A Novel Autophagy-Related IncRNAs Signature for Prognostic Prediction and Clinical Value in Patients With Pancreatic Cancer

14-3-3ε: a protein with complex physiology function but promising therapeutic potential in cancer

Over the past decade, the role of the 14-3-3 protein has received increasing interest. Seven subtypes of 14-3-3 proteins exhibit high homology; however, each subtype maintains its specificity. The 14-3-3ε protein is involved in various physiological processes, including signal transduction, cell proliferation, apoptosis, autophagy, cell cycle regulation, repolarization of cardiac action, cardiac development, intracellular electrolyte homeostasis, neurodevelopment, and innate immunity. It also plays a significant role in the development and progression of various diseases, such as cardiovascular diseases, inflammatory diseases, neurodegenerative disorders, and cancer. These immense and various involvements of 14-3-3ε in diverse processes makes it a promising target for drug development. Although extensive research has been conducted on 14-3-3 dimers, studies on 14-3-3 monomers are limited. This review aimed to provide an overview of recent reports on the molecular mechanisms involved in the regulation of binding partners by 14-3-3ε, focusing on issues that could help advance the frontiers of this field. Video Abstract.

Unravelling the complexity of lncRNAs in autophagy to improve potential cancer therapy

Autophagy is well-known as an internal catabolic process that is evolutionarily conserved and performs the key biological function in maintaining cellular homeostasis. It is tightly controlled by several autophagy-related (ATG) proteins, which are closely associated with many types of human cancers. However, what has remained controversial is the janus roles of autophagy in cancer progression. Interestingly, the biological function of long non-coding RNAs (lncRNAs) in autophagy has been gradually understood in different types of human cancers. More recently, numerous studies have demonstrated that several lncRNAs may regulate some ATG proteins and autophagy-related signaling pathways to either activate or inhibit the autophagic process in cancer. Thus, in this review, we summarize the latest advance in the knowledge of the complicated relationships between lncRNAs and autophagy in cancer. Also, the in-depth dissection of the lncRNAs-autophagy-cancers axis involved in this review would shed new light on discovery of more potential cancer biomarkers and therapeutic targets in the future.

Noncoding RNAs: an emerging modulator of drug resistance in pancreatic cancer

Pancreatic cancer is the eighth leading cause of cancer-related deaths worldwide. Chemotherapy including gemcitabine, 5-fluorouracil, adriamycin and cisplatin, immunotherapy with immune checkpoint inhibitors and targeted therapy have been demonstrated to significantly improve prognosis of pancreatic cancer patients with advanced diseases. However, most patients developed drug resistance to these therapeutic agents, which leading to shortened patient survival. The detailed molecular mechanisms contributing to pancreatic cancer drug resistance remain largely unclear. The growing evidences have shown that noncoding RNAs (ncRNAs), including microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs), are involved in pancreatic cancer pathogenesis and development of drug resistance. In the present review, we systematically summarized the new insight on of various miRNAs, lncRNAs and circRNAs on drug resistance of pancreatic cancer. These results demonstrated that targeting the tumor-specific ncRNA may provide novel options for pancreatic cancer treatments.

Tumor mutation burden-related long non-coding RNAs is predictor for prognosis and immune response in pancreatic cancer

BACKGROUND

Pancreatic cancer is one of the most common malignant tumors with extremely poor prognosis. It is urgent to identify promising prognostic biomarkers for pancreatic cancer.

METHODS

A total of 266 patients with pancreatic adenocarcinoma (PAAD) in the Cancer Genome Atlas (TCGA)-PAAD cohort and the PACA-AU cohort were enrolled in this study. Firstly, prognostic tumor mutation burden (TMB)-related long non-coding RNAs (lncRNAs) were identified by DESeq2 and univariate analysis in the TCGA-PAAD cohort. And then, the TCGA-PAAD cohort was randomized into the training set and the testing set. Least absolute shrinkage and selection operator (LASSO) was used to construct the model in the training set. The testing set, the TCGA-PAAD cohort and the PACA-AU cohort was used as validation. The model was evaluated by multiple methods. Finally, functional analysis and immune status analysis were applied to explore the potential mechanism of our model.

