Identification of Epithelial-Mesenchymal Transition-Related lncRNAs that Associated With the Prognosis and Immune Microenvironment in Colorectal Cancer

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Breast cancer is the most common malignancy in women around the world. Intratumor and intertumoral heterogeneity persist in mammary tumors. Therefore, the identification of biomarkers is essential for the treatment of this malignancy. This study analyzed 28,143 genes expressed in 49 breast cancer cell lines using a Weighted Gene Co-expression Network Analysis to determine specific target proteins for Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes. Sixty-five modules were identified, of which five were characterized as having a high correlation with breast cancer subtypes. Genes overexpressed in the tumor were found to participate in the following mechanisms: regulation of the apoptotic process, transcriptional regulation, angiogenesis, signaling, and cellular survival. In particular, we identified the following genes, considered as hubs: IFIT3, an inhibitor of viral and cellular processes; ETS1, a transcription factor involved in cell death and tumorigenesis; ENSG00000259723 lncRNA, expressed in cancers; AL033519.3, a hypothetical gene; and TMEM86A, important for regulating keratinocyte membrane properties, considered as a key in Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes, respectively. The modules and genes identified in this work can be used to identify possible biomarkers or therapeutic targets in different breast cancer subtypes.

Identification of necroptosis-related lncRNAs for prognosis prediction and screening of potential drugs in patients with colorectal cancer

BACKGROUND

Tumor recurrence and metastasis lead to a poor prognosis in colorectal cancer (CRC). Necroptosis is closely related to the tumor microenvironment (TME) and affects tumor recurrence and metastasis. We aimed to stratify CRC patients according to necroptosis-related long noncoding RNAs (lncRNAs), which can be used to not only evaluate prognosis and improve precision medicine in clinical practice but also screen potential immunotherapy drugs.

AIM

To stratify CRC patients according to necroptosis-related lncRNAs (NRLs), which can be used to not only evaluate prognosis and improve precision medicine in clinical practice but also screen potential immunotherapy drugs.

METHODS

LncRNA expression profiles were collected from The Cancer Genome Atlas. NRLs were identified by coexpression analysis. Cox regression analysis identified a NRL signature. Then, the value of this signature was comprehensively and multidimensionally evaluated, and its reliability for CRC prognosis prediction was assessed with clinical CRC data and compared with that of six other lncRNA signatures. Gene set enrichment analysis, TME analysis and half-maximal inhibitory concentration (IC₅₀) prediction were also performed according to the risk score (RS) of the signature.

RESULTS

An 8-lncRNA signature significantly associated with overall survival (OS) was constructed, and its reliability was validated with clinical CRC data. Most of the areas under the receiver operating characteristic curves (AUCs) values for 1-, 3- and 5-year OS for this signature were higher than those for the other six lncRNA signatures. OS, disease-specific survival and the progression-free interval were all significantly poorer in the high-risk group. The RS of the signature showed good concordance with the predicted prognosis, with AUCs for 1-, 3- and 5-year OS of 0.79, 0.81 and 0.77, respectively. Additionally, the calibration plots for this signature combined with clinical factors showed that this combination could effectively improve the ability to predict OS. The RS was correlated with tumor stage, lymph node metastasis and distant metastasis. Most of the enriched Kyoto Encyclopedia of Genes and Genomes and Gene Ontology terms were tumor metastasis-related pathways in the high-risk group; these patients showed greater infiltration of immunosuppressive cells, such as cancer-associated fibroblasts, hematopoietic stem cells and M2 macrophages, but less infiltration of infiltrating antitumor effector immune cells, such as cluster of differentiation 8⁺ T cells and regulatory T cells (Tregs). We explored additional potential immune checkpoint genes and potential immunotherapeutic and chemotherapeutic drugs with relatively low IC₅₀ values.

CONCLUSION

We identified an NRL signature with strong fidelity that could stably predict prognosis and might be an indicator of the TME of CRC. Furthermore, additional potential immunotherapeutic and chemotherapeutic drugs were explored.

Epithelial-mesenchymal transition-related lncRNAs associated with prognosis and immune cell infiltration in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) remains the most common type of lung cancer and is associated with distant metastasis and poor prognosis. Epithelial-mesenchymal transition (EMT) plays crucial roles in carcinogenesis, embryogenesis, and wound healing. EMT-related molecules may be adopted for early diagnosis and prognosis of cancer and targeting them may constitute an attractive strategy for treatment. This study aims to identify the EMT-related long non-coding RNAs (lncRNAs) and develop a risk signature to accurately predict the prognosis of LUAD patients.

METHODS

The RNA-seq data and corresponding clinical profiles were obtained from LUAD cohort of The Cancer Genome Atlas (TCGA) database. EMT-related lncRNAs significantly associated with prognosis were identified by Pearson correlation analysis and univariate regression analysis. Subsequently, an EMT-related prognostic risk signature was developed through LASSO and multivariate regression analyses. Kaplan Meier and receiver operating characteristic curve analysis were implemented to assess the predictive performance of the signature. The nomogram was constructed to predict the 1-year, 3-year, and 5-year overall survival of LUAD patients. Additionally, enrichment analyses were carried out to identify probable biologic processes and cellular pathways involved in the signature. The correlation of immune cell infiltration and risk score was also evaluated by CIBERSORT algorithm. Finally, we constructed a ceRNA network to further study possible downstream targets and molecular mechanisms of EMT-related lncRNAs in LUAD.

