Prognostic Value of Drug Targets Predicted using Deep Bioinformatic Analysis of M6A-associated lncRNA-based Pancreatic Cancer Model Characteristics and its Tumour Microenvironment

doi:10.21203/rs.3.rs-1200367/v1

PDF

Research Article

Prognostic Value of Drug Targets Predicted using Deep Bioinformatic Analysis of M6A-associated lncRNA-based Pancreatic Cancer Model Characteristics and its Tumour Microenvironment

https://doi.org/10.21203/rs.3.rs-1200367/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 25 Apr, 2022

Read the published version in Frontiers in Genetics →

Version 1

posted 29 Dec, 2021

You are reading this latest preprint version

Background: The role of N6-methyladenosine (m6A)-associated long-stranded non-coding RNA (lncRNA) in pancreatic cancer is unclear. Therefore, we analysed the characteristics and tumour microenvironment in pancreatic cancer and determined the value of m6A-related lncRNAs for prognosis and drug target prediction.

Methods: An m6A-lncRNA co-expression network was constructed using The Cancer Genome Atlas database to screen m6A-related lncRNAs. Prognosis-related lncRNAs were screened using univariate Cox regression; patients were divided into high- and low-risk groups and randomised into training and test groups. In the training group, least absolute shrinkage and selection operator (LASSO) was used for regression analysis and to construct a prognostic model, which was validated in the test group. Tumour mutational burden (TMB), immune evasion, and immune function of risk genes were analysed using R; drug sensitivity and potential drugs were examined using the Genomics of Drug Sensitivity in Cancer database.

Results: We screened 129 m6A-related lncRNAs; 17 prognosis-related m6A-related lncRNAs were obtained using multivariate analysis and three m6A-related lncRNAs (AC092171.5, MEG9, AC002091.1) were screened using LASSO regression. Survival rates were significantly higher (P < 0.05) in the low-risk than in the high-risk group. Risk score was an independent predictor affecting survival (P < 0.001), with the highest risk score being obtained by calculating the c-index. The TMB significantly differed between the high- and low-risk groups (P < 0.05). In the high- and low-risk groups, mutations were detected in 61 of 70 samples and 49 of 71 samples, respectively, with KRAS, TP53, and SMAD4 showing the highest mutation frequencies in both groups. A lower survival rate was observed in patients with a high versus low TMB. Immune function HLA, Cytolytic activity, and Inflammation-promoting, T cell co-inhibition, Check-point, and T cell co-stimulation significantly differed in different subgroups (P < 0.05). Immune evasion scores were significantly higher in the high-risk group than in the low-risk group. Eight sensitive drugs were screened: ABT.888, ATRA, AP.24534, AG.014699, ABT.263, axitinib, A.443654, and A.770041.

Conclusions: We screened m6A-related lncRNAs using bioinformatics, constructed a prognosis-related model, explored TMB and immune function differences in pancreatic cancer, and identified potential therapeutic agents, providing a foundation for further studies of pancreatic cancer diagnosis and treatment.

Cancer Biology

pancreatic cancer

N6-methyladenosine

long non-coding RNA

tumour immune microenvironment

Pancreatic cancer is currently one of the most malignant tumours worldwide and is difficult to diagnose in early stages, leading to poor treatment outcomes and high mortality rates, thus posing a serious global public health risk. According to the 2020 global statistics, there are approximately 500,000 new cases of pancreatic cancer and 460,000 deaths each year^[1] . The main treatment modality is surgery supplemented with chemotherapy and immune-targeted therapy; however, the resectability rate using surgery is only 15–20%, and adjuvant treatments show limited effects, leading to an overall 5-year survival rate of less than 8% ^[2-4]. Therefore, it is important to identify effective biomarkers for determining the prognosis of patients with pancreatic cancer.