RESULTS

A prognostic model based on fourteen TMB-related lncRNAs was established in PAAD. Patients with High risk score was associated with worse prognosis compared to those with low risk score in all four datasets. Besides, the model had great performance in the prediction of 5-year overall survival in four datasets. Multivariate analysis also indicated that the risk score based on our model was independent prognostic factor in PAAD. Additionally, our model had the best predictive efficiency in PAAD compared to typical features and other three published models. And then, our findings also showed that high risk score was also associated with high TMB, microsatellite instability (MSI) and homologous recombination deficiency (HRD) score. Finally, we indicated that high risk score was related to low immune score and less infiltration of immune cells in PAAD.

CONCLUSION

we established a 14 TMB-related lncRNAs prognostic model in PAAD and the model had excellent performance in the prediction of prognosis in PAAD. Our findings provided new strategy for risk stratification and new clues for precision treatment in PAAD.

Comprehensive analysis of anoikis-related long non-coding RNA immune infiltration in patients with bladder cancer and immunotherapy

Background

Anoikis is a form of programmed cell death or programmed cell death(PCD) for short. Studies suggest that anoikis involves in the decisive steps of tumor progression and cancer cell metastasis and spread, but what part it plays in bladder cancer remains unclear. We sought to screen for anoikis-correlated long non-coding RNA (lncRNA) so that we can build a risk model to understand its ability to predict bladder cancer prognosis and the immune landscape.

Methods

We screened seven anoikis-related lncRNAs (arlncRNAs) from The Cancer Genome Atlas (TCGA) and designed a risk model. It was validated through ROC curves and clinicopathological correlation analysis, and demonstrated to be an independent factor of prognosis prediction by uni- and multi-COX regression. In the meantime, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, immune infiltration, and half-maximal inhibitory concentration prediction (IC50) were implemented with the model. Moreover, we divided bladder cancer patients into three subtypes by consensus clustering analysis to further study the differences in prognosis, immune infiltration level, immune checkpoints, and drug susceptibility.

Result

We designed a risk model of seven arlncRNAs, and proved its accuracy using ROC curves. COX regression indicated that the model might be an independent prediction factor of bladder cancer prognosis. KEGG enrichment analysis showed it was enriched in tumors and immune-related pathways among the people at high risk. Immune correlation analysis and drug susceptibility results indicated that it had higher immune infiltration and might have a better immunotherapy efficacy for high-risk groups. Of the three subtypes classified by consensus clustering analysis, cluster 3 revealed a positive prognosis, and cluster 2 showed the highest level of immune infiltration and was sensitive to most chemistries. This is helpful for us to discover more precise immunotherapy for bladder cancer patients.

Conclusion

In a nutshell, we found seven arlncRNAs and built a risk model that can identify different bladder cancer subtypes and predict the prognosis of bladder cancer patients. Immune-related and drug sensitivity researches demonstrate it can provide individual therapeutic schedule with greater precision for bladder cancer patients.

Cuprotosis Programmed-Cell-Death-Related lncRNA Signature Predicts Prognosis and Immune Landscape in PAAD Patients

In terms of mortality and survival, pancreatic cancer is one of the worst malignancies. Known as a unique type of programmed cell death, cuprotosis contributes to tumor cell growth, angiogenesis, and metastasis. Cuprotosis programmed-cell-death-related lncRNAs (CRLs) have been linked to PAAD, although their functions in the tumor microenvironment and prognosis are not well understood. This study included data from the TCGA-PAAD cohort. Random sampling of PAAD data was conducted, splitting the data into two groups for use as a training set and test set (7:3). We searched for differentially expressed genes that were substantially linked to prognosis using univariate Cox and Lasso regression analysis. Through the use of multivariate Cox proportional risk regression, a risk-rating system for prognosis was developed. Correlations between the CRL signature and clinicopathological characteristics, tumor microenvironment, immunotherapy response, and chemotherapy sensitivity were further evaluated. Lastly, qRT-PCR was used to compare CRL expression in healthy tissues to that in tumors. Some CRLs are thought to have strong correlations with PAAD outcomes. These CRLs include AC005332.6, LINC02041, LINC00857, and AL117382.1. The CRL-based signature construction exhibited outstanding predictive performance and offers a fresh approach to evaluating pre-immune effectiveness, paving the way for future studies in precision immuno-oncology.

