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

Analysis of disulfidptosis- and cuproptosis-related LncRNAs in modulating the immune microenvironment and chemosensitivity in colon adenocarcinoma

The main objective was to establish a prognostic model utilising long non-coding RNAs associated with disulfidptosis and cuproptosis. The data for RNA-Sequence and clinicopathological information of Colon adenocarcinoma (COAD) were acquired from The Cancer Genome Atlas. A prognostic model was constructed using Cox regression and the Least Absolute Shrinkage and Selection Operator method. The model's predictive ability was assessed through principal component analysis, Kaplan-Meier analysis, nomogram etc. The ability of identifying the rates of overall survival, infiltration of immune cells, and chemosensitivity was also explored. In vitro experiments were conducted for the validation of differential expression and function of lncRNAs. A disulfidptosis and cuproptosis-related lncRNA prognostic model was constructed. The prognostic model exhibits excellent independent predictive capability for patient outcomes. Based on the authors' model, the high-risk group exhibited higher tumour mutation burdened worse survival. Besides, differences in immune cell infiltration and responsiveness to chemotherapeutic medications exist among patients with different risk scores. Furthermore, aberrant expressions in certain lncRNAs have been validated in HCT116 cells. In particular, FENDRR and SNHG7 could affect the proliferation and migration of colorectal cancer cells. Our study developed a novel prognostic signature, providing valuable insights into prognosis, immune infiltration, and chemosensitivity in COAD patients.

Roles, underlying mechanisms and clinical significances of LINC01503 in human cancers

Long intergenic non-coding RNA 01503 (LINC01503) is a long non-coding RNA (lncRNA) located on human chromosome 9q34.11. There is compelling evidence indicating that LINC01503 is upregulated in multiple types of tumors and functions as a tumor stimulator. The upregulation of LINC01503 was significantly associated with the risk of 12 tumors and showed a strong correlation with clinicopathological characteristics and poor prognosis in 9 tumors. The expression of LINC01503 is regulated by transcription factors such as TP63, EGR1, c-MYC, GATA1 and AR. The downstream regulatory mechanisms of LINC01503 are complex and multifaceted. LINC01503, as a competing endogenous RNA (ceRNA), regulates gene expression by competitively inhibiting miRNA. LINC01503 may also regulate gene expression via interacting with biomolecules or recruiting chromatin-modifying complexes. In addition, LINC01503 can abnormally activate the ERK/MAPK, PI3K/AKT and Wnt/β-catenin signaling pathways to enhance tumor progression. Here, this review presents an overview of the latest research progress of LINC01503 in the field of oncology, summarizes its comprehensive network involved in multiple cancer molecular mechanisms, and explores its potential applications in cancer diagnosis, prognosis, and treatment.

The potential relevance of long non-coding RNAs in colorectal cancer pathogenesis and treatment: A review focus on signaling pathways

Colorectal cancer (CRC) is one of the most frequent cancers in incidence and mortality. Despite advances in cancer biology, molecular genetics, and targeted treatments, CRC prognosis and survival have not kept pace. This is usually due to advanced staging and metastases at diagnosis. Thus, great importance has been placed upon understanding the molecular pathophysiology behind the development of CRC, which has highlighted the significance of non-coding RNA's role and associated intracellular signaling pathways in the pathogenesis of the disease. According to recent studies, long non-coding RNAs (lncRNA), a subtype of ncRNAs whose length exceeds 200 nucleotides, have been found to have regulatory functions on multiple levels. Their actions at the transcription, post-transcriptional, translational levels, and epigenetic regulation have made them prime modulators of gene expression. Due to their role in cellular cancer hallmarks, their dysregulation has been linked to several illnesses, including cancer. Furthermore, their clinical relevance has expanded due to their possible detection in blood which has cemented them as potential future biomarkers and thus, potential targets for new therapy. This review will highlight the importance of lncRNAs and related signaling pathways in the development of CRC and their subsequent clinical applications.

The molecular mechanism of actions and clinical utilities of tumor infiltrating lymphocytes in gastrointestinal cancers: a comprehensive review and future prospects toward personalized medicine

Gastrointestinal (GI) cancers remain a significant global health burden, accounting for a substantial number of cases and deaths. Regrettably, the inadequacy of dependable biomarkers hinders the precise forecasting of patient prognosis and the selection of appropriate therapeutic sequencing for individuals with GI cancers, leading to suboptimal outcomes for numerous patients. The intricate interplay between tumor-infiltrating lymphocytes (TILs) and the tumor immune microenvironment (TIME) has been shown to be a pivotal determinant of response to anti-cancer therapy and consequential clinical outcomes across a multitude of cancer types. Therefore, the assessment of TILs has garnered global interest as a promising prognostic biomarker in oncology, with the potential to improve clinical decision-making substantially. Moreover, recent discoveries in immunotherapy have progressively changed the landscape of cancer treatment and significantly prolonged the survival of patients with advanced cancers. Nonetheless, the response rate remains constrained within solid tumor sufferers, even when TIL landscapes appear comparable, which calls for the development of our understanding of cellular and molecular cross-talk between TIME and tumor. Hence, this comprehensive review encapsulates the extant literature elucidating the TILs' underlying molecular pathogenesis, prognostic significance, and their relevance in the realm of immunotherapy for patients afflicted by GI tract cancers. Within this review, we demonstrate that the type, density, and spatial distribution of distinct TIL subpopulations carries pivotal implications for the prediction of anti-cancer treatment responses and patient survival. Furthermore, this review underscores the indispensable role of TILs in modulating therapeutic responses within distinct molecular subtypes, such as those characterized by microsatellite stability or programmed cell death ligand-1 expression in GI tract cancers. The review concludes by outlining future directions in TIL-based personalized medicine, including integrating TIL-based approaches into existing treatment regimens and developing novel therapeutic strategies that exploit the unique properties of TILs and their potential as a promising avenue for personalized cancer treatment.

Identification and validation of a novel prognosis model based on m5C-related long non-coding RNAs in colorectal cancer

BACKGROUND

The prognosis of tumor patients can be assessed by measuring the levels of lncRNAs (long non-coding RNAs), which play a role in controlling the methylation of the RNA. Prognosis in individuals with colorectal adenocarcinoma (CRC) is strongly linked to lncRNA expression, making it imperative to find lncRNAs that are associated with RNA methylation with strong prognostic value.

METHODS

In this study, by analyzing TCGA dataset, we were able to develop a risk model for lncRNAs that are associated with m5C with prognostic significance by employing LASSO regression and univariate Cox proportional analysis. There were a number of methods employed to ensure the model was accurate, including multivariate and univariate Cox regression analysis, Kaplan analysis, and receiver operating characteristic curve analysis. The principal component analysis, GSEA and GSVA analysis were used for risk model analysis. The CIBERSORT instrument and the TIMER database were used to evaluate the link between the immune cells that infiltrate tumors and the risk model. In vitro experiments were also performed to validate the predicted m5C-related significant lncRNAs.

RESULTS

The m5c regulators were differentially expressed in colorectal cancer and normal tissue. Based on the screening criteria and LASSO regression, 11 m5c-related lncRNAs were identified for developing the prognostic risk model. Multivariate and univariate Cox regression analysis showed the risk score is a crucial prognostic factor in CRC patients. The 1-year, 3-year, and 5-year AUC curves showed the risk score was higher than those identified for other clinicopathological characteristics. A nomogram using the risk score as a quantitative tool was developed for predicting patients' outcomes in clinical settings. In addition, the risk profile of m5C-associated lncRNAs can discriminate between tumor immune cells' characteristics in CRC. Mutation patterns and chemotherapy were analyzed between high- and low- risk groups of CRC patients. Moreover, TNFRSF10A-AS1 was chosen for the in vitro verification of the m5C-connected lncRNA to demonstrate impressive effects on the proliferation, migration and invasion of CRC cells.

CONCLUSION

A risk model including the prognostic value of 11 m5C-associated lncRNAs proves to be a useful prognostic tool for CRC and improves the care of patients suffering from CRC based on these findings.