Long non-coding RNAs (lncRNAs) can be classified as signalling, decoy, guide, and scaffold lncRNAs according to their functions. Aberrant expression of lncRNAs disrupts homeostasis in organisms and may drive or inhibit various cancers ^[5]. For example, downregulation of LINC01232 can inhibit the proliferation and spread of pancreatic cancer cells to improve prognosis ^[6] . N6-methyladenosine (m6A) is an epigenetic regulatory alteration of RNA ^[7] that affects the processing, transport, and stability of lncRNAs through internal modifications and thus influences biological processes ^[8]. m6A-RNA modifications, which are the most abundant modifications in the tumour immune microenvironment (TME), are key factors affecting RNA functions ^{[9, 10]}. m6A enhances the translation of intracellular lysosomal cathepsins by acting on mRNA to inhibit the antigen-presenting action of classical dendritic cells and activation ability of T cells. However, modification with m6A reduces the recruitment of MDSCs and increases the infiltration of CD8⁺ T cells ^[11] .

In recent years, advances in immunotherapy have led to promising results in a subset of tumours such as lung cancer and melanoma ^[12]. Immunotherapy suppresses tumours by altering the TME. Jansen et al. ^[13] showed that tumour cells can be killed by promoting infiltrative aggregation of CD8⁺ T cells. Whether m6A-related lncRNAs exert similar effects on pancreatic cancer remains unclear. In addition, the prognostic impact of m6A-related lncRNAs in the TME of pancreatic cancer has not been widely examined. Therefore, we constructed a model of m6A-related lncRNA and screened these lncRNAs as markers for determining the prognosis of pancreatic cancer. This study was conducted to explore the relationship with of lncRNAs with the TME and screen for relevant drugs and markers for pancreatic cancer prognosis and immunotherapy.

Collection and processing of pancreatic cancer data

We obtained data from 182 patients with pancreatic cancer from The Cancer Genome Atlas (TCGA) database (https://portal. gdc.cancer.gov/) and identifying information was redacted from all patients. A total of 178 patient samples was defined as a combined set, which was randomly divided into a test group and training group. The data were normalised, processed, and analysed using R software 4.1.1 (The R Group for Statistical Computing, Vienna, Austria).

Screening of m6A-associated lncRNAs

We downloaded GTF files from Ensembl (http://asia.Ensembl.org) and identified 24 m6A-related genes, including writers (METTL3, METTL14, METTL16, WTAP, VIRMA, ZC3H13, RBM15, RBM15B), readers (YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3, HNRNPC, FMR1, LRPPRC, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, and RBMX), and erasers (FTO and ALKBH5). The m6A genes and lncRNAs in the samples were evaluated using Pearson correlation analysis, with pre-set criteria of correlation> 0.4 and P < 0.001.

Modelling and evaluation of m6A-associated lncRNA gene markers

Multivariate Cox regression analysis was used to assess independent prognostic factors for pancreatic cancer. Expression data of candidate lncRNAs were obtained using univariate Cox proportional risk analysis and least absolute shrinkage and selection operator (LASSO) optimisation, and m6A-lncRNAs significantly associated with prognosis were identified. These lncRNAs were used to construct a risk profile for each sample. The samples were divided into two groups according to the characteristics and coefficients: high- and low-risk groups, and receiver operating characteristic (ROC) curves were generated using the "survival" and "survivalROC" packages in R software. These ROC curves were used to analyse the 1-, 3-, and 5-year survival rates of patients and assess the accuracy of genetic characteristics for predicting survival. The "pheatmap" package was used to draw a lncRNA expression heat map. The "survcomp" package was used to calculate the c-index for assessing the best model prediction.