Proteomic and Phosphoproteomic Profiling Reveals the Oncogenic Role of Protein Kinase D Family Kinases in Cholangiocarcinoma

Cholangiocarcinoma (CCA) is a lethal malignancy in the hepatobiliary system, with dysregulated protein expression and phosphorylation signaling. However, the protein and phosphorylation signatures of CCAs are little-known. Here, we performed the proteomic and phosphoproteomic profiling of tumors and normal adjacent tissues (NATs) from patients with CCA and predicted eleven PKs high-potentially related to CCA with a comprehensive inference of the functional protein kinases (PKs) (CifPK) pipeline. Besides the two known CCA-associated PKs, we screened the remaining candidates and uncovered five PKs as novel regulators in CCA. Specifically, the protein kinase D (PKD) family members, including PRKD1, PRKD2, and PRKD3, were identified as critical regulators in CCA. Moreover, the pan-inhibitor of the PKD family, 1-naphthyl PP1 (1-NA-PP1), was validated as a potent agent for inhibiting the proliferation, migration, and invasion ability of CCA cells. This study reveals new PKs associated with CCA and suggests PRKD kinases as novel treatment targets for CCA.

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.

Characteristic of Molecular Subtypes in Lung Squamous Cell Carcinoma Based on Autophagy-Related Genes and Tumor Microenvironment Infiltration

Background

Recently, a large number of studies have sought personalized treatment for lung squamous cell carcinoma (LUSC) by dividing patients into different molecular subtypes. Autophagy plays an important role in maintaining the tumor microenvironment and immune-related biological processes. However, the molecular subtypes mediated by autophagy in LUSC are not clear.

Methods

Based on 490 LUSC samples, we systematically analyzed the molecular subtype modification patterns mediated by autophagy-related genes. The ssGSEA and CIBERSORT algorithm were utilized to quantify the relative abundance of TME cell infiltration. Principal component analysis was used to construct autophagy prognostic score (APS) model.

Results

We identified three autophagy subtypes in LUSC, and their clinical outcomes and TME cell infiltration had significant heterogeneity. Cluster A was rich in immune cell infiltration. The enrichment of EMT stromal pathways and immune checkpoint molecules were significantly enhanced, which may lead to its immunosuppression. Cluster B was characterized by relative immunosuppression and relative stromal activation. Cluster C was activated in biological processes related to repair. Patients with high APS were significantly positively correlated with TME stromal activity and poor survival. Meanwhile, high APS showed an advantage in response to anti-PD1 and anti-CTLA4 immunotherapy.

Conclusion

This study explored the autophagy molecular subtypes in LUSC. We also discovered the heterogeneity of TME cell infiltration driven by autophagy-related genes. The established APS model is of great significance for evaluating the prognosis of LUSC patients, the infiltration of TME cells, and the effect of immunotherapy.

Cuproptosis-related lncRNA predict prognosis and immune response of lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) accounts for 50% of lung cancers, with high mortality and poor prognosis. Long non-coding RNA (lncRNA) plays a vital role in the progression of tumors. Cuproptosis is a newly discovered form of cell death that is highly investigated. Therefore, in the present study, we aimed to investigate the role of cuproptosis-related lncRNA signature in clinical prognosis prediction and immunotherapy and the relationship with drug sensitivity.

MATERIAL AND METHODS

Genomic and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and cuproptosis-related genes were obtained from cuproptosis-related studies. The prognostic signature was constructed by co-expression analysis and Cox regression analysis. Patients were divided into high and low risk groups, and then, a further series of model validations were carried out to assess the prognostic value of the signature. Subsequently, lncRNAs were analyzed for gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes Enrichment (KEGG), immune-related functions, and tumor mutation burden (TMB). Finally, we used tumor immune dysfunction and exclusion (TIDE) algorithms on immune escape and immunotherapy of cuproptosis-related lncRNAs, thereby identifying its sensitivity toward potential drugs for LUAD.

RESULTS

A total of 16 cuproptosis-related lncRNAs were obtained, and a prognostic signature was developed. We found that high-risk patients had worse overall survival (OS) and progression-free survival (PFS) and higher mortality. Independent prognostic analyses, ROC, C-index, and nomogram showed that the cuproptosis-related lncRNAs can accurately predict the prognosis of patients. The nomogram and heatmap showed a distinct distribution of the high- and low-risk cuproptosis-related lncRNAs. Enrichment analysis showed that the biological functions of lncRNAs are associated with tumor development. We also found that immune-related functions, such as antiviral activity, were suppressed in high-risk patients who had mutations in oncogenes. OS was poorer in patients with high TMB. TIDE algorithms showed that high-risk patients have a greater potential for immune escape and less effective immunotherapy.

