signature in the tumor microenvironment may be a new prognostic marker that can effectively predict the recurrence and survival of patients with T4N0M0 colon cancer. See Video Abstract at http://links.lww.com/DCR/B503. ASOCIACIÓN DE LA RÚBRICA DE COLÁGENO EN EL MICROAMBIENTE TUMORAL CON LA RECIDIVA Y LA SOBREVIDA DE PACIENTES CON CÁNCER DE COLON T4N0M0: Los factores de riesgo clínico-patológicos actuales no predicen con precisión la recurrencia de la enfermedad en pacientes con cáncer de colon estadío T4N0M0. Presumimos que la rúbrica de colágeno combinada con factores de riesgo clínico-patológicos (nuevo modelo) tendrían un mejor valor pronóstico que los factores de riesgo clínico-patológicos solos (modelo clínico-patológico).El establecer una rúbrica de colágeno en el microambiente tumoral y validar su papel en la predicción de la recidiva del cáncer de colon T4N0M0.Estudio retrospectivo.Investigación llevada a cabo en un centro médico terciario.Se incluyeron pacientes con cáncer de colon T4N0M0 operados en nuestro centro entre 2009 y 2015 (n = 416).Se analizaron un total de 142 características de colágeno en el microambiente tumoral en muestras de cáncer de colon utilizando imágenes de segunda generación armónica. Se construyó una rúbrica de colágeno utilizando un modelo de regresión LASSO Cox.Sobrevida libre de enfermedad y sobrevida global.Las cohortes de entrenamiento y prueba consistieron en 291 y 125 muestras asignadas al azar, con tasas de recurrencia del 19,9% y 22,4%, respectivamente. La rúbrica del colágeno basada en 3 características predijo el riesgo de recurrencia a 1, 3 y 5 años, con el área bajo las curvas características operativas del receptor de 0,808, 0,832 y 0,791 en la cohorte de entrenamiento y 0,836, 0,807 y 0,794 en la cohorte de prueba, respectivamente. El análisis multivariado reveló que la firma de colágeno podría predecir de forma independiente la supervivencia libre de enfermedad (HR = 7,17, p <0,001) y las tasas de sobrevida general (HR = 5,03, p <0,001). El nuevo modelo tuvo un mejor valor pronóstico que el modelo clínico-patológico, que incluyó cuatro factores de riesgo clínico-patológicos: obstrucción o perforación, invasión linfovascular, gemación tumoral y ausencia de quimioterapia.Este estudio estuvo limitado por su diseño retrospectivo.La rúbrica de colágeno en el microambiente tumoral puede ser un nuevo marcador pronóstico para predecir eficazmente la recurrencia y la subrevida de los pacientes con cáncer de colon T4N0M0. Consulte Video Resumen en http://links.lww.com/DCR/B503. (Traducción-Dr. Xavier Delgadillo).

In silico development and clinical validation of novel 8 gene signature based on lipid metabolism related genes in colon adenocarcinoma

BACKGROUND

Changes in lipid metabolism pathways play a major role in colon carcinogenesis and development. Hence, we conducted a systematic analysis of lipid metabolism-related genes to explore new markers that predict the prognosis of colon adenocarcinoma (COAD).

METHODS

The non-negative Matrix Factorization (NMF) algorithm was applied to identify the molecular subtypes based on lipid metabolism-related genes. A weighted correlation network analysis (WCGNA) was used to identify co-expressed genes, and Lasso multivariate Cox analysis was performed to build a risk prognosis model. A timer database was used to analyze the immune infiltration of the gene signature and the GSCALite database was used for genome-wide analysis of the gene signature.

RESULTS

TCGA-COAD samples were divided into 3 subtypes based on lipid metabolism-related genes. 2739 genes were identified by WGCNA analysis. Finally, an 8-gene signature (RTN2, FYN, HEYL, FAM69A, FBXL5, HMGN2, LGALS4, STOX1) was constructed that demonstrated good robustness in different datasets, as well as an independent risk factor for colon cancer patients' prognosis. In addition, our model's predictive efficacy overall was higher than that of the other published models, and the 8 genes' expression analysis indicated that RTN2, HEYL, and STOX1 were all expressed highly significantly in COAD, while FAM69A, FBXL5, LGALS4, FYN and HMGN2 were expressed significantly poorly in cancer tissues, which was confirmed in immunohistochemistry. The 8 genes were expressed significantly differently in COAD immune subtypes and correlated with clinical variables. Genome-wide analysis revealed that the STOX1 mutation frequency was the highest, and genome methylation influenced HEYL, FAM69A, and STOX1 gene expression significantly; further, the expression of HEYL and FBXL5 was correlated positively with Copy number variation (CNV) and was regulated significantly by CNV in most cancers. FBXL5 was correlated significantly with austocystin d and bafilomycin and played an important role in anti-tumor and immunotherapy. The HEYL, FYN, FAM69A, and RTN2 genes' expression was associated with the EMT pathway's activation, while LGALS4 and STOX1 were associated significantly with the EMT pathway's inhibition.

