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

Significance of TP53, CDKN2A, SMAD4 and KRAS in Pancreatic Cancer

The present review demonstrates the major tumor suppressor genes, including TP53, CDKN2A and SMAD4, associated with pancreatic cancer. Each gene's role, prevalence and impact on tumor development and progression are analyzed, focusing on the intricate molecular landscape of pancreatic cancer. In addition, this review underscores the prognostic significance of specific mutations, such as loss of TP53, and explores some potential targeted therapies tailored to these molecular signatures. The findings highlight the importance of genomic analyses for risk assessment, early detection and the design of personalized treatment approaches in pancreatic cancer. Overall, this review provides a comprehensive analysis of the molecular intricacies of pancreatic tumors, paving the way for more effective and tailored therapeutic interventions.

Exploiting the molecular subtypes and genetic landscape in pancreatic cancer: the quest to find effective drugs

Pancreatic Ductal Adenocarcinoma (PDAC) is a very lethal disease that typically presents at an advanced stage and is non-compliant with most treatments. Recent technologies have helped delineate associated molecular subtypes and genetic variations yielding important insights into the pathophysiology of this disease and having implications for the identification of new therapeutic targets. Drug repurposing has been evaluated as a new paradigm in oncology to accelerate the application of approved or failed target-specific molecules for the treatment of cancer patients. This review focuses on the impact of molecular subtypes on key genomic alterations in PDAC, and the progress made thus far. Importantly, these alterations are discussed in light of the potential role of drug repurposing in PDAC.

Construction of a novel signature based on immune-related lncRNA to identify high and low risk pancreatic adenocarcinoma patients

BACKGROUND

Pancreatic adenocarcinoma is one of the most lethal tumors in the world with a poor prognosis. Thus, an accurate prediction model, which identify patients within high risk of pancreatic adenocarcinoma is needed to adjust the treatment and elevate the prognosis of these patients.

METHODS

We obtained RNAseq data of The Cancer Genome Atlas (TCGA) pancreatic adenocarcinoma (PAAD) from UCSC Xena database, identified immune-related lncRNAs (irlncRNAs) by correlation analysis, and identified differential expressed irlncRNAs (DEirlncRNAs) between pancreatic adenocarcinoma tissues from TCGA and normal pancreatic tissues from TCGA and Genotype-Tissue Expression (GTEx). Further univariate and lasso regression analysis were performed to construct prognostic signature model. Then, we calculated the areas under curve and identified the best cut-off value to identify high- and low-risk patients with pancreatic adenocarcinoma. The clinical characteristics, immune cell infiltration, immunosuppressive microenvironment, and chemoresistance were compared between high- and low-risk patients with pancreatic adenocarcinoma.

RESULTS

We identified 20 DEirlncRNA pairs and grouped the patients by the best cut-off value. We proved that our prognostic signature model possesses a remarkable efficiency to predict prognosis of PAAD patients. The AUC for ROC curve was 0.905 for 1-year prediction, 0.942 for 2-year prediction, and 0.966 for 3-year prediction. Patients in high-risk group have poor survival rate and worse clinical characteristics. We also proved that patients in high-risk groups were in immunosuppressive status and may be resistant to immunotherapy. Anti-cancer drug evaluation was performed based on in-silico predated tool, such as paclitaxel, sorafenib, and erlotinib, may be suitable for PAAD patients in high-risk group.

CONCLUSIONS

Overall, our study constructed a novel prognostic risk model based on pairing irlncRNAs, exhibited a promising prediction value in patients with pancreatic adenocarcinoma. Our prognostic risk model may help distinguish PAAD patients suitable for medical treatments.

A new marker constructed from immune-related lncRNA pairs can be used to predict clinical treatment effects and prognosis: in-depth exploration of underlying mechanisms in HNSCC

BACKGROUND

Long non-coding RNA (lncRNA) plays a vital role in tumor proliferation, migration, and treatment. Since it is challenging to standardize the gene expression levels detected by different platforms, the signatures composed of many immune-related single lncRNAs are still inaccurate. Utilizing a gene pair formed of two immune-related lncRNAs and strategically assigning values can effectively meet the demand for a higher-accuracy dual biomarker combination.