CONCLUSION

To conclude, the 16 cuproptosis-related lncRNAs can accurately predict the prognosis of patients with LUAD and may provide new insights into clinical applications and immunotherapy.

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.

Risk Stratification and Validation of Eleven Autophagy-Related lncRNAs for Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC), the most prevalent subtype of esophageal cancer, ranks sixth in cancer-related mortality, making it one of the deadliest cancers worldwide. The identification of potential risk factors for ESCC might help in implementing precision therapies. Autophagy-related lncRNAs are a group of non-coding RNAs that perform critical functions in the tumor immune microenvironment and therapeutic response. Therefore, we aimed to establish a risk model composed of autophagy-related lncRNAs that can serve as a potential biomarker for ESCC risk stratification. Using the RNA expression profile from 179 patients in the GSE53622 and GSE53624 datasets, we found 11 lncRNAs (AC004690.2, AC092159.3, AC093627.4, AL078604.2, BDNF-AS, HAND2-AS1, LINC00410, LINC00588, PSMD6-AS2, ZEB1-AS1, and LINC02586) that were co-expressed with autophagy genes and were independent prognostic factors in multivariate Cox regression analysis. The risk model was constructed using these autophagy-related lncRNAs, and the area under the receiver operating characteristic curve (AUC) of the risk model was 0.728. To confirm that the model is reliable, the data of 174 patients from The Cancer Genome Atlas (TCGA) esophageal cancer dataset were analyzed as the testing set. A nomogram for ESCC prognosis was developed using the risk model and clinic-pathological characteristics. Immune function annotation and tumor mutational burden of the two risk groups were analyzed and the high-risk group displayed higher sensitivity in chemotherapy and immunotherapy. Expression of differentially expressed lncRNAs were further validated in human normal esophageal cells and esophageal cancer cells. The constructed lncRNA risk model provides a useful tool for stratifying risk and predicting the prognosis of patients with ESCC, and might provide novel targets for ESCC treatment.

An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer Patients

Purpose

Pancreatic cancer (PC) is a common, highly lethal cancer with a low survival rate. Autophagy is involved in the occurrence and progression of PC. This study aims to explore the feasibility of using an autophagy-related long noncoding RNA (lncRNA) signature for assessing PC patient survival.

Methods

We obtained RNA sequencing and clinical data of patients from the TCGA website. Autophagy genes were obtained from the Human Autophagy Database. The prognostic model, generated through univariate and multivariate Cox regression analyses, included 10 autophagy-related lncRNAs. Receiver operating characteristic (ROC) curves and forest plots were generated for univariate and multivariate Cox regression analyses, to examine the predictive feasibility of the risk model. Gene set enrichment analysis (GSEA) was used to screen enriched gene sets.

Results

Twenty-eight autophagy-related lncRNAs were filtered out through univariate Cox regression analysis (P < 0.001). Ten autophagy-related lncRNAs, including 4 poor prognosis factors and 6 beneficial prognosis factors, were further screened via multivariate Cox regression analysis. The AUC value of the ROC curve was 0.815. GSEA results demonstrated that cancer-related gene sets were significantly enriched.

Conclusion

A signature based on ten autophagy-related lncRNAs was identified. This signature could be potentially used for evaluating clinical prognosis and might be used for targeted therapy against PC.

Construction of a Novel LncRNA Signature Related to Genomic Instability to Predict the Prognosis and Immune Activity of Patients With Hepatocellular Carcinoma

Background

Genomic instability (GI) plays a crucial role in the development of various cancers including hepatocellular carcinoma. Hence, it is meaningful for us to use long non-coding RNAs related to genomic instability to construct a prognostic signature for patients with HCC.

Methods

Combining the lncRNA expression profiles and somatic mutation profiles in The Cancer Genome Atlas database, we identified GI-related lncRNAs (GILncRNAs) and obtained the prognosis-related GILncRNAs through univariate regression analysis. These lncRNAs obtained risk coefficients through multivariate regression analysis for constructing GI-associated lncRNA signature (GILncSig). ROC curves were used to evaluate signature performance. The International Cancer Genomics Consortium (ICGC) cohort, and in vitro experiments were used for signature external validation. Immunotherapy efficacy, tumor microenvironments, the half-maximal inhibitory concentration (IC50), and immune infiltration were compared between the high- and low-risk groups with TIDE, ESTIMATE, pRRophetic, and ssGSEA program.