CONCLUSION

This study constructed an 8-gene signature as a novel marker to predict colon cancer patients' survival.

Comprehensive functional genomic analyses link APC somatic mutation and mRNA-miRNA networks to the clinical outcome of stage-III colorectal cancer patients

Background

Colorectal cancer (CRC) is a major health concern globally, but exhibits regional and/or environmental distinctions in terms of outcome especially for patients with stage III CRC.

Methods

From 2014 to 2016, matched pairs of tumor and adjacent normal tissue samples from 60 patients with stage I–IV CRC from Chang Gung Memorial Hospital in Taiwan were analyzed using next-generation sequencing. The DNA, mRNA, and miRNA sequences of paired tumor tissues were profiled. An observational study with survival analysis was done. Online datasets of The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) were also integrated and compared.

Results

The gene that exhibited the highest mutation rate was adenomatous polyposis coli (APC) (75.0%), followed by TP53 (70.0%), KRAS (56.6%), and TTN (48.3%). APC was also the most frequently mutated gene in TCGA and ICGC datasets. Surprisingly, for non-metastatic cases (stages I-III), CRC patients with mutated APC had better outcome in terms of overall survival (p = 0.041) and recurrence free survival (p = 0.0048). Particularly for stage III CRC, the overall survival rate was 94.4% and 67.7%, respectively (p = 0.018), and the recurrence free survival rate was 94.4% and 16.7%, respectively (p = 0.00044). Further clinical and gene expression analyses revealed that the APC wt specimens to a greater extent exhibit poor differentiation state as well as EGFR upregulation, providing molecular basis for the poor prognosis of these patients. Finally, based on integrated transcriptome analysis, we constructed the mRNA-miRNA networks underlying disease recurrence of the stage III CRC and uncovered potential therapeutic targets for this clinical condition.

Conclusion

For stage III CRC, patients with mutated APC had better overall and recurrence free survival.

Proteomic profiling reveals a signature for optimizing prognostic prediction in Colon Cancer

Previous studies developed prognostic signatures largely depended on transcriptome profiles. The purpose of our present study was to develop a proteomic signature to optimize the evaluation of prognosis of colon cancer patients. The proteomic data of colon cancer patient cohorts were downloaded from The Cancer Proteome Atlas (TCPA). Patients were randomized 3:2 to train set and internal validation set. Univariate Cox regression and lasso Cox regression analysis were performed to identify the prognostic proteins. A four-protein signature was developed to divide patients into a high-risk group and low-risk group with significantly different survival outcomes in both train set and internal validation set. Time-dependent receiver-operating characteristic at 1 year demonstrated that the proteomic signature presented more prognostic accuracy [area under curve (AUC = 0.704)] than the American Joint Commission on Cancer tumor-node-metastasis (AJCC-TNM) staging system (AUC = 0.681) in entire set. In conclusion, we developed a proteomic signature which can improve prognostic accuracy of patients with colon cancer and optimize the therapeutic and follow-up strategies.

An autophagic gene-based signature to predict the survival of patients with low-grade gliomas

Background

Since autophagy remains an important topic of investigation, the RNA‐sequence profiles of autophagy‐related genes (ARGs) can provide insights into predicting low‐grade gliomas (LGG) prognosis.

Methods

The RNA‐seq profiles of autophagic genes and prognosis data of LGG were integrated from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model were carried out to identify the differentially expressed prognostic autophagy‐related genes. Then, the autophagic‐gene signature was formed and verified in TCGA test set and external CGGA cohorts. Time‐dependent receiver operating characteristic (ROC) was examined to test the accuracy of this signature feature. A nomogram was conducted to meet the needs of clinicians. Sankey diagrams were performed to visualize the relationship between the multigene signatures and clinic‐pathological features.

Results

Twenty‐four ARGs were finally identified most relevant to LGG prognosis. According to the specific prediction index formula, the patients were classified into low‐risk or high‐risk groups. Prognostic accuracy was proved by time‐dependent ROC analysis, with AUC 0.9, 0.93, and 0.876 at the survival time of 2‐, 3‐, and 5‐year, respectively, which was superior to the AUC of the isocitrate dehydrogenase (IDH) mutation. The result was confirmed while validated in the TCGA test set and external validation CGGA cohort. A nomogram was constructed to meet individual needs. With a visualization approach, Sankey diagrams show the relationship of the histological type, IDH status, and predict index. In TCGA and CGGA cohorts, both low‐risk groups displayed better survival rate in LGG while histological type and IDH status did not show consistency results.