METHODS

Co-expression and differential expression analyses were performed on immune genes and lncRNAs data from The Cancer Genome Atlas and the ImmPort database to obtain differentially expressed immune-related lncRNAs for pairwise pairing. The prognostic-related differentially expressed immune-related lncRNAs (PR-DE-irlncRNAs) pairs were then identified by univariate Cox regression and used for lasso regression to construct a prognostic model. Various methods were used to validate the predictive prognostic performance of the model. Additionally, we explored the potential guiding value of the model in immunotherapy and chemotherapy and constructed a nomogram suitable for efficient prognosis prediction. Mechanistic exploration of anti-tumor immunity and mutational perspectives are also included. We also analyzed the correlation between the model and immune checkpoint inhibitors (ICIs)-related, N6-methyadenosine (m6A)-related, and multidrug resistance genes.

RESULTS

We used a total of 20 pairs of PR-DE-irlncRNAs to create a prognosis model. Quantitative real-time polymerase chain reaction experiments further verified the abnormal expression of 11 lncRNAs in HNSCC cells. Various methods have confirmed the excellent performance of the model in predicting patient prognosis. We reasoned that lncRNAs/TP53 mutation might play a positive/negative anti-tumor role through the immune system by multi-perspective analyses. Finally, it was found that the prognostic model was closely related to immunotherapy and chemotherapy as well as the expression of ICIs/m6A/multidrug resistance-related genes.

CONCLUSION

The prognostic model performs excellently in predicting the prognosis of patients and provides the potential value of practical guidance for treatment.

Combined bulk RNA and single-cell RNA analyses reveal TXNL4A as a new biomarker for hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) has a high mortality rate worldwide. The dysregulation of RNA splicing is a major event leading to the occurrence, progression, and drug resistance of cancer. Therefore, it is important to identify new biomarkers of HCC from the RNA splicing pathway.

Methods

We performed the differential expression and prognostic analyses of RNA splicing-related genes (RRGs) using The Cancer Genome Atlas-liver hepatocellular carcinoma (LIHC). The International Cancer Genome Consortium (ICGC)-LIHC dataset was used to construct and validate prognostic models, and the PubMed database was used to explore genes in the models to identify new markers. The screened genes were subjected to genomic analyses, including differential, prognostic, enrichment, and immunocorrelation analyses. Single-cell RNA (scRNA) data were used to further validate the immunogenetic relationship.

Results

Of 215 RRGs, we identified 75 differentially expressed prognosis-related genes, and a prognostic model incorporating thioredoxin like 4A (TXNL4A) was identified using least absolute shrinkage and selection operator regression analysis. ICGC-LIHC was used as a validation dataset to confirm the validity of the model. PubMed failed to retrieve HCC-related studies on TXNL4A. TXNL4A was highly expressed in most tumors and was associated with HCC survival. Chi-squared analyses indicated that TXNL4A expression positively correlated positively with the clinical features of HCC. Multivariate analyses revealed that high TXNL4A expression was an independent risk factor for HCC. Immunocorrelation and scRNA data analyses indicated that TXNL4A was correlated with CD8 T cell infiltration in HCC.

Conclusion

Therefore, we identified a prognostic and immune-related marker for HCC from the RNA splicing pathway.

Identification and validation of the pyroptosis-related long noncoding rna signature to predict the prognosis of patients with bladder cancer

Bladder cancer ranked the second most frequent tumor among urological malignancies. This work investigated bladder cancer prognosis, including the relevance of pyroptosis-related long noncoding RNA (lncRNA) in it and its potential roles. The Cancer Genome Atlas database offered statistics on lncRNAs and clinical data from 411 bladder cancer patients. Pearson correlation analysis was used to evaluate pyroptosis-related lncRNAs. To explore prognosis-associated lncRNAs, we performed univariate Cox regression, least absolute shrinkage and selection operator regression analyses, as well as the Kaplan-Meier method. Multivariate Cox analysis was leveraged to establish the risk score model. Afterward, a nomogram was constructed according to the risk score and clinical variables. Finally, to investigate the potential functions of pyroptosis-related lncRNAs, gene set enrichment analysis was employed. Eleven pyroptosis-related lncRNAs were screened to be closely associated with patients prognosis. On this foundation, a risk score model was created to classify patients into high and low risk groups. The signature was shown to be an independent prognostic factor (P < .001) with an area under the curve of 0.730. Then a nomogram was established including risk scores and clinical characteristics. The nomogram prediction effect is excellent, with a concordance index of 0.86. The 11-lncRNAs signature was associated with the supervision of oxidative stress, epithelial-mesenchymal transition, cell adhesion, TGF-β, and Wingless and INT-1 signaling pathway, according to the gene set enrichment analysis. Our findings indicate that pyroptosis-related lncRNAs, which may affect tumor pathogenesis in many ways, might be exploited to assess the prognosis of bladder cancer patients.