Pharmacological mechanism of quercetin in the treatment of colorectal cancer by network pharmacology and molecular simulation

Colorectal cancer is a serious threat to people's life due to its high incidence and high mortality. Quercetin can effectively treat colorectal carcinoma (CRC), but its exact mechanism of action is still unclear. Then quercetin-related target genes were obtained from Swiss Target Prediction database and Similarity Ensemble Approach (SEA) database, and CRC-related target genes were obtained from GeneCards database, respectively. Common target genes were obtained by FunRich software. String software was used to construct a protein-protein interaction (PPI) network. R package was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Molecular docking, molecular dynamics (MD) simulation and post-dynamics simulation were used to explore the binding stability of quercetin to key targets. In total, 103 and 141 target information of quercetin were obtained from the Swiss Target Prediction database and SEA database, respectively. 1,649 CRC-related genes were obtained from GeneCards database. FunRich software was used to draw venny map and obtain 36 intersection targets of quercetin and CRC. String software was used to construct the PPI network. The core genes were AKT1, EGFR, MMP9, KDR, MET and PTK2. There were 532 items related to biological processes, 14 items related to cellular components, and 43 items related to molecular functions among the key target GO enrichment items. KEGG enrichment pathways of key targets involved cancer pathways, PI3K-Akt signal pathway, etc. The results of molecular docking, MD simulation and post-dynamics simulation showed they had a good affinity and formed a stable effect. So quercetin may play an important role in the treatment of CRC by acting on AKT1, EGFR, MMP9, KDR, MET and PTK2 to affect the development of CRC.Communicated by Ramaswamy H. Sarma.

Constructing a novel signature and predicting the immune landscape of colon cancer using N6-methylandenosine-related lncRNAs

Background: Colon cancer (CC) is a prevalent malignant tumor that affects people all around the world. In this study, N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) in 473 colon cancers and 41 adjacent tissues of CC patients from The Cancer Genome Atlas (TCGA) were investigated. Method: The Pearson correlation analysis was conducted to examine the m6A-related lncRNAs, and the univariate Cox regression analysis was performed to screen 38 prognostic m6A-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression analysis were carried out on 38 prognostic lncRNAs to develop a 14 m6A-related lncRNAs prognostic signature (m6A-LPS) in CC. The availability of the m6A-LPS was evaluated using the Kaplan-Meier and Receiver Operating Characteristic (ROC) curves. Results: Three m6A modification patterns with significantly different N stages, survival time, and immune landscapes were identified. It has been discovered that the m6A-LPS, which is based on 14 m6A-related lncRNAs (TNFRSF10A-AS1, AC245041.1, AL513550.1, UTAT33, SNHG26, AC092944.1, ITGB1-DT, AL138921.1, AC099850.3, NCBP2-AS1, AL137782.1, AC073896.3, AP006621.2, AC147651.1), may represent a new, promising biomarker with great potential. It was re-evaluated in terms of survival rate, clinical features, tumor infiltration immune cells, biomarkers related to Immune Checkpoint Inhibitors (ICIs), and chemotherapeutic drug efficacy. The m6A-LPS has been revealed to be a novel potential and promising predictor for evaluating the prognosis of CC patients. Conclusion: This study revealed that the risk signature is a promising predictive indicator that may provide more accurate clinical applications in CC therapeutics and enable effective therapy strategies for clinicians.

A novel long noncoding RNA AC125257.1 facilitates colorectal cancer progression by targeting miR-133a-3p/CASC5 axis

Colorectal cancer (CRC) is a common malignant gastrointestinal tumor. Long noncoding RNAs (lncRNAs) are revealed to be critically involved in CRC progression, providing new direction for exploring the pathogenesis of CRC. This study aimed to explore the biological functions and regulatory mechanisms of lncRNA AC125257.1 in CRC. Western blotting and reverse-transcription quantitative polymerase chain reaction were used for the measurement of gene expression. Cell counting kit-8 assay and flow cytometry analysis were used to explore the effects of AC125257.1 on CRC cell viability and apoptosis. RNA pull-down and immunoprecipitation assays were performed for validating the binding between AC125257.1 and its potential downstream microRNA. Results showed that lncRNA AC125257.1 expression was upregulated in CRC cells and tumor tissues. AC125257.1 enhanced cell viability and suppressed apoptosis of CRC cells. Moreover, the knockdown of AC125257.1 suppressed CRC progression in vitro and inhibited tumor growth in vivo. miR-133a-3p was revealed to bind with AC125257.1 in CRC cells. CASC5 was proved to be targeted by miR-133a-3p. Moreover, rescue assays indicated that the knockdown of AC125257.1 suppressed the pathogenic overexpression of CASC5. To conclude, AC125257.1 aggravates CRC development via miR-873-5p/CASC5 axis. Our findings might suggest a novel perspective that AC125257.1 may become the target for CRC treatment.

Construction and validation of a novel algorithm based on oncosis-related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis

Colorectal cancer (CRC) has high morbidity and mortality, particularly if diagnosed at an advanced stage. Although there have been several studies on CRC, few have investigated the relationship between oncosis and CRC. Thus, the purpose of the present study was to identify oncosis-related long noncoding RNAs (lncRNAs) and to establish a clinical prognostic model. Original data were acquired from The Cancer Genome Atlas database and PubMed. Differentially expressed oncosis-related lncRNAs (DEorlncRNAs) were identified and were subsequently formed into pairs. Next, a series of tests and analyses, including both univariate and multivariate analyses, as well as Lasso and Cox regression analyses, were performed to establish a receiver operating characteristic curve. A cut-off point was subsequently used to divide the samples into groups labelled as high- or low-risk. Thus, a model was established and evaluated in several dimensions. Six pairs of DEorlncRNAs associated with prognosis according to the algorithm were screened out and the CRC cases were divided into high- and low-risk groups. Significant differences between patients in the different risk groups were observed for several traits, including survival outcomes, clinical pathology characteristics, immune cell infiltration status and drug sensitivity. In addition, PCR and flow cytometry were performed to further verify the model. In summary, a new risk model algorithm based on six pairs of DEorlncRNAs in CRC, which does not require specific data regarding the level of gene expression, was established and validated. This algorithm may be used to predict patient prognosis, immune cell infiltration and drug sensitivity.

Integrated Analysis of a Competing Endogenous RNA Network Reveals a Ferroptosis-related 6-LncRNA Prognostic Signature in Clear Cell Renal Cell Carcinoma.. [DOI: 10.21203/rs.3.rs-2294111/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background: This study set out to elucidate the biological functions and prognostic role of ferroptosis-related lncRNAs based on a synthetic analysis of competing endogenous RNA (ceRNA) networks in clear cell renal cell carcinoma (ccRCC). Methods: Ferroptosis-related genes were obtained from the FerrDb database. The expression data and matched clinical information of lncRNAs, miRNAs, and mRNAs from The Cancer Genome Atlas (TCGA) database were obtained to identify differentially expressed RNAs (DERNAs). The lncRNA-miRNA-mRNA ceRNA network was established utilizing the common miRNAs that were predicted in the RNAHybrid, StarBase, and TargetScan databases. Then, by progressive univariate Cox regression, LASSO,and multivariate Cox regression analysis of gene expression data and clinical information, a ferroptosis-related lncRNA prognosis signature was constructed based on the lncRNAs in ceRNA. Finally, the influence of independent lncRNAs on ccRCC was explored through a series of functional and unsupervised cluster analysis. Results: A total of 35 ferroptosis-related DEmRNAs, 356 DElncRNAs, and 132 DEmiRNAs were sorted out from the KIRC cohort of TCGA database. Overlapping DElncRNA-DEmiRNA and DEmiRNA-DEmRNA interactions among the RNAHybrid, StarBase, and TargetScan databases were constructed and identified, then a ceRNA network with 77 axes related to ferroptosis was established utilizing mutual DEmiRNAs in two interaction networks as nodes. Through synthetic analysis of the expression data and clinical information of 27 lncRNAs in the ceRNA network, a 6-ferroptosis-lncRNA signature including PVT1, CYTOR, MIAT, SNHG17, LINC00265, and LINC00894 was identified in the training set. Kaplan-Meier, PCA, t-SNE analysis, risk score curve, and ROC curve were performed to confirm the validity of the signature in the training set and secondly verified in the validation set. Finally, ssGSEA and ESTIMATE analysis showed that the signature was related with immune cell infiltration and could predict immune-related phenotypes. Conclusions: Our research underlines the role of the 6-ferroptosis-lncRNA signature as a predictor of prognosis and a therapeutic alternative for KIRC.