Conclusions

24‐ARGs may play crucial roles in the progression of LGG, and LGG patients were effectively divided into low‐risk and high‐risk groups according to prognostic prediction. Overall, our study will provide novel strategies for clinical applications.

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

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

The clinical relevance of gene expression based prognostic signatures in colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent cancers, with more than one million new cases every year. In the last few decades, several advancements in therapeutic and preventative levels have reduced the mortality rate, but new biomarkers are required for improved prognosis. The alterations at the genetic and epigenetic level have been recognized as major players in tumorigenesis. The products of gene expression in the form of mRNA, microRNA, and long-noncoding RNA, have started to emerge as important regulatory molecules, playing an important role in cancer. Gene-expression based prognostic risk scores, which quantify and compare their expression, have emerged as promising biomarkers with enormous clinical value. These composite multi-gene models in which more than one gene is used to predict prognosis have been shown to be significantly effective in identifying patients with multiple clinico-pathological risks like overall mortality, response to chemotherapy, risk of metastasis, etc. The advent of microarray and advanced sequencing technologies have led to the generation of large datasets like TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), which have fueled the search for new biomarkers. Continuous evaluation of these candidate biomarkers in clinical settings is promising to improve the management of CRC. These composite gene signatures provide potential in identifying high-risk patients, which might help clinicians to better manage these patients and design appropriate personalized therapeutic interventions. In this review, we emphasize on composite prognostic scores from diverse resources with clinical utility in CRC.

Transcriptome profiling reveals an integrated mRNA-lncRNA signature with predictive value for long-term survival in diffuse large B-cell lymphoma

For patients with diffuse large B-cell lymphoma (DLBCL), survival at 24 months is a milestone for long-term survival. The purpose of this study was to develop a multigene risk score (MGRS) to refine the International Prognostic Index (IPI) model to identify patients with DLBCL at high risk of death within 24 months. Using a robust statistical strategy, we built a MGRS incorporating nine mRNAs and two lncRNAs. Stratification and multivariable Cox regression analysis confirmed the MGRS as an independent risk factor. A nomogram based on IPI+MGRS model was constructed and its calibration plot showed close agreement between predicted 2-year survival rate and observed rate. The 2-year AUC was bigger with the IPI+MGRS model (ΔAUC=0.162; 95%CI 0.1295–0.1903) than with the IPI model, and the IPI+MGRS model more accurately predicted the prognostic risk of DLBCL. The 2-year survival decision curve revealed the IPI+MGRS model was more useful clinically than the IPI model. Functional enrichment analysis showed that the MGRS correlated with cell cycle, DNA replication and repair. The results were validated using an independent external dataset. In conclusion, we successfully developed an integrated mRNA–lncRNA signature to refine the IPI model for predicting long-term survival of patients with DLBCL.

A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer

A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.

An immune cell infiltration-based immune score model predicts prognosis and chemotherapy effects in breast cancer

Background: Immune cells have essential auxiliary functions and influence clinical outcomes in cancer, with high immune infiltration being associated with improved clinical outcomes and better response to treatment in breast cancer (BC). However, studies to date have not fully considered the tumor-infiltrating immune cell (TIIC) landscape in tumors. This study investigated potential biomarkers based on TIICs to improve prognosis and treatment effect in BC.

Results: We enrolled 5112 patients for analysis and used cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT), a new computational algorithm, to quantify 22 TIICs in primary BC. From the results of univariate Cox regression, 12 immune cells were determined to be significantly related to the overall survival (OS) of BC patients. Furthermore, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to construct an immune prognostic model based on six potential biomarkers. By dividing patients into low- and high-risk groups, a significant distinction in OS was found in the training cohort, with 20-year survival rates of 42.6% and 26.3%, respectively. Applying a similar protocol to validation and test cohorts, we found that OS was significantly shorter in the high-risk group than in the low-risk group, regardless of the molecular subtype of BC. Using the immune score model to predict the effect of BC patients to chemotherapy, the survival advantage for the low-risk group was evident among those who received chemotherapy, regardless of the chemotherapy regimen. In evaluating the predictive value of the nomogram, a decision curve showed better predictive accuracy than the standard tumor-node-metastasis (TNM) staging system.

Conclusion: The immune cell infiltration-based immune score model can be effectively and efficiently used to predict the prognosis of BC patients as well as the effect of chemotherapy.

An Autophagy-Related Long Noncoding RNA Signature Contributes to Poor Prognosis in Colorectal Cancer

Purpose

Colorectal cancer is one of the most common malignant primary tumors, prone to metastasis, and associated with a poor prognosis. As autophagy is closely related to the development and treatment of colorectal cancer, we investigated the potential prognostic value of long noncoding RNA (lncRNA) associated with autophagy in colorectal cancer.

