Acquisition and validation of four painful subtypes of colon adenocarcinoma and prognostic analysis

doi:10.21203/rs.3.rs-4335882/v1

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Acquisition and validation of four painful subtypes of colon adenocarcinoma and prognostic analysis

https://doi.org/10.21203/rs.3.rs-4335882/v1

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Background:

Colon adenocarcinoma (COAD) is the most common type of colorectal cancer. Pain is a multidimensional unpleasant experience and various molecular and cellular pathways are implicated in pain signaling. Nevertheless, the exploration of pain-related genes related to colon adenocarcinoma is not clear yet.

Methods:

In this study, the pathways enriched for pain-related genes were analyzed by Metascape. Then, we obtained pain subtypes versus classical subtypes and explored the link between the two. Next, marker genes for different pain subtypes were identified, the enrichment pathways were explored and these marker genes were used to validate the pain subtypes. We then performed an investigation of survival differences between pain subtypes by selecting specific top pathways in each subtype, calculating top pathway scores, and calculating pathway differences by heatmap and Kruskal test. Finally, we predicted the response of different pain subtypes to immunotherapy.

Results:

A total of 146 pain-related genes were enrolled in this study and we finally obtained 4 painful subtypes and 4 stable subtypes. The marker genes for subtypes were validated by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets and found to have a worse prognosis for CS1. The genes of CS1, CS2, CS3 and CS4 markers were mainly enriched in the pathways of Focal adhesion, Human T cell leukemia virus1 infection, Metabolic pathway, and Pertussis, respectively. CS1 and CS4 are more immunogenic. Moreover, CS1 is more sensitive to treatment with CTLA4 inhibitors, CS4 is sensitive to treatment with PD-1 inhibitors.

Conclusions:

Our study's identification of four pain subtypes of COAD provides new ideas for personalised therapy for patients with COAD.

Biological sciences/Cancer

Biological sciences/Computational biology and bioinformatics

colon adenocarcinoma

painful subtype

tumor microenvironment

Colon adenocarcinoma (COAD) is the third most common for men and women, accounting for 9.7% of all cancers^[1]. Based on data from the Global Cancer Observatory (GCO) (gco.iarc.fr), it was estimated that there would be 1.4 million new cases of colon cancer and 500,000 deaths worldwide in 2020^[2]. Although COAD-related mortality and morbidity rates have slowly but steadily declined, hundreds of thousands of people are still affected yearly ^[3]. COAD and its treatment, chemotherapy, biological agents, and radiation therapy often result in significant physical and psychosocial symptoms and adverse reactions in patients. The high symptom burden in cancer patients can lead to severe limitations in their daily activities, poor functioning, disability, and overall impairment in health-related quality of life^[4].

Pain is one of cancer patients' three most common and debilitating symptoms^{[5, 6]}. Unrelenting pain leaves patients without comfort and dramatically affects their activity, mobility, engagement with family and friends, and their whole quality of life^[7]. Considerable oncological evidence suggests that quality of life and survival are associated with early and effective palliative care, such as pain management.^[8–12] Only 50% of cancer patients worldwide currently experience pain relief, yet around 38% of people with cancer still suffer from moderate to severe pain^[13]. Many studies of cancer patients show that pain remains underrated, inadequately assessed, and undertreated^[14–18].

Disease- and treatment-related toxicity, such as pain, is common in patients with COAD and may lead to a reduction in their overall quality of life^{[19, 20]}. The incidence of chronic pain after surgical cancer treatment varies significantly with the type of cancer, treatment method, age, inherited variability, and psychiatric factors^[21]. Oncogenes have now been found to be associated with the development of cancer with pain^[22]. The findings on pain-related genes in cancer patients can provide a reference for the development of the target drugs for tumor therapy, which is of great significance for alleviating pain and improving the quality of life of cancer patients^[23]. However, the research on pain subtypes in colon adenocarcinoma is lacking.