Construction and validation of a glycolysis-related lncRNA signature for prognosis prediction in Stomach Adenocarcinoma

Background: Glycolysis is closely related to the occurrence and progression of gastric cancer (GC). Currently, there is no systematic study on using the glycolysis-related long non-coding RNA (lncRNA) as a model for predicting the survival time in patients with GC. Therefore, it was essential to develop a signature for predicting the survival based on glycolysis-related lncRNA in patients with GC. Materials and methods: LncRNA expression profiles, containing 375 stomach adenocarcinoma (STAD) samples, were obtained from The Cancer Genome Atlas (TCGA) database. The co-expression network of lncRNA and glycolysis-related genes was used to identify the glycolysis-related lncRNAs. The Kaplan-Meier survival analysis and univariate Cox regression analysis were used to detect the glycolysis-related lncRNA with prognostic significance. Then, Bayesian Lasso-logistic and multivariate Cox regression analyses were performed to screen the glycolysis-related lncRNA with independent prognostic significance and to develop the risk model. Patients were assigned into the low- and high-risk cohorts according to their risk scores. A nomogram model was constructed based on clinical information and risk scores. Gene Set Enrichment Analysis (GSEA) was performed to visualize the functional and pathway enrichment analyses of the glycolysis-related lncRNA. Finally, the robustness of the results obtained was verified in an internal validation data set. Results: Seven glycolysis-related lncRNAs (AL353804.1, AC010719.1, TNFRSF10A-AS1, AC005586.1, AL355574.1, AC009948.1, and AL161785.1) were obtained to construct a risk model for prognosis prediction in the STAD patients using Lasso regression and multivariate Cox regression analyses. The risk score was identified as an independent prognostic factor for the patients with STAD [HR = 1.315, 95% CI (1.056-1.130), p < 0.001] via multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were drawn and the area under curve (AUC) values of 1-, 3-, and 5-year overall survival (OS) were calculated to be 0.691, 0.717, and 0.723 respectively. Similar results were obtained in the validation data set. In addition, seven glycolysis-related lncRNAs were significantly enriched in the classical tumor processes and pathways including cell adhesion, positive regulation of vascular endothelial growth factor, leukocyte transendothelial migration, and JAK_STAT signaling pathway. Conclusion: The prognostic prediction model constructed using seven glycolysis-related lncRNA could be used to predict the prognosis in patients with STAD, which might help clinicians in the clinical treatment for STAD.

Construction of a prognostic model of colon cancer patients based on metabolism-related lncRNAs

Background

Many studies have shown that metabolism-related lncRNAs may play an important role in the pathogenesis of colon cancer. In this study, a prognostic model for colon cancer patients was constructed based on metabolism-related lncRNAs.

Methods

Both transcriptome data and clinical data of colon cancer patients were downloaded from the TCGA database, and metabolism-related genes were downloaded from the GSEA database. Through differential expression analysis and Pearson correlation analysis, long non-coding RNAs (lncRNAs) related to colon cancer metabolism were obtained. CRC patients were divided into training set and verification set at the ratio of 2:1. Based on the training set, univariate Cox regression analysis was utilized to determine the prognostic differential expression of metabolic-related lncRNAs. The Optimal lncRNAs were obtain by Lasso regression analysis, and a risk model was built to predict the prognosis of CRC patients. Meanwhile, patients were divided into high-risk and low-risk groups and a survival curve was drawn accordingly to determine whether the survival rate differs between the two groups. At the same time, subgroup analysis evaluated the predictive performance of the model. We combined clinical indicators with independent prognostic significance and risk scores to construct a nomogram. C index and the calibration curve, DCA clinical decision curve and ROC curve were obtained as well. The above results were all verified using the validation set. Finally, based on the CIBERSORT analysis method, the correlation between lncRNAs and 22 tumor-infiltrated lymphocytes was explored.

Results

By difference analysis, 2491 differential lncRNAs were obtained, of which 226 were metabolic-related lncRNAs. Based on Cox regression analysis and Lasso results, a multi-factor prognostic risk prediction model with 13 lncRNAs was constructed. Survival curve results suggested that patients with high scores and have a poorer prognosis than patients with low scores (P<0.05). The area under the ROC curve (AUC) for the 3-year survival and 5-year survival were 0.768 and 0.735, respectively. Cox regression analysis showed that age, distant metastasis and risk scores can be used as independent prognostic factors. Then, a nomogram including age, distant metastasis and risk scores was built. The C index was 0.743, and the ROC curve was drawn to obtain the AUC of the 3-year survival and the 5-year survival, which were 0.802 and 0.832, respectively. The above results indicated that the nomogram has a good predictive effect. Enrichment analysis of KEGG pathway revealed that differential lncRNAs may be related to chemokines, amino acid and sugar metabolism, NOD-like receptor and Toll-like receptor activation as well as other pathways. Finally, the analysis results based on the CIBERSORT algorithm showed that the lncRNAs used to construct the model had a strong polarized correlation with B cells, CD8+T cells and M0 macrophages.

Conclusion

13 metabolic-related lncRNAs affecting the prognosis of CRC were screened by bioinformatics methods, and a prognostic risk model was constructed, laying a solid foundation for the research of metabolic-related lncRNAs in CRC.

Comprehensive analysis of the prognostic signature and tumor microenvironment infiltration characteristics of cuproptosis-related lncRNAs for patients with colon adenocarcinoma

Background

Cuproptosis, a newly described method of regulatory cell death (RCD), may be a viable new therapy option for cancers. Long noncoding RNAs (lncRNAs) have been confirmed to be correlated with epigenetic controllers and regulate histone protein modification or DNA methylation during gene transcription. The roles of cuproptosis-related lncRNAs (CRLs) in Colon adenocarcinoma (COAD), however, remain unknown.

Methods

COAD transcriptome data was obtained from the TCGA database. Thirteen genes associated to cuproptosis were identified in published papers. Following that, correlation analysis was used to identify CRLs. The cuproptosis associated prognostic signature was built and evaluated using Lasso regression and COX regression analysis. A prognostic signature comprising six CRLs was established and the expression patterns of these CRLs were analyzed by qRT-PCR. To assess the clinical utility of prognostic signature, we performed tumor microenvironment (TME) analysis, mutation analysis, nomogram generation, and medication sensitivity analysis.

Results

We identified 49 prognosis-related CRLs in COAD and constructed a prognostic signature consisting of six CRLs. Each patient can be calculated for a risk score and the calculation formula is: Risk score =TNFRSF10A-AS1 * (-0.2449) + AC006449.3 * 1.407 + AC093382.1 *1.812 + AC099850.3 * (-0.0899) + ZEB1-AS1 * 0.4332 + NIFK-AS1 * 0.3956. Six CRLs expressions were investigated by qRT-PCR in three colorectal cancer cell lines. In three cohorts, COAD patients were identified with different risk groups, with the high-risk group having a worse prognosis than the low-risk group. Furthermore, there were differences in immune cell infiltration and tumor mutation burden (TMB) between the two risk groups. We also identified certain drugs that were more sensitive to the high-risk group: Paclitaxel, Vinblastine, Sunitinib and Elescloml.

Conclusions

Our findings may be used to further investigate RCD, comprehension of the prognosis and tumor microenvironment infiltration characteristics in COAD.

Construction and Validation of Pyroptosis-Related lncRNA Prediction Model for Colon Adenocarcinoma and Immune Infiltration Analysis

Objective

Colon adenocarcinoma (COAD) is one of the most prevalent cancers worldwide. However, the pyroptosis-related lncRNAs of COAD have not been deeply examined and validated. Here, we constructed and validated a risk model on pyroptosis-related lncRNAs in COAD.

Methods

The RNA sequencing transcriptome and clinical data of COAD patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed pyroptosis-related mRNAs and mRNA-lncRNA coexpression network were identified. After univariate and multifactorial cox analyses of prognosis-related lncRNAs, a risk model was constructed. Next, we analyzed the differences in immune infiltration, immune checkpoint blockade-, immune checkpoint-, and N6-methyladenosine-related gene expressions between the high- and low-risk groups. RT-qPCR was used to validate the expression of lncRNAs.