Results

Five GILncRNAs were used to construct a GILncSig. It was confirmed that the GILncSig has good prognostic evaluation performance for patients with HCC by drawing a time-dependent ROC curve. Patients were divided into high- and low-risk groups according to the GILncSig risk score. The prognosis of the low-risk group was significantly better than that of the high-risk group. Independent prognostic analysis showed that the GILncSig could independently predict the prognosis of patients with HCC. In addition, the GILncSig was correlated with the mutation rate of the HCC genome, indicating that it has the potential to measure the degree of genome instability. In GILncSig, LUCAT1 with the highest risk factor was further validated as a risk factor for HCC in vitro. The ESTIMATE analysis showed a significant difference in stromal scores and ESTIMATE scores between the two groups. Multiple immune checkpoints had higher expression levels in the high-risk group. The ssGSEA results showed higher levels of tumor-antagonizing immune cells in the low-risk group compared with the high-risk group. Finally, the GILncSig score was associated with chemotherapeutic drug sensitivity and immunotherapy efficacy of patients with HCC.

Conclusion

Our research indicates that GILncSig can be used for prognostic evaluation of patients with HCC and provide new insights for clinical decision-making and potential therapeutic strategies.

Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma

Cholangiocarcinoma (CCA) is featured by common occurrence and poor prognosis. Autophagy is a biological process that has been extensively involved in the progression of tumors. Long noncoding RNAs (lncRNAs) have been discovered to be critical in diagnosing and predicting various tumors. It may be valuable to elaborate autophagy-related lncRNAs (ARlncRNAs) in CCA, and indeed, there are still few studies concerning the role of ARlncRNAs in CCA. Here, a prognostic ARlncRNA signature was constructed to predict the survival outcome of CCA patients. Through identification, three differentially expressed ARlncRNAs (DEARlncRNAs), including CHRM3.AS2, MIR205HG, and LINC00661, were screened and were considered predictive signatures. Furthermore, the overall survival (OS) of patients with high-risk scores was significantly lower than that of patients with low scores. Interestingly, the risk score was an independent factor for the OS of patients with CCA. Moreover, receiver operating characteristic (ROC) curve analysis showed that the screened and constructed prognosis signature for 1 year (AUC = 0.884), 3 years (AUC =0.759), and 5 years (AUC = 0.788) presented a high score of accuracy in predicting OS of CCA patients. Gene set enrichment analysis (GSEA) revealed that the three DEARlncRNAs were significantly enriched in CCA-related signaling pathways, including “pathways of basal cell carcinoma”, “glycerolipid metabolism”, etc. Quantitative real-time PCR (qRT-PCR) showed that expressions of CHRM3.AS2, MIR205HG, and LINC00661 were higher in CCA tissues than those in normal tissues, similar to the trends detected in the CCA dataset. Furthermore, Pearson’s analysis reported an intimate correlation of the risk score with immune cell infiltration, indicating a predictive value of the signature for the efficacy of immunotherapy. In addition, the screened lncRNAs were found to have the ability to modulate the expression of mRNAs by interacting with miRNAs based on the established lncRNA-miRNA-mRNA network. In conclusion, our study develops a novel nomogram with good reliability and accuracy to predict the OS of CCA patients, providing a significant guiding value for developing tailored therapy for CCA patients.

Long non-coding RNA LINC01559 exerts oncogenic role via enhancing autophagy in lung adenocarcinoma

BACKGROUND

Long non-coding RNAs (lncRNAs) have been verified to play fatal role in regulating the progression of lung adenocarcinoma (LUAD). Although lncRNAs play important role in regulating the autophagy of tumor cells, the function and molecular mechanism of LINC01559 in regulating lung cancer development remain to be elucidated.

METHOD AND MATERIALS

In this study, we used bioinformatics to screen out autophagy-related lncRNAs from TCGA-LUAD repository. Then the least absolute shrinkage and selection operator (LASSO) regression was applied to establish the signature of autophagy-related lncRNAs so that clinical characteristics and survival in LUAD patients be evaluated. Finally, we selected the most significant differences lncRNA, LINC01559, to verify its function in regulating LUAD progression in vitro.