Assessment of tumour immune microenvironment and mutational load and potential drug screening using m6A-associated lncRNA models

The selected m6A-associated lncRNAs were divided into high- and low-risk groups for differential analysis. To evaluate the immune cell infiltration data of pancreatic cancer and associated immune functions, we downloaded the immune set files and used the "limma, GSVA, GSEABase, pheatmap, reshape2" package to determine and visualise the correlations of high- and low-risk groups with individual immune checkpoints. TIDE was downloaded at http://tide.dfci.harvard.edu/login/ to print documents. The TIDE scores of high- and low-risk samples were calculated and visualised using "limma, ggpubr" in R. The difference between the tumour loads of the high- and low-risk groups were compared using "maftools" in R and visualised in a waterfall plot. The Genomics of Drug Sensitivity in Cancer database (https://cancerrxgene.org) is used for large-scale drug screening and evaluation of the tumour response to chemotherapeutic agents in combination with genomic analysis. Using the Genomics of Drug Sensitivity in Cancer database, the half-maximal inhibitory concentration (IC₅₀) of drugs commonly used for pancreatic cancer tumours was calculated to assess the clinical application of this model in the treatment of pancreatic cancer. We then used Wilcoxon signed-rank test to compare the difference in IC₅₀ between the high- and low-risk groups. To visualise the data, box plots were drawn using "PRrophytic" and "ggplot2" in R.

m6A-associated lncRNAs and their prognostic values

The expression profiles of 24 common m6A genes and 14,056 lncRNAs were obtained by analysing 178 pancreatic adenocarcinoma samples from TCGA. Through the Pearson test, we screened 129 m6A-associated lncRNAs (Fig1A), which were used to derive 17 m6A-associated lncRNAs with prognostic value via co-expression analysis and univariate Cox regression analysis, the results of which are shown in the following figure (Fig1B).

TCGA cohort-based m6A-associated lncRNA model construction

To eliminate covariate collinearity and avoid overfitting of the prognostic model, LASSO regression analysis was performed for the 17 differentially expressed m6A-associated lncRNAs with prognostic value, revealing three lncRNAs that could be further analysed using multivariate Cox regression. Ultimately, m6A-lncRNAs were used to determine the prognosis of patients with pancreatic adenocarcinoma based on the expression values of three lncRNAs (AC092171.5, MEG9, and AC002091.1)(Fig1E). The genes showing high expression levels were AC092171.5 [hazard ratio (HR) = 0.439, 95% confidence interval (CI) = 0.197–0.976)], MEG9 (HR = 0.309, 95% CI = 0.114–0.838), AC002091.1 (HR = 0.522, 95% CI = 0.288–0.954). The Cox coefficients of the three lncRNAs were used for modelling to calculate the prognostic risk scores for each patient in TCGA cohort as follows: -(1.20237572230772 × expression level of AC092171.5) - (0.975350163451454 × expression level of MEG9) - (1.49408312354077 × expression level of AC002091.1). Multivariate Cox regression analysis，Cox univariate analysis and c-index curve showed that the risk scores were significantly associated with survival and independent of clinical parameters(Figure2A, B, C). Subsequently, all patients were divided into high- and low-risk groups based on the median risk scores(Fig3A, B). In the high-risk subgroup, the expression levels of AC092171.5, MEG9, and AC002091.1 were lower Compared to low-risk group(Fig3E). According to the survival curves, the overall survival rate was lower in the high-risk subgroup than in the low-risk subgroup. The ROC curves showed AUC values of 0.710, 0.745, and 0.838 at 1, 3, and 5 years, respectively(Fig3D). Therefore, the time-dependent ROC curves validated the prognostic value of the risk score.

Validation of test group-based m6A-lncRNA model

To validate the prediction reliability of the training group m6A-lncRNA, the 88 samples in the test group were divided into high- and low-risk subgroups based on their median risk scores, which were calculated as described for the training group cohort(Fig4A, B). In the risk curve, the survival rate of the high-risk gene group was lower than that of the low-risk gene group(Fig4C). In the heat map, the expression of risk genes was similar to that in the training group(Fig4E). In the survival curve, the survival time of the high-risk gene group was significantly shorter than that of the low-risk gene group. The AUC values at 1, 3, and 5 years in the time-dependent ROC curve were 0.688, 0.746, and 0.919, respectively.(Fig4D)