This study utilized 146 pain-related genes obtained from Genecard and Fragments Per Kilobase of exon model per Million mapped fragments (FPKM)transcriptome data, methylation data, sample annotation, and survival information from TCGA database (https://portal.gdc.cancer.gov/) for colon adenocarcinoma and RNA-seq dataset from GEO database (http://www.ncbi.nlm.nih.gov/geo/) to explore the linkage between pain subtypes and classical subtypes by analyzing pain-related gene enrichment pathways and differentiating pain subtypes of COAD. By integrating survival information from the TCGA and GEO cohorts, survival differences between different pain subtypes will be explored. Finally, the immune microenvironment of COAD patients and the response of different pain subtypes to immunotherapy will be explored. In conclusion, the discovery of pain subtypes of COAD provides new ideas for the study of pain and the treatment of patients with COAD.

Data acquisition and preprocessing

The 146 pain-related genes were obtained from Genecard (https://www.genecards.org). The FPKM transcriptome data (from the TCGA database), methylation data, sample annotation and survival information for COAD were downloaded from the UCSC Xena database (https://xena.ucsc.edu/), and the COAD- RNA-seq dataset GSE39582 from the GEO database. The 146 pain-associated gene-enriched pathways were analyzed by Metascape (https://metascape.org/) and mapped network pathways using Cytoscape(3.8.2).

Acquisition of COAD pain subtypes and classical subtypes

Nonnegative Matrix Factorization was used to differentiate pain subtypes of COAD based on the expression of pain genes. The R package CMScaller^[24] was also used to identify the classical COAD subtypes and explore the link between pain and classical subtypes.

Validation of pain subtypes

We integrated survival information from the TCGA and GEO cohorts to explore differences in survival between pain subtypes. The PAM (Partitioning Around Medoids) algorithm was used to identify marker genes of different pain subtypes and to examine the pathways of marker gene enrichment. The KEGG pathway enrichment analysis was then performed on the marker genes of different subtypes. These marker genes were used as templates, and the pain isoforms were validated by the NTP (NearestTemplatePrediction) algorithm in an external dataset (GSE39582) using the templates.

Calculation of differences in specific top pathways

We used clusterProfiler for Gene Set Enrichment Analysis (GSEA) (KEGG gene set) to select specific top courses in each typing. Then, we calculated top pathway scores by ssGSEA and estimated pathway differences by heat map and Kruskal test.

Differences in the immune microenvironment of different subtypes

MeTIL(methylation of tumour-infiltrating lymphocyte) scores for TCA COAD patients are calculated using PCA ^[25]. Immune score and matrix score are calculated for patients by using the estimate package. The immune microenvironment of COAD patients was calculated using curated immune signatures (including 22 CIBERSORT signatures and 2 MCP counter signatures). It was calculated for the expression of common immune checkpoints. The above signatures were then explored for differences in pain subtypes, and heat maps were drawn.

Sensitivity of different subtypes of immunotherapy

We also used Submap to compare the expression profiles of our defined pain subtypes with another published dataset containing 47 melanoma patients who responded to immunotherapy to predict the response of different pain subtypes to immunotherapy.

Acquisition of pain-related gene enrichment pathways

Pain-related genes were mainly enriched in RNA splicing via transesterification reactions with bulged adenosine as a nucleophile, snRNP Assembly, COPI-dependent Golgi-to-ER retrograde traffic, and Amyotrophic lateral sclerosis (Fig. 1A). We obtained four core modules by finding the critical genes through the MCODE (molecular complex detection) algorithm, especially the genes in the red module are the most important, including UBL5, AQR, EFTUD2, SNU13, MFAP1, SNRPB, SNRPD2, SNRPD3, SART1, DHX8, PRPF8, CRNKL1, CWC22, FUS etc. (Fig. 1B).