Result

A risk model was constructed based on 9 pyroptosis-related lncRNAs and separated COAD patients into the high- and low-risk groups. Immune infiltration analysis and immune checkpoint blockade-, immune checkpoint-, and N6-methyladenosine-related genes showed significant differences between the two subgroups. RT-qPCR showed that the 9 pyroptosis-related lncRNAs could be used as prognostic indicators.

Conclusion

A novel risk model based on pyroptosis-related lncRNAs was constructed and demonstrated that these lncRNAs might be used as independent prognostic biomarkers. This will also assist shed light on the COAD prognosis and therapy.

A novel prognostic signature based on cuproptosis-related lncRNA mining in colorectal cancer

Background: Colorectal cancer (CRC) is a common malignant tumor that affects the large bowel or the rectum. Cuproptosis, recently discovered programmed cell death process, may play an important role in CRC tumorigenesis. Long non-coding RNAs (lncRNAs) can alter the proliferation of colorectal cancer cells through the control and activation of gene expression. To date, cuproptosis-related lncRNAs, have not been investigated as potential predictive biomarkers in colorectal cancer.

Methods: The mRNA and lncRNA expression data of colorectal cancer were gathered from The Tumor Genome Atlas (TCGA) database, and Pearson correlation analysis and univariate Cox regression analysis were used to identify the lncRNAs with differential prognosis. Colorectal cancer was classified using consistent clustering, and the clinical significance of different types, tumor heterogeneity, and immune microenvironment differences was investigated. The differential lncRNAs were further screened using LASSO regression to develop a risk scoring model, which was then paired with clinicopathological variables to create a nomogram. Finally, the copy number changes in the high-risk and low-risk groups were compared.

Results: Two clusters were formed based on the 28 prognostic cuproptosis-related lncRNAs, and the prognosis of cluster 2 was found to be significantly lower than that of cluster 1. Cluster 1 showed increased immune cell infiltration and immunological score, as well as strong enrichment of immune checkpoint genes. Next, LASSO regression was used to select 11 distinctive lncRNAs, and a risk score model was constructed using the training set to distinguish between high and low-risk groups. Patients in the high-risk group had a lower survival rate than those in the low-risk group, and both the test set and the total set produced consistent results. The AUC value of the ROC curve revealed the scoring model’s efficacy in predicting long-term OS in patients. Moreover, the model could be used as an independent predictor when combined with a multivariate analysis of clinicopathological features, and our nomogram could be used intuitively to predict prognosis.

Conclusion: Collectively, we developed a risk model using 11 differential lncRNAs and demonstrated that the model has predictive value as well as clinical and therapeutic implications for predicting prognosis in CRC patients.

Identification of Autophagy-Related LncRNA to Predict the Prognosis of Colorectal Cancer

Objective: To establish a prediction model based on autophagy-related lncRNAs and investigate the functional enrichment of autophagy-related lncRNAs in colorectal cancer.

Methods: TCGA database was used to extract the transcriptome data and clinical features of colorectal cancer patients. HADb was used to obtain autophagy-related genes. Pearson correlation analysis was performed to identify autophagy-related lncRNAs. The autophagy-related lncRNAs with prognostic values were selected. Based on the selected lncRNAs, the risk score model and nomogram were constructed, respectively. Calibration curve, concordance index, and ROC curve were performed to evaluate the predictive efficacy of the prediction model. GSEA was performed to figure out the functional enrichment of autophagy-related lncRNAs.

Results: A total of 13413 lncRNAs and 938 autophagy-related genes were obtained. A total of 709 autophagy-related genes were identified in colon cancer tissues, and 11 autophagy-related lncRNAs (AL138756.1, LINC01063, CD27-AS1, LINC00957, EIF3J-DT, LINC02474, SNHG16, AC105219.1, AC068580.3, LINC02381, and LINC01011) were finally selected and set as prognosis-related lncRNAs. According to the risk score, patients were divided into the high-risk and low-risk groups, respectively. The survival K–M (Kaplan–Meier) curve showed the low-risk group exhibits better overall survival than the high-risk group. The AUCs under the ROC curves were 0.72, 0.814, and 0.83 at 1, 3, and 5 years, respectively. The C-index (concordance index) of the model was 0.814. The calibration curves at 1, 3, and 5 years showed the predicting values were consistent with the actual values. Functional enrichment analysis showed that autophagy-related lncRNAs were enriched in several pathways.

Conclusions: A total of 11 specific autophagy-related lncRNAs were identified to own prognostic value in colon cancer. The predicting model based on the lncRNAs and clinical features can effectively predict the OS. Furthermore, functional enrichment analysis showed that autophagy-related genes were enriched in various biological pathways.

Emerging role of lncRNAs in drug resistance mechanisms in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) originates in the squamous cell lining the mucosal surfaces of the head and neck region, including the oral cavity, nasopharynx, tonsils, oropharynx, larynx, and hypopharynx. The heterogeneity, anatomical, and functional characteristics of the patient make the HNSCC a complex and difficult-to-treat disease, leading to a poor survival rate and a decreased quality of life due to the loss of important physiologic functions and aggressive surgical injury. Alteration of driver-oncogenic and tumor-suppressing lncRNAs has recently been recently in HNSCC to obtain possible biomarkers for diagnostic, prognostic, and therapeutic approaches. This review provides current knowledge about the implication of lncRNAs in drug resistance mechanisms in HNSCC. Chemotherapy resistance is a major therapeutic challenge in HNSCC in which lncRNAs are implicated. Lately, it has been shown that lncRNAs involved in autophagy induced by chemotherapy and epithelial–mesenchymal transition (EMT) can act as mechanisms of resistance to anticancer drugs. Conversely, lncRNAs involved in mesenchymal–epithelial transition (MET) are related to chemosensitivity and inhibition of invasiveness of drug-resistant cells. In this regard, long non-coding RNAs (lncRNAs) play a pivotal role in both processes and are important for cancer detection, progression, diagnosis, therapy response, and prognostic values. As the involvement of more lncRNAs is elucidated in chemoresistance mechanisms, an improvement in diagnostic and prognostic tools could promote an advance in targeted and specific therapies in precision oncology.

Construction and validation of a cuproptosis-related lncRNA signature as a novel and robust prognostic model for colon adenocarcinoma

Background

Cuproptosis, a newly identified form of programmed cell death, is thought to play a role in tumorigenesis. Long non-coding RNAs (lncRNAs) are reported to be associated with tumor progression and prognosis in colon adenocarcinoma (COAD). However, the role and prognostic value of cuproptosis-related lncRNAs in COAD remains unknown. This study is devoted to constructing and validating a cuproptosis-related lncRNA signature that can predict COAD patient outcomes using bioinformatics methods.

Methods

The COAD mRNA and lncRNA expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database and 2,567 cuproptosis-related lncRNAs were obtained. A 10 cuproptosis-related-lncRNA prognostic signature was then constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression model and patients were divided into high- and low-risk groups. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a nomogram were employed to evaluate the predictive power of the signature. The immune characteristics and drug sensitivity were also investigated based on the signature. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to verify the risk model. In vitro experiments were conducted to validate the expression of the ten lncRNAs during cuproptosis.

Results

The high-risk group was associated with shorter overall survival (OS) time in COAD patients (p&lt;0.001). Multivariate Cox regression indicated that a high-risk score was an independent risk factor for poor prognosis (p&lt;0.001). ROC curve analysis was performed to confirm the validity of the signature (area under the curve (AUC) at 3 years: 0.879). Gene Ontology (GO) enrichment analysis revealed that the signature was highly correlated with the immune response in biological processes. The immune function, the score of the immune cells, and the expression of immune checkpoints were significantly different between the two risk groups. Three drugs, LAQ824, FH535, YM155, were found to be more sensitive in the high-risk group. Finally, the expression levels of the ten lncRNAs comprising the signature were tested by qRT-PCR.

Conclusion

A ten-cuproptosis-related lncRNA signature was constructed that provided promising insights into personalized prognosis and drug selection among COAD patients.