Tumour mutation load analysis

The tumour mutation burden (TMB) indices of high- and low-risk genes were calculated separately; as shown in the violin plot (Fig5A), the TMB of differed for high- and low-risk genes (P < 0.05), with a higher TMB in the high-risk group. The waterfall plot shows the top 30 mutation frequencies. In the high-risk group (Fig5B), mutations were detected in 61 of 70 samples; KRAS (71%), TP53 (64%), and SMAD4 (23%) showed the highest mutation frequencies. In the low-risk group (Fig5C), mutations were detected in 49 of 71 samples, with KRAS (41%), TP53 (45%), and SMAD4 (15%) showing the highest mutation frequencies. As shown in the figure (Fig5D), the survival rate of patients with a high TMB was lower than that of patients with a low TMB. Additionally, the mutation frequency of high-risk genes was greater than that of genes in the low-risk group, and the survival rate of the high-TMB+high-risk gene group was lowest, followed by the low-TMB+high-risk gene, high-TMB+low-risk gene, and low-TMB+low-risk gene groups (Fig5E).

Differences in immune function

We observed significant differences in immune-related functions between the high- and low-risk groups (Fig6A). As a practical tool for assessing anti-tumour immunity, HLA was significantly lower in the high-risk subgroup, indirectly indicating weaker HLA function in this subgroup; TypeII IFN Reponse was lower in the high-risk group than in the low-risk group. In addition, Cytolytic activity, Inflammation-promoting, T cell co-inhibition, Check-point, and T cell co-stimulation showed lower expression in the high-risk group than in the low-risk group. As shown in the figure (Fig6B), the two groups were significantly different (P < 0.001), with higher TIDE scores in the high-risk than in the low-risk, indicating that the low-risk group has lower immune evasion potential and that immunotherapy has lower efficacy in the high-risk group.

Screening for potential drugs

Screening revealed eight potential therapeutic drugs which showed different sensitivities between high- and low-risk groups (P < 0.05): For ABT.888, ATRA, AP.24534, AG.014699, ABT.263, and axitinib, the IC₅₀ in the low-risk group was less than that in the high-risk group (Fig7A-E), indicating that patients in the low-risk group were more sensitive to the drugs. For A.443654 and A.770041, the IC₅₀ in the low-risk group was lower than that in the high-risk group, indicating that patients in the high-risk group are more sensitive to these drugs (Fig7F-J).

Pancreatic cancer is a common and highly malignant tumour of the digestive system that is currently difficult to detect, diagnose, and treat early because of its unique biology. The main therapeutic options for pancreatic cancer include surgery, chemotherapy, and radiotherapy ^{[14, 15]}. However, only approximately 20% of patients can be treated with surgery ^[16]. Studies ^[17] have shown that the median survival time after surgery is only 24–30 months. Therefore, it is important to find a reliable biomarker for determining the prognosis of pancreatic cancer. LncRNAs play important roles in biological processes by regulating gene expression and influencing various aspects of these processes such as transcriptional regulation, gene imprinting, and chromatin remodelling ^{[18, 19]}. In previous studies, m6A-related lncRNAs were shown to determine the prognosis of lung adenocarcinoma and low-grade glioma ^{[20, 21]}. Therefore, m6A-related lncRNAs show potential as valuable targets for the diagnosis and treatment of pancreatic cancer. In this study, we integrated m6A-related lncRNAs from the Ensembl database and m6A-related lncRNAs from TCGA database and their corresponding clinical data, which were used to construct a prognostic model of m6A-associated lncRNAs and internal validation. The relationship between various genes and the immune microenvironment was explored and screened to identify relevant drugs.