Obtained and validated four pain subtypes

Clustering of COAD samples by NMF revealed that the samples were optimally typed at r = 4 (Fig. 2A). The matrix heat map also showed the four subtypes of the models (CS1, CS2, CS3, CS4) (Fig. 2B); UMAP showed the distribution of the subtypes (Fig. 2C) and found that the four subtypes were well differentiated. CMScaller classified COAD into four stable subtypes (CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable, and immune solid activation; CMS2 (canonical, 37%). Epithelial, marked WNT and MYC signalling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; CMS4 (mesenchymal, 23%), prominent transforming growth factor-β activation, stromal invasion, and angiogenesis. (Fig. 2D, E). We compared the pain subtypes with stable subtypes and found that CS1 was more associated with CMS4, and CS3 was more associated with CMS2 (Fig. 2F).

Internal and external datasets to validate subtypes and analyze the prognosis of subtypes

After the marker genes for the subtypes were selected by the PAM algorithm, these marker genes were used as templates to validate the pain subtypes in the TCGA and GEO datasets by the NTP algorithm(Fig. 3A, B). Then, we analyzed the differences in survival between the 4 pain subtypes. Although the survival differences between the four pain subtypes were insignificant, CS1 showed a poorer prognosis than the other subtypes (Fig. 3C, D).

Pathways of Maker Gene Enrichment by Subtypes

We can clearly distinguish the four subtypes from each other by the expression of the maker gene of each subtype as seen in the heatmap (Fig. 4A). By performing KEGG pathway enrichment analysis of marker genes of different subtypes we found that the marker genes of CS1 are mainly enriched in pathways such as Focal adhesion (Fig. 4B); CS2 marker genes are enriched primarily in tracks such as Human T cell leukemia virus1 infection (Fig. 4C); CS3 marker genes are mainly enriched in pathways such as Metabolic pathway (Fig. 4D); CS4 marker genes are enriched primarily in tracks such as Pertussis (Fig. 4E).

Distinctive pathways of different subtypes

Figure 5A shows the significantly highly expressed pathways of different subtypes. CS1 is mainly enriched in pathways such as focal adhesion, regulation of actin cytoskeleton, ECM receptor interaction, vascular smooth muscle contraction, arrhythmogenic right ventricular cardiomyopathy arvc, dilated cardiomyopathy, hypertrophic cardiomyopathy hcm, in which the dysregulation of focal adhesion is thought to be associated with tumour invasion^[26]. Figure 5B shows the different subtypes of significantly lower expressed pathways.

Differences in the tumor microenvironment between subtypes

We found that CS1 and CS4 had higher expression of immune checkpoints and a higher degree of immune cell infiltration (Fig. 6A). Also, CS1 and CS4 had higher ImmuneScore and StromalScore (Fig. 6B, C). This shows that CS1 and CS4 have more robust immunogenicity. The four subtypes of MeTIL were similar (Fig. 6D). These results suggest that CS1 and CS4 have a better prognosis compared to CS2 and CS3.

Sensitivity of different subtypes to immunotherapy as well as chemotherapies

We found that CS4 was more sensitive to treatment with PD-1 inhibitors, and CS1 was more sensitive to treatment with CTLA4 inhibitors (Nominal p value < 0.05) (Fig. 7A). The four subtypes differed in their treatment to standard chemotherapeutic agents such as Bexarotene, Bortezomib, Camptothecin, Cytarabine, Cisplatin, Cytarabine, Docetaxel, Gefitinib, Methotrexate, Temsirolimus, where CS4 showed lower IC50 values for these chemotherapeutic agents, indicating that CS4 is more sensitive to these chemotherapies (Fig. 7B).

Pain is the most common symptom in cancer patients referred to palliative care^[27]. The total number of cancer patients/survivors is forecast to exceed 20 million by 2026^[28]. The number of people suffering from cancer pain will also keep on increasing. Pain can affect social relationships with family and caregivers, and concerns about pain are the second most common problem reported by relatives caring for someone with advanced cancer at home^[29]. Pain is a major problem for people with colon adenocarcinoma and does not improve with time. Stoma procedures are typical for these sufferers and may cause a unique experience of pain, worry, and disconnect from ordinary activities^[30]. Patients with colon adenocarcinoma who have chronic pain are more likely to report psychological distress in the years following therapy^{[31, 32]}. Furthermore, many studies have shown that people with painful colon adenocarcinoma are more likely to suffer from negative emotions such as anxiety and depression than those without pain^{[33, 34]}. Adverse symptoms such as pain can be very burdensome for patients with colon adenocarcinoma and can have a very negative impact on the overall health-related quality of life (QoL) of patients with colon cancer^[35]. Pain-related genes play an important role in the prognosis and pain-related effects of cancer patients, and low-expression of pain-related genes improves the survival of cancer patients^[23]. This study is a correlation analysis of pain subtypes in COAD to suggest some solutions for COAD treatment.