RESULTS

We found high expression of LINC01559 indicates lymph node metastasis and poor prognosis. Besides, LINC01559 promotes lung cancer cell proliferation and migration in vitro, by enhancing autophagy signal pathway via sponging hsa-miR-1343-3p.

CONCLUSION

We revealed a novel prognostic model based on autophagy-related lncRNAs, and provide a new therapeutic target and for patients with lung adenocarcinoma named LINC01559.

Non-coding RNA-mediated autophagy in cancer: A protumor or antitumor factor?

Autophagy, usually referred to as macroautophagy, is a cytoprotective behavior that helps cells, especially cancer cells, escape crises. However, the role of autophagy in cancer remains controversial. The induction of autophagy is favorable for tumor growth, as it can degrade damaged cell components accumulated during nutrient deficiency, chemotherapy, or other stresses in a timely manner. Whereas the antitumor effect of autophagy might be closely related to its crosstalk with metabolism, immunomodulation, and other pathways. Recent studies have verified that lncRNAs and circRNAs modulate autophagy in carcinogenesis, cancer cells proliferation, apoptosis, metastasis, and chemoresistance via multiple mechanisms. A comprehensive understanding of the regulatory relationships between ncRNAs and autophagy in cancer might resolve chemoresistance and also offer intervention strategies for cancer therapy. This review systematically displays the regulatory effects of lncRNAs and circRNAs on autophagy in the contexts of cancer initiation, progression, and resistance to chemo- or radiotherapy and provides a novel insight into cancer therapy.

Development and Verification of an Autophagy-Related lncRNA Signature to Predict Clinical Outcomes and Therapeutic Responses in Ovarian Cancer

Objective: Long noncoding RNAs (lncRNAs) are key regulators during ovarian cancer initiation and progression and are involved in mediating autophagy. In this study, we aimed to develop a prognostic autophagy-related lncRNA signature for ovarian cancer.

Methods: Autophagy-related abnormally expressed lncRNAs were screened in ovarian cancer with the criteria values of |correlation coefficient| > 0.4 and p < 0.001. Based on them, a prognostic lncRNA signature was established. The Kaplan–Meier overall survival analysis was conducted in high- and low-risk samples in the training, verification, and entire sets, followed by receiver operating characteristics (ROCs) of 7-year survival. Multivariate Cox regression analysis was used for assessing the predictive independency of this signature after adjusting other clinical features. The associations between the risk scores and immune cell infiltration, PD-L1 expression, and sensitivity of chemotherapy drugs were assessed in ovarian cancer.

Results: A total of 66 autophagy-related abnormally expressed lncRNAs were identified in ovarian cancer. An autophagy-related lncRNA signature was constructed for ovarian cancer. High-risk scores were indicative of poorer prognosis compared with the low-risk scores in the training, verification, and entire sets. ROCs of 7-year survival confirmed the well-predictive efficacy of this model. Following multivariate Cox regression analysis, this model was an independent prognostic factor. There were distinct differences in infiltrations of immune cells, PD-L1 expression, and sensitivity of chemotherapy drugs between high- and low-risk samples.

Conclusions: This study constructed an autophagy-related lncRNA signature that was capable of predicting clinical outcomes and also therapeutic responses for ovarian cancer.

Challenges for Better Diagnosis and Management of Pancreatic and Biliary Tract Cancers Focusing on Blood Biomarkers: A Systematic Review

Simple Summary

Pancreatic and biliary tract cancers are malignant tumors that have a very poor prognosis and are resistant to chemotherapy. The later a cancer is detected, the worse the prognosis becomes; therefore, early detection is important. Biomarkers are physiological indices that serve as a guide to indicate the presence or absence of a certain disease, or its progression. The purpose of our research is to summarize previously reported biomarkers for the diagnosis and prognosis of pancreatic and biliary tract cancers.