A novel cuproptosis-related prognostic lncRNA signature and lncRNA MIR31HG/miR-193a-3p/TNFRSF21 regulatory axis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) remains the most common subtype of lung malignancy. Cuproptosis is a newly identified cell death which could regulate tumor cell proliferation and progression. Long non-coding RNAs (lncRNAs) are key molecules and potential biomarkers for diagnosing and treating various diseases. However, the effects of cuproptosis-related lncRNAs on LUAD are still unclear. In our study, 7 cuproptosis-related lncRNAs were selected to establish a prognostic model using univariate Cox regression analysis, LASSO algorithm, and multivariate analysis. Furthermore, we evaluated AC008764.2, AL022323.1, ELN-AS1, and LINC00578, which were identified as protective lncRNAs, while AL031667.3, AL606489.1, and MIR31HG were identified as risk lncRNAs. The risk score calculated by the prognostic model proved to be an effective independent factor compared with other clinical features by Cox regression analyses [univariate analysis: hazard ratio (HR) = 1.065, 95% confidence interval (CI) = 1.043–1.087, P < 0.001; multivariate analysis: HR = 1.067, 95% CI = 1.044–1.091, P < 0.001]. In addition, both analyses (ROC and nomogram) were used to corroborate the accuracy and reliability of this signature. The correlation between cuproptosis-related lncRNAs and immune microenvironment was elucidated, where 7 immune cells and 8 immune-correlated pathways were found to be differentially expressed between two risk groups. Furthermore, our results also identified and verified the ceRNA of cuproptosis-related lncRNA MIR31HG/miR-193a-3p/TNFRSF21 regulatory axis using bioinformatics tools. MIR31HG was highly expressed in LUAD specimens and some LUAD cell lines. Inhibition of MIR31HG clearly reduced the proliferation, migration, and invasion of the LUAD cells. MIR31HG showed oncogenic features via sponging miR-193a-3p and tended to positively regulate TNFRSF21 expression. In a word, lncRNA MIR31HG acts as an oncogene in LUAD by targeting miR-193a-3p to modulate TNFRSF21, which may be beneficial to the gene therapy of LUAD.

Immune-Related LncRNAs to Construct a Prognosis Risk-Assessment Model for Gastric Cancer

Background: Gastric cancer is a prevalent cause of tumor death. Tumor immunotherapy aims to reshape the specific immunity to tumors in order to kill the tumor. LncRNAs play a pivotal role in regulating the tumor immune microenvironment. Herein, immune-related lncRNAs were used to establish a prognosis risk-assessment model for gastric cancer and provide personalized predictions while providing insights and targets for gastric cancer treatment to enhance patient prognosis. Methods: Gastric adenocarcinoma transcriptome and clinical data were acquired from the The Cancer Genome Atlas (TCGA) database to screen the immune-related lncRNAs. Then, LASSO COX regression was utilized to construct the prognosis risk-assessment model. Afterward, the reliability of the model was evaluated the relationship between immune infiltration, clinical characteristics, and the model was analyzed. Results: We identified 13 lncRNAs and constructed the prognosis assessment model. According to the median risk score of the training set, the patients were assigned to different risk groups. Overall survival time was shorter in the high-risk group. In the high-risk group, higher infiltration of mono-macrophages, dendritic cells, CD4+ T cells, and CD8+ T cells was observed. Moreover, the model was positively related to tumor metastasis. Conclusion: The prognosis risk-assessment model developed in this research can effectively predict the prognosis of gastric cancer patients. This tool is expected to be further applied to clinics in the future, thus providing a novel target for immunotherapy in gastric cancer patients.

Lactate Metabolism-Associated lncRNA Pairs: A Prognostic Signature to Reveal the Immunological Landscape and Mediate Therapeutic Response in Patients With Colon Adenocarcinoma

Background

Lactate metabolism is critically involved in the tumor microenvironment (TME), as well as cancer progression. It is important to note, however, that lactate metabolism-related long non-coding RNAs (laRlncRNAs) remain incredibly understudied in colon adenocarcinoma (COAD).

Methods

A gene expression profile was obtained from the Cancer Genome Atlas (TCGA) database to identify laRlncRNA expression in COAD patients. A risk signature with prognostic value was identified from TCGA and Gene Expression Omnibus (GEO) cohort based on laRlncRNA pairs by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were carried out to verify the expression of laRlncRNAs in COAD. The relationship of laRlncRNA pairs with immune landscape as well as the sensitivity of different therapies was explored.

Results

In total, 2378 laRlncRNAs were identified, 1,120 pairs of which were studied to determine their prognostic validity, followed by a risk signature established based on the screened 5 laRlncRNA pairs. The laRlncRNA pairs-based signature provided a better overall survival (OS) prediction than other published signatures and functioned as a prognostic marker for COAD patients. According to the calculated optimal cut-off point, patients were divided into high- and low-risk groups. The OS of COAD patients in the high-risk group were significantly shorter than that of those in the low-risk group (P=4.252e-14 in the TCGA cohort and P=2.865-02 in the GEO cohort). Furthermore, it remained an effective predictor of survival in strata of gender, age, TNM stage, and its significance persisted after univariate and multivariate Cox regressions. Additionally, the risk signature was significantly correlated with immune cells infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) as well as immunotherapeutic efficacy and chemotherapy sensitivity. Finally, one of the laRlncRNA, LINC01315, promotes proliferation and migration capacities of colon cancer cells.

Conclusion

The newly identified laRlncRNAs pairs-based signature exhibits potential effects in predicting prognosis, deciphering patients’ immune landscape, and mediating sensitivity to immunotherapy and chemotherapy. Findings in our study may provide evidence for the role of laRlncRNAs pairs as novel prognostic biomarkers and potentially individualized therapy targets for COAD patients.

Integrated Analysis of a Ferroptosis-Related LncRNA Signature for Evaluating the Prognosis of Patients with Colorectal Cancer

LncRNAs have been well known for their multiple functions in the tumorigenesis, development, and relapse of colorectal cancer (CRC). Accumulating studies demonstrated that the expression of lncRNAs can be regulated by ferroptosis, a biological process that has been revealed to suppress CRC progression. However, the functions and clinical implications of ferroptosis-associated lncRNAs in CRC remain largely unknown. We, herein, aim to construct a prognostic signature with ferroptosis-related lncRNAs for the prognostic estimation of CRC patients. Firstly, we identified the lncRNAs related to ferroptosis based on the RNA-Seq data of CRC from the TCGA database. The univariate and multivariate Cox analyses were then performed to establish a prognostic signature composed of eight ferroptosis-related lncRNAs (AL161729.4, AC010973.2, CCDC144NL-AS1, AC009549.1, LINC01857, AP003555.1, AC099850.3, and AC008494.3). Furthermore, we divided the CRC patients into high- and low-risk groups based on the signature and found the overall survival (OS) of patients in the high-risk group was significantly shorter than that in the low-risk group (p = 3.31 × 10-11). Moreover, the patients in the high-risk groups had shorter recurrence-free survival (RFS) (p = 6.5 × 10-3) and disease-free survival (DFS) (p = 4.27 × 10-4), as well as higher tumor recurrence rate. Additionally, we found that the oncogenic pathways were enriched in the high-risk group, whereas the ferroptosis pathway that probably repressed CRC development was enriched in the low-risk group. In summary, our signature may provide a theoretical foundation for not only accurate judgment for prognosis but also evaluation for recurrence and metastasis in CRC patients.

Long noncoding RNA TNFRSF10A-AS1 promotes colorectal cancer through upregulation of HuR

BACKGROUND

Recent studies have emphasized the emerging importance of long noncoding RNAs (lncRNAs) in colorectal cancer (CRC). However, the functions and regulatory mechanisms of numerous lncRNAs in CRC have not been fully elucidated.

AIM

To explore the functional role and underlying molecular mechanisms of lncRNA TNFRSF10A-AS1 in CRC.

METHODS

TNFRSF10A-AS1 expression was measured by quantitative real-time polymerase chain reaction in CRC, and the relationship between TNFRSF10A-AS1 levels and the clinicopathological features of CRC patients was analyzed. The effect of TNFRSF10A-AS1 expression on CRC proliferation and metastasis was examined in vitro and in vivo. Mechanistically, we investigated how TNFRSF10A-AS1 is involved in CRC as a competitive endogenous RNA.