Identification of platinum resistance-related gene signature for prognosis and immune analysis in bladder cancer

Purpose: Currently, there is limited knowledge about platinum resistance-related long non-coding RNAs (lncRNAs) in bladder cancer. We aim to identify platinum resistance-related lncRNAs and construct a risk model for accurate prognostic prediction of bladder cancer. Methods: Transcriptomic and clinical data were extracted from The Cancer Genome Atlas (TCGA) database, and platinum resistance-related genes were obtained from HGSOC-Platinum. The platinum resistance-related lncRNAs were obtained by the Spearman correlation analysis. Then, we constructed a risk score model through Cox regression analysis and the LASSO algorithm. The model was verified by analyzing the median risk score, Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and heatmap. We also developed a nomogram and examined the relationship between the risk score model, immune landscape, and drug sensitivity. Lastly, we assessed the differential expression of PRR-lncRNAs in the cisplatin-resistant bladder cancer cell line and the normal bladder cancer cell line using qRT-PCR. Results: We developed and validated an eight-platinum resistance-related lncRNA risk model for bladder cancer. The risk model showed independent prognostic significance in univariate and multivariate Cox analyses. Based on multivariate analysis, we developed a nomogram. The modified model is both good predictive and clinically relevant after evaluation. Furthermore, immune-related and drug-sensitivity analyses also showed significant differential expression between high and low-risk groups. The qRT-PCR demonstrated that most of the lncRNAs were upregulated in cisplatin-resistance cancerous tissues than in control tissues. Conclusion: We have developed a predictive model based on eight platinum resistance-related lncRNAs, which could add meaningful information to clinical decision-making.

High expression of RNF169 is associated with poor prognosis in pancreatic adenocarcinoma by regulating tumour immune infiltration

Background: Pancreatic adenocarcinoma (PAAD) is a highly deadly and aggressive tumour with a poor prognosis. However, the prognostic value of RNF169 and its related mechanisms in PAAD have not been elucidated. In this study, we aimed to explore prognosis-related genes, especially RNF169 in PAAD and to identify novel potential prognostic predictors of PAAD. Methods: The GEPIA and UALCAN databases were used to investigate the expression and prognostic value of RNF169 in PAAD. The correlation between RNF169 expression and immune infiltration was determined by using TIMER and TISIDB. Correlation analysis with starBase was performed to identify a potential regulatory axis of lncRNA-miRNA-RNF169. Results: The data showed that the level of RNF169 mRNA expression in PAAD tissues was higher than that in normal tissues. High RNF169 expression was correlated with poor prognosis in PAAD. In addition, analysis with the TISIDB and TIMER databases revealed that RNF169 expression was positively correlated with tumour immune infiltration in PAAD. Correlation analysis suggested that the long non-coding RNA (lncRNA) AL049555.1 and the microRNA (miRNA) hsa-miR-324-5p were involved in the expression of RNF169, composing a potential regulatory axis to control the progression of PAAD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that RNF169 plays a role in PAAD through pathways such as TNF, Hippo, JAK-STAT and Toll-like receptor signaling. Conclusion: In summary, the upregulation of RNF169 expression mediated by ncRNAs might influence immune cell infiltration in the microenvironment; thus, it can be used as a prognostic biomarker and a potential therapeutic target in PAAD.

LncRNA PTOV1-AS2 Promotes Colon Cancer Progression through the miR-145-5p/FSCN1 Axis

Objective

The long noncoding RNA (lncRNA) gene PTOV1-AS2 is a potentially oncogenic lncRNA gene. However, its role and regulatory mechanism in the occurrence and development of colon cancer are still unclear. In this study, the lncRNA PTOV1-AS2 was used as a starting point to investigate the role of competing endogenous RNA (ceRNA) regulatory mechanisms in colon cancer.