Methods

In this study, we acquired information on the expression of lncRNAs in colorectal cancer from the Cancer Genome Atlas (TCGA) database and found that 860 lncRNAs were associated with autophagy-related genes. Subsequently, univariate Cox regression analysis was used to investigate 32 autophagy-related lncRNAs linked to colon cancer prognosis. Subsequently, eight of the 32 autophagy-related lncRNAs (i.e., long intergenic nonprotein coding RNA 1503 [LINC01503], ZEB1 antisense RNA 1 [ZEB1-AS1], AC087481.3, AC008760.1, AC073896.3, AL138756.1, AL022323.1, and TNFRSF10A-AS1) were selected through multivariate Cox regression analysis. Based on these autophagy-related lncRNAs, a risk signature was constructed, and the patients were divided into high- and low-risk groups.

Results

The high-risk group's overall survival time was significantly shorter than that of the low-risk group (p < 0.0001). Receiver operating characteristic curve analysis was performed to further confirm the validity of the model (area under the curve: 0.689). Moreover, multivariate regression suggested that the risk score was a significant prognostic risk factor in colorectal cancer. Gene set enrichment analysis showed that these gene sets are significantly enriched in cancer-related pathways, such as Kirsten rat sarcoma viral oncogene homolog (KRAS) signaling.

Conclusion

The risk signature of eight autophagy-related lncRNAs has prognostic potential for colorectal cancer. These autophagy-related lncRNAs may play a vital role in the biology of colorectal cancer.

Integrated analysis identifies a long non-coding RNAs-messenger RNAs signature for prediction of prognosis in hepatitis B virus-hepatocellular carcinoma patients

Hepatitis B virus (HBV) infection is a leading cause of hepatocellular carcinoma (HCC), but HBV-HCC related prognosis signature remains rarely investigated. This study was to identify an integrated long non-coding RNAs-messenger RNAs (lncRNA-mRNA) signature for prediction of overall survival (OS) and explore their underlying functions.One RNA-sequencing dataset (training set, n = 95) and one microarray dataset E-TABM-36 (validation set, n = 44) were collected. Least absolute shrinkage and selection operator analysis was performed to identify an lncRNA-mRNA prognosis signature. The OS difference of patients in the high-risk and low-risk risk groups was evaluated by Kaplan-Meier curve. Area under the receiver operating characteristic curve (AUC), Harrell concordance index (C-index) calculation, and multivariate analyses with clinical characteristics were used to determine the prognostic ability. Furthermore, a coexpression network was constructed to interpret the functions.Nine signature genes (3 lncRNAs and 6 mRNAs) were selected to generate the risk score model. Patients belonging to the high-risk group showed a significantly shorter survival than those of the low-risk group. The prediction accuracy of the risk score for 5-year OS was 0.936 and 0.905 for the training set and validation set, respectively. Also, this risk score was independent of various clinical variables for the prognosis prediction. Incorporation of the risk score remarkably increased the predictive power of the routine clinical prognostic factors (vascular invasion status, tumor recurrence status) (AUC = 0.942 vs 0.628; C-index = 0.7997 vs 0.6908). Furthermore, LncRNA insulin-like growth factor 2 antisense RNA (IGF2-AS) and long intergenic non-protein coding RNA 342 (LINC00342) were predicted to exert tumor suppression effects by regulating homeobox D1 (HOXD1) and secreted frizzled related protein 5 (SFRP5), respectively; while lncRNA rhophilin Rho GTPase binding protein 1 antisense RNA 1 (RHPN1-AS1) may possess carcinogenic potential by promoting the transcription of chromobox 2 (CBX2), cell division cycle 20 (CDC20), matrix metallopeptidase 12 (MMP12), stratifin (SFN), tripartite motif containing 16 (TRIM16), and uroplakin 3A (UPK3A). These mRNAs may be associated with cell proliferation or apoptosis related pathways.This study may provide a novel, effective prognostic biomarker, and some therapeutic targets for HBV-HCC patients.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

The m6A-Related mRNA Signature Predicts the Prognosis of Pancreatic Cancer Patients

N6-methyladenosine (m6A) has an important epitranscriptomic modification that controls cancer self-renewal and cell fate. The addition of m6A to mRNA is a reversible modification. The deposition of m6A is encoded by a methyltransferase complex involving three homologous factors, jargonized as "writers," "erasers," and "readers." However, their roles in pancreatic adenocarcinoma (PAAD) are underexploited. With the use of The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases, we provided an mRNA signature that may improve the prognostic prediction of PAAD patients based on the genetic status of m6A regulators. PAAD patients with genetic alteration of m6A regulators had worse disease-free and overall survival. After comparing PAAD groups with/without genetic alteration of m6A regulators, we identified 196 differentially expressed genes (DEGs). Then, we generated a 16-mRNA signature score system through least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Multivariate cox regression analysis demonstrated that a high-risk score significantly correlates with poor prognosis. Moreover, time-dependent receiver operating characteristic (ROC) curves revealed it was effective in predicting the overall survival in both training and validation sets. PAH, ZPLD1, PPFIA3, and TNNT1 from our signature also exhibited an independent prognostic value. Collectively, these findings can improve the understanding of m6A modifications in PAAD and potentially guide therapies in PAAD patients.