RESULTS

TNFRSF10A-AS1 was expressed at a high level in CRC and the upregulation of TNFRSF10A-AS1 was associated with advanced T grade and tumor size in CRC patients. A functional investigation revealed that TNFRSF10A-AS1 enhanced the proliferation, migration ability and invasion ability of colon cancer cells in vitro and in vivo. A mechanistic analysis demonstrated that TNFRSF10A-AS1 acted as a miR-3121-3p molecular sponge to regulate HuR expression, ultimately promoting colorectal tumorigenesis and progression.

CONCLUSION

TNFRSF10A-AS1 exerts a tumor-promoting function through the miR-3121-3p/HuR axis in CRC, indicating that it may be a novel target for CRC therapy.

Non-coding RNAs associated with autophagy and their regulatory role in cancer therapeutics

Cancer widely affects the world's health population and ranks second leading cause of death globally. Because of poor prognosis of various types of cancer such as sarcoma, lymphoma, adenomas etc., their high recurrence and metastasis rate and low early diagnosis rate have become concern lately. Role of autophagy in cancer progression is being studied since long. Autophagy is cell's self-degradative mechanism towards stress and has role in degradation of the cytoplasmic macromolecules which has potential to damage other cytosolic molecules. Autophagy can promote as well as inhibit tumorigenesis depending upon the associated protein combinations in cancer cells. Recent studies have shown that non-coding RNAs (ncRNAs) do not code for protein but play essential role in modulation of gene expression. At transcriptional level, different ncRNAs like lncRNAs, miRNAs and circRNAs directly or indirectly affect different stages of autophagy like autophagy-dependent and non-apoptotic cell death in cancer cells. This review focuses on the involvement of ncRNAs in autophagy and the modulation of several cancer signal transduction pathways in cancers such as lung, breast, prostate, pancreatic, thyroid, and kidney cancer.

A novel risk score model based on five angiogenesis-related long non-coding RNAs for bladder urothelial carcinoma

Background

Tumour angiogenesis is an independent risk factor for bladder urothelial carcinoma (BUC) progression, but viable and promising antiangiogenic targets are understudied. Emerging evidence suggests that long non-coding RNAs (lncRNAs) play prominent role in the tumour microenvironment and tumour angiogenesis.

Methods

The clinical data of BUC patients were obtained from TCGA database and clinical specimens of 138 BUC patients. Univariate and multivariate COX regression analyses were used to identify survival-related ARLNRs (sARLNRs) from The Molecular Signatures Database v4.0. Fisher’s exact probability method was used to detect the correlations between sARLNRs levels and clinicopathological characteristics. A chain of experiments including FACS, qPCR, immunohistochemistry, tube formation, migration and invasion assays, combining with co-culture models, were utilized to validate the clinical significance and angiogenetic correlation of sARLNRs.

Results

Five sARLNRs were employed to establish an angiogenesis-related risk score model, by which patients in the low-risk group obtained better overall survival than those in the high-risk group. The expression of AC005625.1 and AC008760.1 was significantly related to ECs percentage, tumour size and muscle invasion status. Besides, AC005625.1 and AC008760.1 expressed lower in BUC cell lines and tumour tissues than that in normal urothelial cells and adjacent normal tissues, with much lower levels in more advanced T stages. A prominently higher proportion of ECs was detected in tumour tissues with lower expression of AC005625.1 and AC008760.1. In the co-culture models, we found that knockdown of AC005625.1 and AC008760.1 in BUC cells increased the tube formation, migration and invasion abilities of HUVEC. The expression levels of CD31, VEGF-A, VIMENTIN and N-CADHERIN were also enhanced in HUVEC cells co-cultured with siR-AC005625.1 and siR-AC008760.1-treated T24 cells.

Conclusion

In the study, we identify five sARLNRs and validate their clinical significance, angiogenesis correlation and prognosis-predictive values in BUC. These findings may provide a new perspective and some promising antiangiogenic targets for clinical diagnosis and treatment strategies of BUC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02575-1.

Establishment and validation of a prognostic model based on HRR-related lncRNAs in colon adenocarcinoma

BACKGROUND

Colon cancer (CRC) is the second leading cause of cancer-related death, and its 5-year survival rate is very low. Homologous recombination repair (HRR) is deficient in most colon cancer. Some long non-coding RNAs (lncRNAs) participate in tumorigenesis of colon cancer through the HRR pathway. We aim to establish a prognostic model based on the HRR-related lncRNAs, expecting to provide a new strategy for precision treatment development in colon cancer.

METHODS

Pearson's correlation was used to identify the HRR-related prognostic lncRNAs in the TCGA-COAD cohort. The TCGA-COAD cohort was randomized into the training set and the testing set. LASSO Cox regression was used to establish the model which was analyzed in the training set and validated in the testing set and the entire TCGA-COAD cohort. Finally, we explored the potential biological function of our model.

RESULTS

A prognostic model was established based on nineteen HRR-related lncRNAs in the training set. COAD patients were scored by the uniform formula and divided into high-risk and low-risk groups based on the median risk score. Patients with high-risk scores indicated poor prognosis in the training set, and the result was confirmed in the testing set and the entire TCGA-COAD cohort (all p < 0.01). Multivariable analysis suggested that our model was an independent factor for overall survival in COAD. The area under the curve (AUC) and C-index indicated that our model had better predictive efficiency than other indicators in the TCGA-COAD cohort. Functional enrichment analysis suggested that our model was associated with the MAPK pathway in COAD. Besides, our model was positively correlated with the HRD scores.

CONCLUSION

A new prognostic model was established based on nineteen HRR-related lncRNAs which had excellent predictive efficiency on the prognosis of COAD. This prognostic model may provide a new strategy for prognostic prediction of COAD patients.

Investigation of the expression levels of CDH1, FHIT, PTEN, and TTPAL genes in colorectal tumors

BACKGROUND

The main aim of the study is to assess expression levels of CDH1, FHIT, PTEN, and TTPAL genes in tumors and peripheral bloods of colorectal cancer patients in staged I-IV.

METHODS

Gene expression analysis of related genes were performed for tumor tissues and peripheral blood samples of 51 colorectal cancer patients and colon tissues and blood samples of 5 healthy individuals. The real-time-PCR reaction method was used for the analysis.

RESULTS

Alteration of mRNA levels of related genes in tumor tissues of colorectal cancer cases was determined compared to control tissues. GAPDH and TBP were used for the normalization. While the mRNA levels of CDH1 decreased, the mRNA level of the FHIT and TTPAL genes increased in the tumor tissues. There was no PTEN gene expression difference in tumor tissues (total). The mRNA levels of the CDH1 and PTEN genes were increased while the mRNA levels of FHIT and TTPAL genes decreased in the blood (total). T he mRNA levels of the CDH1 gene decreased at each stage (I-IV) in the tumor tissues and increased at each stage (I-IV) in the blood. T he PTEN gene mRNA levels at each stage were controversial. The mRNA levels of the FHIT gene increased at stage I-II-III, decreased at stage IV in the tissues and decreased at each stage (I-IV) in the blood. The mRNA levels of TTPAL gene increased at each stage (I-IV) in the tissues and decreased at each stage (I-IV) in the blood.

Molecular Analysis of Colorectal Cancers Suggests a High Frequency of Lynch Syndrome in Indonesia

Simple Summary

The incidence of young people <50 years old who are diagnosed with colorectal cancer (CRC), termed as early onset colorectal cancer (EOCRC), accounted for nearly 30% of the total CRC patients in Indonesia, which is about three times higher than what is being reported in Europe, the UK and USA. Lynch syndrome (LS) is a hereditary type of CRC that is associated with a younger age of onset. Detecting LS has been long reported to be a cost-effective strategy to provide aid in the diagnosis or management of the individual or at-risk family members. The aim of this retrospective study was to screen for Lynch Syndrome in Indonesian CRC patients using simple and robust polymerase chain reaction (PCR)-based molecular testing, known as N_LyST (Nottingham Lynch Syndrome Test). To our knowledge, we are the first to study and observe a potentially higher frequency of LS (13.85%) among CRC patients in Indonesia (n = 231). This may partially contribute to the reported much higher rate of EOCRC found in the country.