Methods

The expression of lncRNA PTOV1-AS2 mRNA in colon cancer tissues and cell lines was measured by quantitative real-time polymerase chain reaction (qRT-PCR) and screened for differential expression in cells. We examined the effects of lncRNA PTOV1-AS2 overexpression or downregulation of its expression on various cellular processes in HCT116 and SW620 cells after the transfection with an overexpression construct or PTOV1-AS2p-specific shRNA, respectively. In particular, we examined the effects on cell proliferation, migration, and invasion using the cell counting kit-8 CCK-8 assay and Transwell migration and invasion assays, respectively. In addition, the binding targets of lncRNA PTOV1-AS2/miR-145-5p and miR-145-5p/FSCN1 were predicted using various bioinformatics tools and validated by a dual luciferase assay. We also examined the effect of the lncRNA PTOV1-AS2/miR-145-5p axis on FSCN1 expression by qRT-PCR analysis. Furthermore, we investigated the effect of the PTOV1-AS2/miR-145-5p/FSCN1 axis on the biological function of colon cancer cells using an in vitro colon cancer cell model with reduced expression of PTOV1-AS2 and simultaneous transfection of a miR-145-5p inhibitor or FSCN1 vector. Additionally, we established a colon cancer xenograft tumor nude mouse model and used it to investigate the effect of locally injected lncRNA PTOV1-AS2 vector on the tumor growth and survival status of tumor-bearing mice.

Results

We found that PTOV1-AS2 was highly expressed in colon cancer, which was associated with worse survival. High expression of PTOV1-AS2 promoted cell proliferation, migration, and invasion, while low expression of PTOV1-AS2 inhibited these processes in HCT116 and SW620 cells. The microRNA miR-145-5p was found to bind to the 3'-UTR region of both PTOV1-AS2 and FSCN1. In addition, miR-145-5p decreased the protein expression of its target gene FSCN1 and reduced the PTOV1-AS2-induced expression of FSCN1 in colon cancer cell lines. Also, silencing miR-145-5p or enhancing FSCN1 expression could partially restore the inhibition of cell proliferation, migration, invasion, and the tumorigenic capacity caused by silencing the expression of PTOV1-AS2 in vitro and in vivo.

Conclusion

PTOV1-AS2 promotes colon cancer progression by "sponging" miR-145-5p to upregulate FSCN1.

UHMK1 Is a Novel Marker for Personalized Prediction of Pancreatic Cancer Prognosis

Pancreatic ductal adenocarcinoma (PDAC) is among the leading causes of cancer mortality, and new therapeutic options are urgently needed. Long noncoding RNA H19 (H19) is known to promote PDAC progression, but the downstream genes of H19 are largely unknown. Five PDAC cell lines, nonmalignant pancreatic cells, TCGA, GEO-derived pancreatic tissues (malignant, n=413; nonmalignant, n=234), a pancreatic tissue array (n=96), and pancreatic tissues from our clinic (malignant, n=20; nonmalignant, n=20) were examined by a gene array, RT-qPCR, Western blotting, MTT, colony formation, wound-healing, siRNA-mediated gene silencing, bioinformatics, xenotransplantation, and immunohistochemistry assays. The cell cycle inhibitor, UHMK1, was identified to have the strongest correlation with H19. UHMK1 expression was enhanced in PDAC, and high UHMK1 expression correlated with tumor stage, and lower overall survival. siRNA-mediated UHMK1 downregulation inhibited progression signaling. siRNA-mediated downregulation of H19 or UHMK1 inhibited tumor proliferation and xenograft growth. Based on the correlation between UHMK1 expression and clinical parameters, we developed a nomogram that reliably predicts patient prognosis and overall survival. Together, we characterized UHMK1 as an H19-induced oncogene and verified it as a novel PDAC prognostic marker for overall survival.

Long non-coding RNA LINC00680 functions as a ceRNA to promote esophageal squamous cell carcinoma progression through the miR-423-5p/PAK6 axis

Background

Esophageal squamous cell carcinoma (ESCC) is a common invasive malignancy worldwide with poor clinical outcomes. Increasing amount of long non-coding RNAs (lncRNAs) have been reported to be involved in cancer development. However, lncRNAs that are functional in ESCC and the underlying molecular mechanisms remain largely unknown.