Development and validation of a novel epigenetic signature for predicting prognosis in colon cancer

Epigenetic factors play a critical role in carcinogenesis by imparting a distinct feature to the chromatin architecture. The present study aimed to develop a novel epigenetic signature for evaluating the relapse-free survival of colon cancer patients. Public microarray datasets were acquired from the Gene Expression Omnibus databases: GSE39582, GSE17538, GSE33113, and GSE37892 set. Patients from GSE39582 set were randomized 1:1 into training and internal validation series. Patients were divided into high-risk and low-risk groups in training series based on a set of 11 epigenetic factors (p < .001). The good reproducibility for the prognostic value of the epigenetic signature was confirmed in the internal validation series (p < .001), external validation series (a combination of GSE17538 set, GSE33113 set, and GSE37892 set; p = .018), and entire series (p < .001). Furthermore, a nomogram, which integrated the epigenetic signature, pathological stage, and postoperative chemotherapy, was developed based on the GSE39582 set. The time-dependent receiver operating characteristic curve at 1 year demonstrated that the comprehensive signature presented superior prognostic value than the pathological stage. In conclusion, an epigenetic signature, which could be utilized to divide colon cancer patients into two groups with significantly different risk of relapse, was established. This biomarker would aid in identifying patients who require an intensive follow-up and aggressive therapeutic intervention.

The Long Non-Coding RNA-RoR Promotes the Tumorigenesis of Human Colorectal Cancer by Targeting miR-6833-3p Through SMC4

BACKGROUND

Long non-coding RNA regulator of reprogramming (LINC-RoR) has shown different expressions in a variety of tumors as a stem cell inducer through reprogramming regulation. However, its role and regulation mechanisms in colorectal cancer (CRC) are still unclear.

MATERIALS AND METHODS

Quantitative real-time PCR and Western blot were performed to examine LINC-RoR expression in paired CRC samples and cell lines. The relationship of LINC-RoR expression with clinicopathological characteristics and clinical outcomes was analyzed. The biological functions of LINC-RoR were studied by MTS and colony formation in vitro. Cell apoptosis was analysed by the flow cytometry. The Dual-luciferase reporter assays and RIP assays were performed to explore the regulatory relationship of LINC-RoR.

RESULTS

In this study, we found that LINC-RoR was upregulated in CRC cell lines and tissues. High expression of LINC-RoR was associated with poorer survival time and multivariate analysis results showed that LINC-RoR was an independent risk factor of tumor malignancy progression. Overexpression of LINC-RoR promoted the cell proliferation and knocked down it can reverse the effect in vitro. The regulatory network of LINC-ROR/miR-6833-3p/SMC4 was predicted with bioinformatics analysis tools and validated via dual-luciferase reporter assays and RIP. Further study revealed that in overexpression LINC-RoR cell lines the expression of miR-6833-3p was downregulated and miR-6833-3p can inhibit its target gene SMC4, the apoptosis-related protein.

CONCLUSION

We concluded that LINC-RoR functions as an oncogene in CRC through the miR-6833-3p/SMC4 pathway.

A Combined four-mRNA Signature Associated with Lymphatic Metastasis for Prognosis of Colorectal Cancer

Background: Colorectal cancer (CRC) is one of the most common malignant tumors in the world. Lymph node metastasis (LNM) is a common mode of metastasis of CRC. However, the combined mRNA biomarkers associated with LNM of CRC that can effectively predict CRC prognosis have not been reported yet.

Methods: To identify biomarkers that are associated with LNM, we collected data from the The Cancer Genome Atlas (TCGA) database. The edgeR package was searched to seek LNM-related genes by comparisons between cancer samples and normal colorectal tissues and between LNM and non-LNM (NLNM) of CRC. Univariate and multivariate regression analysis of genes in the intersection to build gene signature associated with independent prognosis of CRC, and then verified by Kaplan-Meier curve and log-rank test, receiver operating characteristic (ROC) curve was used to determine the efficiency of survival prediction of our four-mRNA signature. Finally, the potential molecular mechanisms and properties of these gene signature were also explored with functional and pathway enrichment analysis.