We performed a series of bioinformatics analyses to develop an m6A-associated lncRNA model for determining the prognosis of pancreatic cancer. Three lncRNAs (AC092171.5, MEG9, AC002091.1) were screened for subsequent analysis. Guyugang et al. ^[22] found that AC092171.5 was expressed at lower levels in patients with high-risk lung adenocarcinoma, and Wei et al. ^[23] found that patients with high AC002091.1 expression in osteosarcoma had a better prognosis. In our study, MEG9, AC092171.5, and AC002091.1 were highly expressed in low-risk patients; these three lncRNAs may suppress pancreatic adenocarcinogenesis. In the training group, the survival of patients in the high-risk group was shorter than that in the low-risk group, whereas the test group showed consistent results. Taken together, these results confirm that our m6A-associated lncRNA model can accurately predict survival time in this population.

In addition, m6A methylation modifications play integral roles in innate and adaptive immune responses, which may be useful for further studies of pancreatic cancer pathogenesis. Immune cells in the TME of pancreatic cancer are in a state of quantity and functional imbalance, and have clearly defective anti-tumour functions, resulting in a unique TME ^[24]. The TME of pancreatic cancer is characterised by dense fibrosis and massive inflammatory cell infiltration, which prevent infiltration of effector cells such as T cells and natural killer cells into the tumour. Through this mechanism, pancreatic cancer cells can evade host immune surveillance. In addition, these inflammatory cells affect pancreatic carcinogenesis and inhibit the chemotherapeutic effects of relevant chemotherapeutic agents, such as gemcitabine ^[25]. McGranahan et al. ^[26] showed that reduced or absent HLA-1 expression prevents a presented antigen from being recognised by T cells, thus causing immune evasion of the tumour. In our study, HLA function was lower in the high-risk group than in the low-risk group, suggesting a greater capacity for immune evasion in the high-risk group. In addition, tumour surface antigens can bind to effector cell immune checkpoint molecules, which can promote in vivo effector T cell hypofunction or apoptosis to promote immune evasion by tumours ^[27]. We observed lower immune checkpoint function in the high-risk group than in the low-risk group, which may also contribute to poor prognosis.

In recent years, immunotherapy has shown initial success in treating malignant tumours and good efficacy towards melanoma, lung cancer, and other malignant tumours ^[28]. Exploring potentially mutated genes in pancreatic cancer can facilitate diagnosis and rational treatment selection. KARS and TP53 showed high mutation rates in both the high- and low-risk groups. Cullis et al. ^[29] showed that mutations in KRAS are an important cause of the immunosuppressive state of tumours, as mutations in KRAS impair recognition of pancreatic cancer cells by T cells to result in immune evasion ^[30]. In addition, immunosuppression in the TME is related to mutations in KRAS. A related study ^[31] showed that mutations in the TP53 gene are common in tumours and affect the recruitment and activity of T cells, thus also leading to immune evasion ^[32]. These m6A-associated lncRNA gene mutations are closely associated with immune activity in the TME, suggesting an interaction between m6A modifications and tumour immunogenomics.

Our study had some limitations. First, this study was limited to TCGA database, and no further validation was performed using external databases. Second, our results must be validated in further experiments, and we have collected relevant clinical samples to perform this validation. Finally, animal and clinical trials are needed to explore the therapeutic effects of the predicted drugs on pancreatic cancer. However, our study provides a foundation for further exploration of biomarkers and immune mechanisms for diagnosing and treating pancreatic cancer based on m6A-related lncRNAs.

We screened 57 differentially expressed m6A-associated lncRNAs with prognostic value based on 182 pancreatic cancer samples and identified three lncRNAs based on expression to construct and validate an m6A-lncRNA model. We found that these genes are correlated with the immune function characteristics of the TME and tumour conformity and calculated immune evasion scores for high- and low-risk groups. Finally, we screened relevant drugs to further identify tumour immunophenotypes to guide clinical applications.

N6-methyladenosine , m6A. Non-coding RNA, lncRNA. Least absolute shrinkage and selection operator , LASSO. Tumour mutational burden , TMB. Tumour immune microenvironment , TME. The Cancer Genome Atlas , TCGA. Half-maximal inhibitory concentration , IC₅₀.

Ethics approval and consent to participate:

Not applicable.

Consent for publication:

Not applicable

Availability of data and materials:

The data is truly available. The authors could provide the analytical code of R language for reasonable request.