Methods

Transcriptomic analysis was performed to identify dysregulated lncRNAs in ESCC tissue samples. The high expression of LINC00680 in ESCC was validated by RT-qPCR, and the oncogenic functions of LINC00680 was investigated by cell proliferation, colony formation, migration and invasion assays in ESCC cells in vitro and xenografts derived from ESCC cells in mice. RNA-seq, competitive endogenous RNA (ceRNA) network analysis, and luciferase reporter assays were carried out to identify LINC00680 target genes and the microRNAs (miRNAs) bound to LINC00680. Antisense oligonucleotides (ASOs) were used for in vivo treatment.

Results

Transcriptome profiling revealed that a large number of lncRNAs was dysregulated in ESCC tissues. Notably, LINC00680 was highly expressed, and upregulation of LINC00680 was associated with large tumor size, advanced tumor stage, and poor prognosis. Functionally, knockdown of LINC00680 restrained ESCC cell proliferation, colony formation, migration, and invasion in vitro and inhibited tumor growth in vivo. Mechanistically, LINC00680 was found to act as a ceRNA by sponging miR-423-5p to regulate PAK6 (p21-activated kinase 6) expression in ESCC cells. The cell viability and motility inhibition induced by LINC00680 knockdown was significantly reversed upon PAK6 restoration and miR-423-5p inhibition. Furthermore, ASO targeting LINC00680 substantially suppressed ESCC both in vitro and in vivo.

Conclusions

An oncogenic lncRNA, LINC00680, was identified in ESCC, which functions as a ceRNA by sponging miR-423-5p to promote PAK6 expression and ESCC. LINC00680/miR-423-5p/PAK6 axis may serve as promising diagnostic and prognostic biomarkers and therapeutic targets for ESCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-022-01539-3.

Recognition of Glycometabolism-Associated lncRNAs as Prognosis Markers for Bladder Cancer by an Innovative Prediction Model

The alteration of glycometabolism is a characteristic of cancer cells. Long non-coding RNAs (lncRNAs) have been documented to occupy a considerable position in glycometabolism regulation. This research aims to construct an effective prediction model for the prognosis of bladder cancer (BC) based on glycometabolism-associated lncRNAs (glyco-lncRNAs). Pearson correlation analysis was applied to get glyco-lncRNAs, and then, univariate cox regression analysis was employed to further filtrate survival time-associated glyco-lncRNAs. Multivariate cox regression analysis was utilized to construct the prediction model to divide bladder cancer (BC) patients into high- and low-risk groups. The overall survival (OS) rates of these two groups were analyzed using the Kaplan-Meier method. Next, gene set enrichment analysis and Cibersortx were used to explore the enrichment and the difference in immune cell infiltration, respectively. pRRophetic algorithm was applied to explore the relation between chemotherapy sensitivity and the prediction model. Furthermore, reverse transcriptase quantitative polymerase chain reaction was adopted to detect the lncRNAs constituting the prediction signature in tissues and urine exosomal samples of BC patients. A powerful model including 6 glyco-lncRNAs was proposed, capable of suggesting a risk score for each BC patient to predict prognosis. Patients with high-risk scores demonstrated a shorter survival time both in the training cohort and testing cohort, and the risk score could predict the prognosis without depending on the traditional clinical traits. The area under the receiver operating characteristic curve of the risk score was higher than that of other clinical traits (0.755 > 0.640, 0.485, 0.644, or 0.568). The high- and low-risk groups demonstrated very distinct immune cells infiltration conditions and gene set enriched terms. Besides, the high-risk group was more sensitive to cisplatin, docetaxel, and sunitinib. The expression of lncRNA AL354919.2 featured with an increase in low-grade patients and a decrease in T3-4 and Stage III-IV patients. Based on the experiment results, lncRNA AL355353.1, AC011468.1, and AL354919.2 were significantly upregulated in tumor tissues. This research furnishes a novel reference for predicting the prognosis of BC patients, assisting clinicians with help in the choice of treatment.