Abstract

Background: pancreatic cancer (PCa) and biliary tract cancer (BTC) are cancers with a poor prognosis and few effective treatments. One of the reasons for this is late detection. Many researchers are tackling to develop non-invasive biomarkers for cancer, but few are specific for PCa or BTC. In addition, genetic abnormalities occur in cancer tissues, which ultimately affect the expression of various molecules. Therefore, it is important to identify molecules that are altered in PCa and BTC. For this systematic review, a systematic review of Medline and Embase to select biomarker studies of PCa and BTC patients was conducted. Results: after reviewing 72 studies, 79 biomarker candidates were identified, including 22 nucleic acids, 43 proteins, and 14 immune cell types. Of the 72 studies, 61 examined PCa, and 11 examined BTC. Conclusion: PCa and BTC are characterized by nucleic acid, protein, and immune cell profiles that are markedly different from those of healthy subjects. These altered molecules and cell subsets may serve as cancer-specific biomarkers, particularly in blood. Further studies are needed to better understand the diagnosis and prognosis of PCa and BTC.

Helicobacter pylori infection and other bacteria in pancreatic cancer and autoimmune pancreatitis

Helicobacter pylori (H. pylori) is an infectious agent influencing as much as 50% of the world’s population. It is the causative agent for several diseases, most especially gastric and duodenal peptic ulcer, gastric adenocarcinoma and mucosa-associated lymphoid tissue lymphoma of the stomach. A number of other, extragastric manifestations also are associated with H. pylori infection. These include neurological disorders, such as Alzheimer’s disease, demyelinating multiple sclerosis and Parkinson’s disease. There is also evidence for a relationship between H. pylori infection and such dermatological diseases as psoriasis and rosacea as well as a connection with infection and open-angle glaucoma. Generally little is known about the relationship between H. pylori infection and diseases of the pancreas. Most evidence about H. pylori and its potential role in the development of pancreatic diseases concerns pancreatic adenocarcinoma and autoimmune forms of chronic pancreatitis. There is data (albeit not fully consistent) indicating modestly increased pancreatic cancer risk in H. pylori-positive patients. The pathogenetic mechanism of this increase is not yet fully elucidated, but several theories have been proposed. Reduction of antral D-cells in H. pylori-positive patients causes a suppression of somatostatin secretion that, in turn, stimulates increased secretin secretion. That stimulates pancreatic growth and thus increases the risk of carcinogenesis. Alternatively, H. pylori, as a part of microbiome dysbiosis and the so-called oncobiome, is proven to be associated with pancreatic adenocarcinoma development via the promotion of cellular proliferation. The role of H. pylori in the inflammation characteristic of autoimmune pancreatitis seems to be explained by a mechanism of molecular mimicry among several proteins (mostly enzymes) of H. pylori and pancreatic tissue. Patients with autoimmune pancreatitis often show positivity for antibodies against H. pylori proteins. H. pylori, as a part of microbiome dysbiosis, also is viewed as a potential trigger of autoimmune inflammation of the pancreas. It is precisely these relationships (and associated equivocal conclusions) that constitute a center of attention among pancreatologists, immunologists and pathologists. In order to obtain clear and valid results, more studies on sufficiently large cohorts of patients are needed. The topic is itself sufficiently significant to draw the interest of clinicians and inspire further systematic research. Next-generation sequencing could play an important role in investigating the microbiome as a potential diagnostic and prognostic biomarker for pancreatic cancer.

The Use of Zebrafish Xenotransplant Assays to Analyze the Role of lncRNAs in Breast Cancer

Breast cancer represents a great challenge since it is the first cause of death by cancer in women worldwide. LncRNAs are a newly described class of non-coding RNAs that participate in cancer progression. Their use as cancer markers and possible therapeutic targets has recently gained strength. Animal xenotransplants allows for in vivo monitoring of disease development, molecular elucidation of pathogenesis and the design of new therapeutic strategies. Nevertheless, the cost and complexities of mice husbandry makes medium to high throughput assays difficult. Zebrafishes (Danio rerio) represent a novel model for these assays, given the ease with which xenotransplantation trials can be performed and the economic and experimental advantages it offers. In this review we propose the use of xenotransplants in zebrafish to study the role of breast cancer lncRNAs using low to medium high throughput assays.

An EMT-Related Gene Signature for Predicting Response to Adjuvant Chemotherapy in Pancreatic Ductal Adenocarcinoma

Background

For pancreatic ductal adenocarcinoma (PDAC) patients, chemotherapy failure is the major reason for postoperative recurrence and poor outcomes. Establishment of novel biomarkers and models for predicting chemotherapeutic efficacy may provide survival benefits by tailoring treatments.