Competing interests

The all authors have no financial or other conflicts of interest to disclose.

Funding

There is no source of funding for research and/or publication.

Authors’ contributions

Peng-Wei Cao: Design the study, writing. Lei Liu: Data analysis and drawing. Zi-Han Li: Data checking. Feng Cao: Revise the manuscript pictures and supervision. Fu-bao Liu: Conceptualization, Methodology, Software

Acknowledgements

The authors thank Editage (https://www.editage.cn) for providing linguistic assistance.

Sung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA Cancer J Clin, 2021,71(3):209-249.
Shaib W L, Ip A, Cardona K, et al. Contemporary Management of Borderline Resectable and Locally Advanced Unresectable Pancreatic Cancer[J]. Oncologist, 2016,21(2):178-187.
Ying H, Dey P, Yao W, et al. Genetics and biology of pancreatic ductal adenocarcinoma[J]. Genes Dev, 2016,30(4):355-385.
McGuigan A, Kelly P, Turkington R C, et al. Pancreatic cancer: A review of clinical diagnosis, epidemiology, treatment and outcomes[J]. World J Gastroenterol, 2018,24(43):4846-4861.
Bhan A, Soleimani M, Mandal S S. Long Noncoding RNA and Cancer: A New Paradigm[J]. Cancer Res, 2017,77(15):3965-3981.
Li Q, Lei C, Lu C, et al. LINC01232 exerts oncogenic activities in pancreatic adenocarcinoma via regulation of TM9SF2[J]. Cell Death Dis, 2019,10(10):698.
Sunwoo H, Wu J Y, Lee J T. The Xist RNA-PRC2 complex at 20-nm resolution reveals a low Xist stoichiometry and suggests a hit-and-run mechanism in mouse cells[J]. Proc Natl Acad Sci U S A, 2015,112(31):E4216-E4225.
Zhou K I, Parisien M, Dai Q, et al. N(6)-Methyladenosine Modification in a Long Noncoding RNA Hairpin Predisposes Its Conformation to Protein Binding[J]. J Mol Biol, 2016,428(5 Pt A):822-833.
Ma S, Chen C, Ji X, et al. The interplay between m6A RNA methylation and noncoding RNA in cancer[J]. J Hematol Oncol, 2019,12(1):121.
Yang G, Sun Z, Zhang N. Reshaping the role of m6A modification in cancer transcriptome: a review[J]. Cancer Cell Int, 2020,20:353.
Han D, Liu J, Chen C, et al. Anti-tumour immunity controlled through mRNA m(6)A methylation and YTHDF1 in dendritic cells[J]. Nature, 2019,566(7743):270-274.
Liu L, Bai X, Wang J, et al. Combination of TMB and CNA Stratifies Prognostic and Predictive Responses to Immunotherapy Across Metastatic Cancer[J]. Clin Cancer Res, 2019,25(24):7413-7423.
Jansen C S, Prokhnevska N, Master V A, et al. An intra-tumoral niche maintains and differentiates stem-like CD8 T cells[J]. Nature, 2019,576(7787):465-470.
Murphy J E, Wo J Y, Ryan D P, et al. Total Neoadjuvant Therapy With FOLFIRINOX Followed by Individualized Chemoradiotherapy for Borderline Resectable Pancreatic Adenocarcinoma: A Phase 2 Clinical Trial[J]. JAMA Oncol, 2018,4(7):963-969.
De Dosso S, Siebenhuner A R, Winder T, et al. Treatment landscape of metastatic pancreatic cancer[J]. Cancer Treat Rev, 2021,96:102180.
King J, Bouvet M, Singh G, et al. Improving theranostics in pancreatic cancer[J]. J Surg Oncol, 2017,116(1):104-113.
Siegel R L, Miller K D, Fuchs H E, et al. Cancer Statistics, 2021[J]. CA Cancer J Clin, 2021,71(1):7-33.
Kanduri C. Long noncoding RNA and epigenomics[J]. Adv Exp Med Biol, 2011,722:174-195.
Ponting C P, Oliver P L, Reik W. Evolution and functions of long noncoding RNAs[J]. Cell, 2009,136(4):629-641.