A TP53-associated immune prognostic signature for the prediction of the overall survival and therapeutic responses in pancreatic cancer

Pancreatic cancer (PC) is a highly fatal disease correlated with an inferior prognosis. The tumor protein p53 (TP53) is one of the frequent mutant genes in PC and has been implicated in prognosis. We collected somatic mutation data, RNA sequencing data, and clinical information of PC samples in the Cancer Genome Atlas (TCGA) database. TP53 mutation was an independent prognostic predictor of PC patients. According to TP53 status, Gene set enrichment analysis (GSEA) suggested that TP53 mutations were related to the immunophenotype of pancreatic cancer. We identified 102 differentially expressed immune genes (DEIGs) based on TP53 mutation status and developed a TP53-associated immune prognostic model (TIPM), including Epiregulin (EREG) and Prolactin receptor (PRLR). TIPM identified the high-risk group with poor outcomes and more significant response potential to cisplatin, gemcitabine, and paclitaxel therapies. And we verified the TIPM in the International Cancer Genome Consortium (ICGC) cohort (PACA-AU) and Gene Expression Omnibus (GEO) cohort (GSE78229 and GSE28735). Finally, we developed a nomogram that reliably predicts overall survival in PC patients on the bias of TIPM and other clinicopathological factors. Our study indicates that the TIPM derived from TP53 mutation patterns might be an underlying prognostic therapeutic target. But more comprehensive researches with a large sample size is necessary to confirm the potential.

Annotation of pituitary neuroendocrine tumors with genome-wide expression analysis

Pituitary neuroendocrine tumors (PitNETs) are common, generally benign tumors with complex clinical characteristics related to hormone hypersecretion and/or growing sellar tumor mass. PitNETs can be classified based on the expression pattern of anterior pituitary hormones and three main transcriptions factors (TF), SF1, PIT1 and TPIT that regulate differentiation of adenohypophysial cells. Here, we have extended this classification based on the global transcriptomics landscape using tumor tissue from a well-defined cohort comprising 51 PitNETs of different clinical and histological types. The molecular profiles were compared with current classification schemes based on immunohistochemistry. Our results identified three main clusters of PitNETs that were aligned with the main pituitary TFs expression patterns. Our analyses enabled further identification of specific genes and expression patterns, including both known and unknown genes, that could distinguish the three different classes of PitNETs. We conclude that the current classification of PitNETs based on the expression of SF1, PIT1 and TPIT reflects three distinct subtypes of PitNETs with different underlying biology and partly independent from the expression of corresponding hormones. The transcriptomic analysis reveals several potentially targetable tumor-driving genes with previously unknown role in pituitary tumorigenesis.

Elevated Ras related GTP binding B (RRAGB) expression predicts poor overall survival and constructs a prognostic nomogram for colon adenocarcinoma

Currently, no articles have explored the roles of RRAGB gene in the occurrence and development of cancer. By means of The Cancer Genome Atlas (TCGA) data mining, we found that this gene might be a novel prognostic predictor for colon adenocarcinoma (COAD). Hence, this article was carried out to explore its roles in COAD and associations with immunity. RRAGB single-gene expression matrix and corresponding clinical information were extracted from TCGA database. Univariate/multivariate cox regression analyses and gene set enrichment analysis (GSEA) were utilized to identify independent prognostic factors and RRAGB related pathways, respectively. Relationships between RRAGB and immunity were also analyzed. Boxplot and K-M survival analysis indicated that RRAGB was not only differently expressed in COAD (P < 0.05), but also significantly associated with overall survival (OS; P < 0.05). Univariate and multivariate Cox hazard regression analyses indicated that RRAGB could serve as an independent prognostic factor for COAD (both P < 0.05). GSEA identified five signaling pathways significantly enriched in the high-RRAGB expression phenotype. Moreover, a RRAGB-based nomogram was successfully constructed and displayed a satisfactory performance. In addition, RRAGB expression was found to be significantly associated with microsatellite instability (MSI), tumor mutational burden (TMB) and immunity. Our results revealed that RRAGB could be a prognostic biomarker for COAD in terms of OS and markedly related to MSI, TMB, and immunity. We also constructed an RRAGB-based nomogram with a satisfactory performance. Further researches should be carried out to validate our findings.