Abstract

There is about three times higher incidence of young patients <50 years old with colorectal cancer, termed EOCRC, in Indonesia as compared to Europe, the UK and USA. The aim of this study was to investigate the frequency of Lynch Syndrome (LS) in Indonesian CRC patients. The previously described Nottingham Lynch Syndrome Test (N_LyST) was used in this project. N_LyST is a robust high-resolution melting (HRM)-based test that has shown 100% concordance with standard reference methods, including capillary electrophoresis and Sanger sequencing. The test consisted of five mononucleotide microsatellite markers (BAT25, BAT26, BCAT25, MYB, EWSR1), BRAF V600E mutation and MLH1 region C promoter for methylation (using bisulphite-modified DNA). A total of 231 archival (2016–2019) formalin-fixed, paraffin-embedded (FFPE) tumour tissues from CRC patients collected from Dr. Sardjito General Hospital Yogyakarta, Indonesia, were successfully tested and analysed. Among those, 44/231 (19.05%) were MSI, 25/231 (10.82%) were harbouring BRAF V600E mutation and 6/231 (2.60%) had MLH1 promoter methylation. Almost all—186/197 (99.45%)—MSS cases were MLH1 promoter unmethylated, while there were only 5/44 (11.36%) MSI cases with MLH1 promoter methylation. Similarly, only 9/44 (20.45%) of MSI cases were BRAF mutant. There were 50/231 (21.65%) EOCRC cases, with 15/50 (30%) regarded as MSI, as opposed to 29/181 (16.02%) within the older group. In total, 32/231 patients (13.85%) were classified as “Probable Lynch” (MSI, BRAF wildtype and MLH1 promoter unmethylated), which were enriched in EOCRC as compared to older patients (24% vs. 11.05%, p = 0.035). Nonetheless, 30/50 (76.00%) cases among the EOCRC cases were non-LS (sporadic) and were significantly associated with a left-sided tumour. The overall survival of both “Probable Lynch” and non-LS (sporadic) groups (n = 227) was comparable (p = 0.59), with follow up period of 0–1845 days/61.5 months. Stage, node status, histological grading and ECOG score were significantly associated with patient overall survival (p < 0.005), yet only ECOG was an independent factor for OS (HR: 4.38; 95% CI: 1.72–11.2; p = 0.002). In summary, this study is the first to reveal a potentially higher frequency of LS among CRC patients in Indonesia, which may partially contribute to the reported much higher number of EOCRC as compared to the incidence in the West.

CDKN2B-AS1 Promotes Malignancy as a Novel Prognosis-Related Molecular Marker in the Endometrial Cancer Immune Microenvironment

The prognosis of patients with endometrial cancer (EC) is closely associated with immune cell infiltration. Although abnormal long non-coding RNA (lncRNA) expression is also linked to poor prognosis in patients with EC, the function and action mechanism of immune infiltration-related lncRNAs underlying the occurrence and development of EC remains unclear. In this study, we analyzed lncRNA expression using The Cancer Genome Atlas and clinical data and identified six lncRNAs as prognostic markers for EC, all of which are associated with the infiltration of immune cell subtypes, as illustrated by ImmLnc database and ssGSEA analysis. Real-time quantitative polymerase chain reaction showed that CDKN2B-AS1 was significantly overexpressed in EC, whereas its knockdown inhibited the proliferation and invasion of EC cells and the in vivo growth of transplanted tumors in nude mice. Finally, we constructed a competing endogenous RNA regulatory network and conducted Gene Ontology enrichment analysis to elucidate the potential molecular mechanism underlying CDKN2B-AS1 function. Overall, we identified molecular targets associated with immune infiltration and prognosis and provide new insights into the development of molecular therapies and treatment strategies against EC.

A signature based on 11 autophagy genes for prognosis prediction of colorectal cancer

Aim

To develop an autophagy-gene-based signature that could help to anticipate the therapeutic effects of Colorectal Cancer (CRC).

Methods

We downloaded the gene expression profiles of CRC samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. Genes with significant prognostic value in CRC were screened through univariate Cox regression analysis, while the LASSO Cox regression method was applied to screen optimal genes to construct the autophagy‐related prognostic signature.

Results

11 autophagy genes were identified and selected for the establishment of prognosis prediction model for CRC patients. The CRC patients were classified into the low- and high-risk groups according to the optimal cutoff value. The time-dependent ROC curves indicated the good performance of this model in prognosis prediction, with AUC values of 0.66, 0.66, and 0.67 at 1, 3 and 5 years for TCGA samples, as well as AUC values of 0.63, 0.65 and 0.64 for GEO samples, respectively. The multivariate Cox regression analysis results confirmed risk score as the independent marker for prognosis prediction in CRC. Besides, the constructed nomogram also had high predictive value. The results analysis on the tumor infiltrating immune cells (TIICs) relative ratios and mRNA levels of key immune checkpoint receptors indicated the signature was closely related to immune microenvironment of CRC in the context of TIICs and immune checkpoint receptors’ mRNA level. The proportion of MSI-L + MSI-H in the high-risk group was higher than that in the low-risk group. Moreover, the tumor purity was evaluated by estimate function package suggested that lower tumor purity in CRC might lead to a poorer prognosis.

Conclusion

The autophagy-related features obtained in this study were able to divide the CRC patients into low- and high-risk groups, which should be contribute to the decision-making of CRC treatment.

Establishment of a novel ferroptosis-related lncRNA pair prognostic model in colon adenocarcinoma

Long non-coding RNAs (lncRNAs) have been reported to be prognostic factors for cancer. Ferroptosis is an iron-dependent process of programmed cell death. Here, we established a ferroptosis-related lncRNA (frlncRNA) pair signature and revealed its prognostic value in colon adenocarcinoma (COAD) by analyzing the data from The Cancer Genome Atlas (TCGA). FrlncRNAs were identified based on co-expression analysis using the Pearson correlation. Differentially expressed frlncRNAs (DEfrlncRNAs) were recognized and paired, followed by prognostic assessment using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) penalized Cox analysis was used to determine and construct a risk score prognostic model, by which the receiver operating characteristic (ROC) curves for predicting the overall survival (OS) were conducted. Following the evaluation of whether it was an independent prognostic factor, correlations between the risk score model and clinicopathological characteristics, hypoxia- and immune-related factors, and somatic variants were investigated. In total, 148 DEfrlncRNA pairs were identified, 25 of which were involved in a risk score prognostic signature. The area under ROC curves (AUCs) representing the predictive effect for 1-, 3-, and 5-year survival rates were 0.860, 0.885, and 0.934, respectively. The risk score model was confirmed as an independent prognostic factor and was significantly superior to the clinicopathological characteristics. Correlation analyses showed disparities in clinicopathological characteristics, hypoxia- and immune-related factors, and somatic variants, as well as specific signaling pathways between high- and low-risk groups. The novel risk score prognostic model constructed by pairing DEfrlncRNAs showed promising clinical prediction value in COAD.

Fatty Acid Metabolism-Related lncRNAs Are Potential Biomarkers for Predicting the Overall Survival of Patients With Colorectal Cancer

Abnormal metabolism, including abnormal fatty acid metabolism, is an emerging hallmark of cancer. The current study sought to investigate the potential prognostic value of fatty acid metabolism-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC). To this end, we obtained the gene expression data and clinical data of patients with CRC from The Cancer Genome Atlas (TCGA) database. Through gene set variation analysis (GSVA), we found that the fatty acid metabolism pathway was related to the clinical stage and prognosis of patients with CRC. After screening differentially expressed RNAs, we constructed a fatty acid metabolism-related competing endogenous RNA (ceRNA) network based on the miRTarBase, miRDB, TargetScan, and StarBase databases. Next, eight fatty acid metabolism-related lncRNAs included in the ceRNA network were identified to build a prognostic signature with Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, and a nomogram was established based on the lncRNA signature and clinical variables. The signature and nomogram were further validated by Kaplan–Meier survival analysis, Cox regression analysis, calibration plots, receiver operating characteristic (ROC) curves, decision curve analysis (DCA). Besides, the TCGA internal and the quantitative real-time polymerase chain reaction (qRT-PCR) external cohorts were applied to successfully validate the robustness of the signature and nomogram. Finally, in vitro assays showed that knockdown of prognostic lncRNA TSPEAR-AS2 decreased the triglyceride (TG) content and the expressions of fatty acid synthase (FASN) and acetyl-CoA carboxylase 1 (ACC1) in CRC cells, which indicated the important role of lncRNA TSPEAR-AS2 in modulating fatty acid metabolism of CRC. The result of Oil Red O staining showed that the lipid content in lncRNA TSPEAR-AS2 high expression group was higher than that in lncRNA TSPEAR-AS2 low expression group. Our study may provide helpful information for fatty acid metabolism targeting therapies 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.