Results: 329 mRNAs were up-regulated in CRC tissues with LNM, and 8461 mRNAs were up-regulated in CRC tissues, the intersection is 100 mRNAs. After univariate and multivariate Cox regression analysis of 100 mRNAs, a novel four LNM related mRNAs (EPHA8, KRT85, GABRA3, and CLPSL1) were screened as independent prognostic indicators of CRC. Surprisingly, the four-mRNA signature can predict the prognosis of CRC patients independently of clinical factors andthe area under the curve (AUC) of the ROC is 0.730. The novel four-mRNA signature was used to identify high and low-risk groups. Stratified analysis indicated the risk score based on four-mRNA signature was an independent prognostic indicator for female, T3+T4, N1+N2 ,stage III+IV and patients with no new tumor event. Functional annotation of this risk model in high-risk patients revealed that pathways associated with neuroactive ligand-receptor interaction, estrogen signaling pathway, and steroid hormone biosynthesis.

Conclusions: By conducting TCGA data mining, our study demonstrated that a four-mRNA signature associated with LNM can be used as a combined biomarker for independent prognosis of CRC.

A stroma-related lncRNA panel for predicting recurrence and adjuvant chemotherapy benefit in patients with early-stage colon cancer

The heterogeneity in prognoses and chemotherapeutic responses of colon cancer patients with similar clinical features emphasized the necessity for new biomarkers that help to improve the survival prediction and tailor therapies more rationally and precisely. In the present study, we established a stroma‐related lncRNA signature (SLS) based on 52 lncRNAs to comprehensively predict clinical outcome. The SLS model could not only distinguish patients with different recurrence and mortality risks through univariate analysis, but also served as an independent factor for relapse‐free and overall survival. Compared with the conventionally used TNM stage system, the SLS model clearly possessed higher predictive accuracy. Moreover, the SLS model also effectively screened chemotherapy‐responsive patients, as only patients in the low‐SLS group could benefit from adjuvant chemotherapy. The following cell infiltration and competing endogenous RNA (ceRNA) network functional analyses further confirmed the association between the SLS model and stromal activation‐related biological processes. Additionally, this study also identified three phenotypically distinct colon cancer subtypes that varied in clinical outcome and chemotherapy benefits. In conclusion, our SLS model may be a significant determinant of survival and chemotherapeutic decision‐making in colon cancer and may have a strong clinical transformation value.

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

Colorectal cancer (CRC) is one of the most common cancers worldwide, whose morbidity and mortality gradually increased. Here, we aimed to identify and access prognostic long non-coding RNAs (lncRNAs) associated with overall survival (OS) in CRC. Firstly, RNA expression profiles were obtained from The Cancer Genome Atlas (TCGA) database, and 439 CRC patients were enrolled as a training set. Univariate Cox analysis and the least absolute shrinkage and selection operator analysis (LASSO) were performed to identify the prognostic lncRNAs. Multivariable Cox regression analysis was used to establish a prognostic risk formula including three lncRNAs (AP003555.2, AP006284.1, and LINC01602). The low-risk group had a better OS than the high-risk group (P < 0.0001), and the areas under the receiver operating characteristic curve (AUCs) of 3- and 5-year OS were 0.712 and 0.674, respectively. Then, we evaluated the signature in a clinical validation set which were collected from the Affiliated Hospital of Jiangnan University. Compared with the low-risk group, patients' OS were found to be significantly worse in the high-risk group (P = 0.0057). The AUCs of 3- and 5-year OS were 0.701 and 0.694, respectively. Finally, we constructed an lncRNA–microRNA (miRNA)–messenger RNA (mRNA) competing endogenous RNA (ceRNA) network to explore the potential function of three differentially expressed lncRNAs (DElncRNAs). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these DElncRNAs were involved with several cancer-related pathways. In summary, our data provide evidence that the three-lncRNA signature could serve as an independent biomarker to predict prognosis in CRC. This study will also suggest that these three lncRNAs potentially participate in the progression of CRC.

A Novel DNA Methylation-Based Signature Can Predict the Responses of MGMT Promoter Unmethylated Glioblastomas to Temozolomide