Methods

Univariate cox regression analysis was employed to identify EMT-related genes with prognostic potential for DFS. These genes were subsequently submitted to LASSO regression analysis and multivariate cox regression analysis to identify an optimal gene signature in TCGA training cohort. The predictive accuracy was assessed by Kaplan–Meier (K-M), receiver operating characteristic (ROC) and calibration curves and was validated in PACA-CA cohort and our local cohort. Pathway enrichment and function annotation analyses were conducted to illuminate the biological implication of this risk signature.

Results

LASSO and multivariate Cox regression analyses selected an 8-gene signature comprised DLX2, FGF9, IL6R, ITGB6, MYC, LGR5, S100A2, and TNFSF12. The signature had the capability to classify PDAC patients with different DFS, both in the training and validation cohorts. It provided improved DFS prediction compared with clinical indicators. This signature was associated with several cancer-related pathways. In addition, the signature could also predict the response to immune-checkpoint inhibitors (ICIs)-based immunotherapy.

Conclusion

We established a novel EMT-related gene signature that was capable of predicting therapeutic response to adjuvant chemotherapy and immunotherapy. This signature might facilitate individualized treatment and appropriate management of PDAC patients.

A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer

BACKGROUND

Gene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer.

METHODS

Somatic mutation data from three cohorts were used to identify the common survival-related gene mutation with Kaplan-Meier curves. RNA-sequencing data were used to explore the signature for survival prediction. First, Weighted Gene Co-expression Network Analysis was conducted to identify candidate genes. Then, the ICGC-PACA-CA cohort was applied as the training set and the TCGA-PAAD cohort was used as the external validation set. A TP53-associated signature calculating the risk score of every patient was developed with univariate Cox, least absolute shrinkage and selection operator, and stepwise regression analysis. Kaplan-Meier and receiver operating characteristic curves were plotted to verify the accuracy. The independence of the signature was confirmed by the multivariate Cox regression analysis. Finally, a prognostic nomogram including 359 patients was constructed based on the combined expression data and the risk scores.

RESULTS

TP53 mutation was screened to be the robust and survival-related mutation type, and was associated with immune cell infiltration. Two thousand, four hundred fifty-five genes included in the six modules generated in the WGCNA were screened as candidate survival related TP53-associated genes. A seven-gene signature was constructed: Risk score = (0.1254 × ERRFI1) - (0.1365 × IL6R) - (0.4400 × PPP1R10) - (0.3397 × PTOV1-AS2) + (0.1544 × SCEL) - (0.4412 × SSX2IP) - (0.2231 × TXNL4A). Area Under Curves of 1-, 3-, and 5-year ROC curves were 0.731, 0.808, and 0.873 in the training set and 0.703, 0.677, and 0.737 in the validation set. A prognostic nomogram including 359 patients was constructed and well-calibrated, with the Area Under Curves of 1-, 3-, and 5-year ROC curves as 0.713, 0.753, and 0.823.

CONCLUSIONS

The TP53-associated signature exhibited good prognostic efficacy in predicting the overall survival of PC patients.

Identification of autophagy-related long non-coding RNA prognostic signature for breast cancer

Autophagy‐related long non‐coding RNAs (lncRNAs) disorders are related to the occurrence and development of breast cancer. The purpose of this study is to explore whether autophagy‐related lncRNA can predict the prognosis of breast cancer patients. The autophagy‐related lncRNAs prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression. We identified five autophagy‐related lncRNAs (MAPT‐AS1, LINC01871, AL122010.1, AC090912.1, AC061992.1) associated with prognostic value, and they were used to construct an autophagy‐related lncRNA prognostic signature (ALPS) model. ALPS model offered an independent prognostic value (HR = 1.664, 1.381‐2.006), where this risk score of the model was significantly related to the TNM stage, ER, PR and HER2 status in breast cancer patients. Nomogram could be utilized to predict survival for patients with breast cancer. Principal component analysis and Sankey Diagram results indicated that the distribution of five lncRNAs from the ALPS model tends to be low‐risk. Gene set enrichment analysis showed that the high‐risk group was enriched in autophagy and cancer‐related pathways, and the low‐risk group was enriched in regulatory immune‐related pathways. These results indicated that the ALPS model composed of five autophagy‐related lncRNAs could predict the prognosis of breast cancer patients.