A Prognostic Autophagy-Related Long Non-coding RNA (ARlncRNA) Signature in Acute Myeloid Leukemia (AML)

Background

Some studies have proven that autophagy and lncRNA play important roles in AML. Several autophagy related lncRNA signatures have been shown to affect the survival of patients in some other cancers. However, the role of autophagy related lncRNA in AML has not been explored yet. Hence, this study aims to find an autophagy related lncRNA signature that can affect survival for AML patients.

Method

A Pearson correlation analysis, a Kaplan–Meier survival curve, a univariate cox regression, and a multivariate cox regression were performed to establish an autophagy related lncRNA signature. A univariate cox regression, a multivariate cox regression, a Kaplan–Meier survival curve, and a ROC curve were applied to confirm if the signature is an independent prognosis for AML patients. The relationship between the signature and the clinical features was explored by using a T test. Gene Set Enrichment Analysis (GSEA) was used to investigate the potential tumor related pathways.

Results

A four-autophagy related lncRNA (MIR133A1HG, AL359715.1, MIRLET7BHG, and AL356752.1) signature was established. The high risk score based on signature was related to the short survival time of AML patients. The signature was an independent factor for the prognosis for AML patients (HR = 1.684, 95% CI = 1.324–2.142, P < 0.001). The signature was correlated with age, leukocyte numbers, and FAB (M3 or non-M3). The P53, IL6/JAK/STAT3, TNF-α, INF-γ, and IL2/STAT5 pathways might contribute to the differences between the risk groups based on signature in AML.

Conclusion

The four autophagy related lncRNAs and their signature might be novel biomarkers for predicting the survival of AML patients. Some biological pathways might be the potential mechanisms of the signature for the survival of AML patients.

A Prognostic Ferroptosis-Related lncRNAs Signature Associated With Immune Landscape and Radiotherapy Response in Glioma

Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are implicated in the regulation of tumor cell ferroptosis. However, the prognostic value of ferroptosis-related lncRNAs has never been comprehensively explored in glioma. In this study, the transcriptomic data and clinical information of glioma patients were downloaded from TCGA, CGGA and Rembrandt databases. We identified 24 prognostic ferroptosis-related lncRNAs, 15 of which (SNAI3-AS1, GDNF-AS1, WDFY3-AS2, CPB2-AS1, WAC-AS1, SLC25A21-AS1, ARHGEF26-AS1, LINC00641, LINC00844, MIR155HG, MIR22HG, PVT1, SNHG18, PAXIP1-AS2, and SBF2-AS1) were used to construct a ferroptosis-related lncRNAs signature (FRLS) according to the least absolute shrinkage and selection operator (LASSO) regression. The validity of this FRLS was verified in training (TCGA) and validation (CGGA and Rembrandt) cohorts, respectively. The Kaplan-Meier curves revealed a significant distinction of overall survival (OS) between the high- and low-risk groups. The receiver operating characteristic (ROC) curves exhibited robust prognostic capacity of this FRLS. A nomogram with improved accuracy for predicting OS was established based on independent prognostic factors (FRLS, age, and WHO grade). Besides, patients in the high-risk group had higher immune, stroma, and ESTIMATE scores, lower tumor purity, higher infiltration of immunosuppressive cells, and higher expression of immune checkpoints. Patients in the low-risk group benefited significantly from radiotherapy, while no survival benefit of radiotherapy was observed for those in the high-risk group. In conclusion, we identified the prognostic ferroptosis-related lncRNAs in glioma, and constructed a prognostic signature which was associated with the immune landscape of glioma microenvironment and radiotherapy response.

A prognostic predictive model constituted with gene mutations of APC, BRCA2, CDH1, SMO, and TSC2 in colorectal cancer

Background

Colorectal cancer (CRC) is a malignant tumor that seriously threatens human health. A CRC predictive model can be used as an effective method to provide an appropriate treatment for CRC patients.

Methods

A total of 34 CRC patients were enrolled in this study. After performing 1000-gene panel targeted next-generation sequencing (NGS), high-frequency mutation genes were screened, and their functional terms and pathways were enriched. In The Cancer Genome Atlas (TCGA) CRC cases, the risk factors for overall survival (OS) were screened by univariate and multivariate analysis, and a predictive model was constructed and verified. Subsequently, the relationship among mutation status, gene expression, methylation level, and OS was analyzed to explore the molecular mechanism of CRC progression.

Results

A total of 26 high-frequency mutation genes were screened, which were mainly enriched in breast cancer and proteoglycans in cancer pathways. The clinical parameters of age, stage, recurrence and metastasis, the mutation status of APC, BRCA2, CDH1, SMO, and TSC2 were identified as risk factors for the construction of the predictive model. The areas under the receiver operating characteristic curve were 0.734, 0.754, 0.774, and 0.74 for 1-, 3-, 5- and 7-year survival in the model group, respectively.

Conclusions

We identified several mutated genes and clinical parameters affecting OS and established a model to better predict the OS of CRC patients.

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

Background: The expression of long non-coding RNA (lncRNA) is associated with the epithelial-mesenchymal transition (EMT) in tumorigenicity, but the role of EMT-related lncRNA in colorectal cancer (CRC) remains unclear.

Methods: The clinical data and gene expression profile of CRC patients were obtained from The Cancer Genome Atlas database. Differential expression analysis, Cox regression model, and Kaplan-Meier analysis were used to study the relationship between EMT-related lncRNAs and the prognosis of CRC. Functional analysis and unsupervised clustering analysis were performed to explore the influence of certain lncRNAs on CRC. Finally, Cytoscape was used to construct mRNA-lncRNA networks.

Results: Two signatures incorporating six and ten EMT-related lncRNAs were constructed for predicting the overall survival (OS) and disease-free survival (DFS), respectively. Kaplan-Meier survival curves indicated that patients in the high-risk group had a poorer prognosis than those in the low-risk group. The results of the functional analysis suggested that the P53 and ECM-receptor pathways affect the prognosis of CRC, and AL591178.1 is a key prognostic EMT-related lncRNA, which is negatively related to immune cells, P53 pathway, and ECM-receptor pathway.

Conclusion: Six OS-related and ten DFS-related EMT-related lncRNAs were correlated with the prognosis of CRC by potentially affecting the immune microenvironment, and AL591178.1 plays a key role as a prognostic factor.

Identification of 6 Hub Proteins and Protein Risk Signature of Colorectal Cancer

Background

Colorectal cancer (CRC) is the second most common cause of cancer death in the United States and the third most common cancer globally. The incidence of CRC tends to be younger, and we urgently need a reliable prognostic assessment strategy.

Methods

Protein expression profile and clinical information of 390 CRC patients/samples were downloaded from the TCPA and TCGA database, respectively. The Kaplan-Meier, Cox regression, and Pearson correlation analysis were applied in this study.

Results

Based on the TCPA and TCGA database, we screened 6 hub proteins and first constructed protein risk signature, all of which were significantly associated with CRC patients' overall survival (OS). The risk score was an independent prognostic factor and significantly related with the size of the tumor in situ (T). 6 hub proteins were differentially expressed in cancer and normal tissues and in different CRC stages, which were validated at the ONCOMINE database. Next, 40 coexpressed proteins of 6 hub proteins were extracted from the TCPA database. In the protein-protein interaction (PPI) network, HER1, HER2, and CTNNB1 were at the center. Function enrichment analysis illustrated that 46 proteins were mainly involved in the EGFR (HER1) tyrosine kinase inhibitor resistance pathway.

Conclusion

Studies indicated that 6 hub proteins might be considered as new targets for CRC therapies, and the protein risk signature can be used to predict the OS of CRC patients.