Glioblastoma (GBM) is the most malignant glioma, with a median overall survival (OS) of 14–16 months. Temozolomide (TMZ) is the first-line chemotherapy drug for glioma, but whether TMZ should be withheld from patients with GBMs that lack O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is still under debate. DNA methylation profiling holds great promise for further stratifying the responses of MGMT promoter unmethylated GBMs to TMZ. In this study, we studied 147 TMZ-treated MGMT promoter unmethylated GBM, whose methylation information was obtained from the HumanMethylation27 (HM-27K) BeadChips (n = 107) and the HumanMethylation450 (HM-450K) BeadChips (n = 40) for training and validation, respectively. In the training set, we performed univariate Cox regression and identified that 3,565 CpGs were significantly associated with the OS of the TMZ-treated MGMT promoter unmethylated GBMs. Functional analysis indicated that the genes corresponding to these CpGs were enriched in the biological processes or pathways of mitochondrial translation, cell cycle, and DNA repair. Based on these CpGs, we developed a 31-CpGs methylation signature utilizing the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. In both training and validation datasets, the signature identified the TMZ-sensitive GBMs in the MGMT promoter unmethylated GBMs, and only the patients in the low-risk group appear to benefit from the TMZ treatment. Furthermore, these identified TMZ-sensitive MGMT promoter unmethylated GBMs have a similar OS when compared with the MGMT promoter methylated GBMs after TMZ treatment in both two datasets. Multivariate Cox regression demonstrated the independent prognostic value of the signature in TMZ-treated MGMT promoter unmethylated GBMs. Moreover, we also noticed that the hallmark of epithelial–mesenchymal transition, ECM related biological processes and pathways were highly enriched in the MGMT unmethylated GBMs with the high-risk score, indicating that enhanced ECM activities could be involved in the TMZ-resistance of GBM. In conclusion, our findings promote our understanding of the roles of DNA methylation in MGMT umethylated GBMs and offer a very promising TMZ-sensitivity predictive signature for these GBMs that could be tested prospectively.

Integration analysis for novel lncRNA markers predicting tumor recurrence in human colon adenocarcinoma

BACKGROUND

Numerous evidence has suggested that long non-coding RNA (lncRNA) acts an important role in tumor biology. This study focuses on the identification of novel prognostic lncRNA biomarkers predicting tumor recurrence in human colon adenocarcinoma.

METHODS

We obtained the research data from The Cancer Genome Atlas (TCGA) database. The interaction among different expressed lncRNA, miRNA and mRNA markers between colon adenocarcinoma patients with and without tumor recurrence were verified with miRcode, starBase and miRTarBase databases. We established the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network based on the verified association between the selected markers. We performed the functional enrichment analysis to obtain better understanding of the selected lncRNAs. Then we use multivariate logistic regression to identify the prognostic lncRNA markers with covariates. We also generated a nomogram predicting tumor recurrence risk based on the identified lncRNA biomarkers and clinical covariates.

RESULTS

We included 12,727 lncRNA, 1881 miRNA and 47,761 mRNA profiling and clinical features for 113 colon adenocarcinoma patients obtained from the TCGA database. After filtration, we used 37 specific lncRNAs, 60 miRNAs and 148 mRNAs in the ceRNA network analysis. We identified five lncRNAs as prognostic lncRNA markers predicting tumor recurrence in colon adenocarcinoma, in which four of them were identified for the first time. Finally, we generated a nomogram illustrating the association between the identified lncRNAs and the tumor recurrence risk in colon adenocarcinoma.

CONCLUSIONS

The four newly identified lncRNA biomarkers might be potential prognostic biomarkers predicting tumor recurrence in colon adenocarcinoma. We recommend that further clinical and fundamental researches be conducted on the identified lncRNA markers.

LINC00957 Acted as Prognostic Marker Was Associated With Fluorouracil Resistance in Human Colorectal Cancer

Colorectal cancer (CRC) is one of the most prevalent digestive tumors in China. Recent studies indicate that long intergenic non-coding RNAs (lincRNAs) play a crucial role in predicting survival for CRC patients. However, the novel lincRNA, LINC00957, is largely unclear in CRC. The purpose of the current study was to determine LINC00957 expression, assess its the clinical significance and explore the potential mechanism in CRC. The qRT-PCR was used to quantify the expression levels of LINC00957 in tissues and cell lines. Our research revealed that LINC00957 was significantly higher expression in CRC. In addition, the LINC00957 expression was associated with TNM stage and chemotherapy outcome, but age, gender, tumor size, histological grade, primary tumor location. CRC patients with high LINC00957 expression level showed poor overall survival (P = 0.002). Multivariate survival analysis indicated that LINC00957 was a prognostic factor for CRC patients (P = 0.010). Mechanically, inhibition of LINC00957 expression reversed 5-FU resistance by down-regulating P-gP. In summary, our study indicated that this novel lncRNA expression signature might be a useful biomarker of the prognosis and therapeutic target for CRC patients.

Prognostic value of long non-coding RNA signatures in bladder cancer

Bladder cancer (BLCA) is a devastating cancer whose early diagnosis can ensure better prognosis. Aim of this study was to evaluate the potential utility of lncRNAs in constructing lncRNA-based classifiers of BLCA prognosis and recurrence. Based on the data concerning BLCA retrieved from TCGA, lncRNA-based classifiers for OS and RFS were built using the least absolute shrinkage and selection operation (LASSO) Cox regression model in the training cohorts. More specifically, a 14-lncRNA-based classifier for OS and a 12-lncRNA-based classifier for RFS were constructed using the LASSO Cox regression. According to the prediction value, patients were divided into high/low-risk groups based on the cut-off of the median risk-score. The log-rank test showed significant differences in OS and RFS between low- and high-risk groups in the training, validation and whole cohorts. In the time-dependent ROC curve analysis, the AUCs for OS in the first, third, and fifth year were 0.734, 0.78, and 0.78 respectively, whereas the prediction capability of the 14-lncRNA classifier was superior to a previously published lncRNA classifier. As for the RFS, the AUCs in the first, third, and fifth year were 0.755, 0.715, and 0.740 respectively. In summary, the two-lncRNA-based classifiers could serve as novel and independent prognostic factors for OS and RFS individually.