RESULTS

Eight EMT-related lncRNAs were identified to develop a prognostic risk signature in LUAD. Patients with high-risk scores had worse survival outcomes than those with low-risk scores. The signature showed robust predictive potential, and was verified to be an independent prognostic factor. Moreover, the risk score based on the signature was significantly correlated with immune cell infiltration in LUAD.

CONCLUSIONS

We established and validated a prognostic signature that reflects the tumor microenvironment characteristics and predicts the outcomes for LUAD.

Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer

Background

Most prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients.

Methods

Prognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and followed by multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan-Meier (K-M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-, 3-, and 5-year survival probabilities. Function annotation and pathway enrichment analyses were used to elucidate the biological implications of this model.

Results

A total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues.

Conclusion

A credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients.

Identification of Epithelial-Mesenchymal Transition- (EMT-) Related LncRNA for Prognostic Prediction and Risk Stratification in Esophageal Squamous Cell Carcinoma

Background

Epithelial-mesenchymal transition (EMT) is significantly associated with the invasion and development of esophageal squamous cell carcinoma (ESCC). However, the importance of EMT-related long noncoding RNA (lncRNA) is little known in ESCC.

Methods

GSE53624 (N = 119) and GSE53622 (N = 60) datasets retrieved from the Gene Expression Omnibus (GEO) database were used as training and external validation cohorts, respectively. GSE53624 and GSE53622 datasets were all sampled from China. Then, the prognostic value of EMT-related lncRNA was comprehensively investigated by weighted coexpression network analysis (WGCNA) and COX regression model.

Results

High expression of PLA2G4E-AS1, AC063976.1, and LINC01592 significantly correlated with the favorable overall survival (OS) of ESCC patients, and LINC01592 had the greatest contribution to OS. Importantly, ESCC patients were divided into low- and high-risk groups based on the optimal cut-off value of risk score estimated by the multivariate COX regression model of these three lncRNA. Patients with high risk had a shorter OS rate and restricted mean survival time (RMST) than those with low risk. Moreover, univariate and multivariate COX regression revealed that risk stratification, age, and TNM were independent prognostic predictors, which were used to construct a nomogram model for individualized and visualized prognosis prediction of ESCC patients. The calibration curves and time-dependent ROC curves in the training and validation cohorts suggested that the nomogram model had a good performance. Interestingly, clear trends indicated that risk score positively correlated with tumor microenvironment (TME) scores and immune checkpoints TIGIT, CTLA4, and BTLA. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that PLA2G4E-AS1, AC063976.1, and LINC01592 were primarily associated with TNF signaling pathway, NF-kappa B signaling pathway, and ECM-receptor interaction.

Conclusion

We developed EMT-related lncRNA PLA2G4E-AS1, AC063976.1, and LINC01592 for prognostic prediction and risk stratification of Chinese ESCC patients, which might provide deep insight for personalized prognosis prediction in Chinese ESCC patients and be potential biomarkers for designing novel therapy.

Large tumor suppressor 2 is a prognostic biomarker and correlated with immune infiltrates in colorectal cancer

Colorectal cancer (CRC) is a common malignancy that has both low 5-year survival and high prevalence. Immunotherapy has achieved impressive progress for treatment of CRC, but still faces huge challenges. Although large tumor suppressor 2 (LATS2) is well accepted to be related to cancer progression, the prognostic potential and immune response role of LATS2 expression in CRC remain unclear. To investigate the value of LATS2 for prognosis and immune infiltration, a retrospective study of 213 CRC patients was carried out. We determined the expression of LATS2 in tumor tissues by immunohistochemistry. The results indicated that LATS2 expression was down-regulated in CRC tissues and clearly related to tumor differentiation (P = 0.002) and TNM stage (P = 0.002). Low LATS2 expression and TNM stage were subsequently identified as significant independent predictors of prognosis in CRC by univariate and multivariate analyses. In Kaplan–Meier survival analyses, CRC patients with elevated LATS2 expression and early TNM stage had better overall survival. We further found that LATS2 was involved in the regulation of immune-related pathways and that its expression was positively related to tumor-infiltrating immune cells by GSEA, TIMER, and ssGSEA analyses. In summary, our data imply that LATS2 may act as a cancer suppressor gene and be correlated with clinical prognosis and immune infiltration in CRC. Thus, LATS2 may be applied as a novel biomarker for predicting clinical outcomes and immune infiltration levels in CRC.

Long Non-coding RNA Signature for Liver Metastasis of Colorectal Cancers

Colorectal cancer ranks within the top three cancers both in terms of incidence as well as deaths. Metastasis is often the major cause of mortality and liver is the primary and most common site to which colorectal cancers metastasize. We tested the prognostic ability of a long non-coding RNA (lncRNA) signature in liver metastatic colorectal cancers. We first evaluated expression levels of several lncRNAs in eight excised liver metastases from primary colorectal cancers and found significantly upregulated lncRNAs HOTAIR and MALAT1 along with significantly downregulated LOC285194. We further compared the expression levels of HOTAIR, MALAT1 and LOC285194 in primary colorectal tumors at the time of initial diagnosis and correlated them with disease progression and liver metastasis. HOTAIR and MALAT1 were significantly upregulated and LOC285194 was significantly downregulated in twelve patients who were diagnosed with liver metastasis within 5 years of initial diagnosis, compared to the five patients with no metastasis. A positive signature comprising of high HOTAIR/MALAT1 and low LOC285194 also correlated with progression to higher grade tumors. Thus, the lncRNA signature comprising of high HOTAIR/MALAT1 and low LOC285194 could be a prognostic signature for liver metastasis as well as overall poor survival.