Wang W, Li J, Lin F, et al. Identification of N(6)-methyladenosine-related lncRNAs for patients with primary glioblastoma[J]. Neurosurg Rev, 2021,44(1):463-470.
Lin S, Choe J, Du P, et al. The m(6)A Methyltransferase METTL3 Promotes Translation in Human Cancer Cells[J]. Mol Cell, 2016,62(3):335-345.
Guo Y, Qu Z, Li D, et al. Identification of a prognostic ferroptosis-related lncRNA signature in the tumor microenvironment of lung adenocarcinoma[J]. Cell Death Discov, 2021,7(1):190.
Wei J, Fang D L, Huang C K, et al. Screening a novel signature and predicting the immune landscape of metastatic osteosarcoma in children via immune-related lncRNAs[J]. Transl Pediatr, 2021,10(7):1851-1866.
Zheng Q, Hou J, Zhou Y, et al. The RNA helicase DDX46 inhibits innate immunity by entrapping m(6)A-demethylated antiviral transcripts in the nucleus[J]. Nat Immunol, 2017,18(10):1094-1103.
Erkan M, Hausmann S, Michalski C W, et al. The role of stroma in pancreatic cancer: diagnostic and therapeutic implications[J]. Nat Rev Gastroenterol Hepatol, 2012,9(8):454-467.
McGranahan N, Rosenthal R, Hiley C T, et al. Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution[J]. Cell, 2017,171(6):1259-1271.
Kalbasi A, Ribas A. Tumour-intrinsic resistance to immune checkpoint blockade[J]. Nat Rev Immunol, 2020,20(1):25-39.
Ohaegbulam K C, Assal A, Lazar-Molnar E, et al. Human cancer immunotherapy with antibodies to the PD-1 and PD-L1 pathway[J]. Trends Mol Med, 2015,21(1):24-33.
Cullis J, Das S, Bar-Sagi D. Kras and Tumor Immunity: Friend or Foe?[J]. Cold Spring Harb Perspect Med, 2018,8(9).
Soreide K, Sund M. Epidemiological-molecular evidence of metabolic reprogramming on proliferation, autophagy and cell signaling in pancreas cancer[J]. Cancer Lett, 2015,356(2 Pt A):281-288.
Merz V, Gaule M, Zecchetto C, et al. Targeting KRAS: The Elephant in the Room of Epithelial Cancers[J]. Front Oncol, 2021,11:638360.
Blagih J, Buck M D, Vousden K H. p53, cancer and the immune response[J]. J Cell Sci, 2020,133(5).

No competing interests reported.

PDF

Journal Publication

published 25 Apr, 2022

Read the published version in Frontiers in Genetics →

Version 1

posted 29 Dec, 2021

You are reading this latest preprint version

Prognostic Value of Drug Targets Predicted using Deep Bioinformatic Analysis of M6A-associated lncRNA-based Pancreatic Cancer Model Characteristics and its Tumour Microenvironment

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Collection and processing of pancreatic cancer data

Screening of m6A-associated lncRNAs

Modelling and evaluation of m6A-associated lncRNA gene markers

Assessment of tumour immune microenvironment and mutational load and potential drug screening using m6A-associated lncRNA models

Results

m6A-associated lncRNAs and their prognostic values

TCGA cohort-based m6A-associated lncRNA model construction

Validation of test group-based m6A-lncRNA model

Tumour mutation load analysis

Differences in immune function

Screening for potential drugs

Discussion

Conclusions

Abbreviations

Declarations

Ethics approval and consent to participate:

Consent for publication:

Availability of data and materials:

Competing interests

Funding

Authors’ contributions

Acknowledgements

References

Competing Interests

Status:

Journal Publication

Version 1