Identification and validation of a prognostic four-genes signature for hepatocellular carcinoma: integrated ceRNA network analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most aggressive malignant tumors, with a poor long-term prognosis worldwide. The functional deregulations of global transcriptome were associated with the genesis and development of HCC, but lacks systematic research and validation.

METHODS

A total of 519 postoperative HCC patients were included. We built an interactive and visual competing endogenous RNA network. The prognostic signature was established with the least absolute shrinkage and selection operator algorithm. Multivariate Cox regression analysis was used to screen for independent prognostic factors for HCC overall survival.

RESULTS

In the training set, we identified a four-gene signature (PBK, CBX2, CLSPN, and CPEB3) and effectively predicted the overall survival. The survival times of patients in the high-score group were worse than those in the low-score group (p = 0.0004), and death was also more likely in the high-score group (HR 2.444, p < 0.001). The results were validated in internal validation set (p = 0.0057) and two external validation cohorts (HR 2.467 and 2.6). The signature (AUCs of 1, 2, 3 years were 0.716, 0.726, 0.714, respectively) showed high prognostic accuracy in the complete TCGA cohort.

CONCLUSIONS

In conclusion, we successfully built a more extensive ceRNA network for HCC and then identified a four-gene-based signature, enabling prediction of the overall survival of patients with HCC.

Identification and validation of an immune cell infiltrating score predicting survival in patients with lung adenocarcinoma

BACKGROUND

Immune infiltration may predict survival and have clinical significance in lung cancer. However, immune signatures derived from immune profiling based on bulk tumor transcriptomes have not been systematically established in lung adenocarcinoma. We aimed to construct an immune cell infiltrating score, using a new algorithm for evaluating immune infiltration, to improve the prognostic model of lung adenocarcinoma.

METHODS

Public datasets of lung adenocarcinoma from the Gene Expression Omnibus and The Cancer Genome Atlas were adopted as the training and validation cohorts. Fractions of different immune cell subtypes in each sample were estimated using the CIBERSORT algorithm. The immune infiltrating score was further developed by a least absolute shrinkage and selection operator regression model. The prognostic value and clinical relationship of the model was then further explored.

RESULTS

An immune infiltrating score model was established on the basis of the immune cells in the training cohort. A high score was associated with significantly worse survival in patients with lung adenocarcinoma (P < 0.001). The prognostic value of the score was confirmed in the validation cohort. The immune infiltrating score could improve the accuracy of predictions of survival when combined with the staging system. Furthermore, the score was potentially associated with patient smoking status and histologic subtype of lung adenocarcinoma. Its possible association with the efficacy of adjuvant chemotherapy was not statistically significant.

CONCLUSION

The immune cell infiltrating score has prognostic significance in predicting overall survival in patients with lung adenocarcinoma.

A robust panel based on tumour microenvironment genes for prognostic prediction and tailoring therapies in stage I-III colon cancer

Background

Tumour microenvironment (TME) is critical for the regulation of cancer development as well as therapy. The objective of the current study was the development of a robust prognostic model based on TME-relevant genes.

Methods

Five public microarray datasets providing clinical information were obtained. The least absolute shrinkage and selection operator regression method was used to reduce the dimensionality of robust prognostic genes identified via the bootstrap method.

Findings

We established a prognostic panel, designated as tumour microenvironment risk score (TMRS), consisting of 100 genes. With specific risk score formulae, the TMRS panel possesses a strong ability to predict relapse-free survival and overall survival through both univariate and multivariate analyses. Compared with the TNM stage, the TMRS panel showed much higher predictive accuracy. Further analysis revealed that patients with higher TMRS scores exhibited no therapeutic benefits from adjuvant chemotherapy, probably due to the activation of stromal relevant pathways and infiltration of stromal cells. Besides colon cancer, the TMRS panel was also revealed to be a reliable tool for prognostic prediction and chemotherapeutic decision-making in gastric cancer. Its value in predicting immunotherapy outcomes was also confirmed in two other cohorts consisting of metastatic urothelial carcinoma patients and melanoma patients.

Interpretation

Our TMRS panel may be an effective tool for survival prediction and treatment guidance in patients with stage I–III colon cancer.

Fund

This work was supported by the National Natural Science Foundation of China (No. 81772580) and Guangzhou Planed Project of Science and Technology (No. 201803010070).