We obtained and validated four pain subtypes (CS1, CS2, CS3, CS4) and four stable subtypes (CMS1, CMS2, CMS3, CMS4) by pathway analysis of pain gene enrichment associated with colon adenocarcinoma. By comparing the pain subtypes with the classical subtypes, we found that CS1 was more associated with CMS4, and CS3 was more associated with CMS2. The marker genes for these subtypes, which we subsequently selected by the PAM algorithm, were also validated in the TCGA and GEO datasets; however, the analysis of survival differences performed between the four pain subtypes was not statistically significant. Yet, CS1 had a poorer prognosis compared to the performance of several other subtypes.

We then analyzed the enrichment pathways of marker genes for each subtype. The marker genes of CS1 were mainly enriched in pathways such as Focal adhesion, Focal adhesion affects almost all aspects of cellular life^[36], of which the integrin family is a central part^[37]. Integrin heterodimers can trigger signalling in cancer, and the behaviour of cancer cells is determined by integrin expression patterns^[38]. Once integrins are activated, downstream cascades, including the PI3K/AKT and MAPK pathways, are initiated to promote tumour survival and progression. Integrins are present in tumour cells and tumour-associated host cells and profoundly affect the tumour cells and the tumour microenvironment^[39]. Integrin α3 and β3 transcription and focal adhesion signalling are required for colorectal cancer growth and migration^[40]. The marker genes of CS2 were mainly enriched in pathways such as Human T cell leukemia virus1(HTLV-1)infection, HTLV-1-infected cells contribute to the freedom of infected cells from host immunosurveillance, inducing T cells to develop anergy and immunosuppressive functions^[41]. And the marker genes of CS3 were mainly enriched in pathways such as the Metabolic pathway. The marker genes of CS4 were mainly enriched in pathways such as Pertussis. The study of these enrichment pathways has led to a better understanding of the different subtypes of marker genes.

Over time, it has been recognized that cancer is an evolutionary and ecological process associated with continuous, dynamic, and interactive interaction between cancer cells and the tumour microenvironment (TME)^[42]. The TME includes all noncancerous host cells in the tumour, such as the fibroblasts, endothelium, neurons, adipocytes, adaptive and innate immune cells, as well as their noncellular components, including the extracellular matrix (ECM), and soluble products such as chemokines, cytokines, growth factors, and extracellular vesicles^[43]. Targeting TME offers significant therapeutic advantages over direct targeting of cancer cells, which are susceptible to drug resistance due to their genomic instability. In contrast, non-tumour cells in TME are genetically more stable and vulnerable^[43]. Next, we analyzed the differences in the TME between the different subtypes. We showed that CS1 and CS4 had higher expression of immune checkpoints and immune cell infiltration, as well as higher ImmuneScore and StromalScore, indicating that there were more immune or matrix components in the TME, so CS1 and CS4 were more immunogenic. This provides new ideas for us to treat different subtypes of colon adenocarcinoma.

Recently, immune checkpoint molecules such as PD-1 have been identified as targets for immunotherapy in colon adenocarcinoma^[44]. Human colon cancer cells express functional PD-1, and it also blocks the growth of human colon cancer cells^[45]. CTLA-4 has been correlated with persistent clinical responses against a diverse range of cancer types has excellent potential as a novel cancer treatment ^[46–48], and is the first to be approved for cancer treatment^[49]. Finally, the sensitivity of different subtypes to immunotherapy and chemotherapy was analyzed, with CS4 being more sensitive to PD-1 inhibitors and CS1 being more sensitive to CTLA-4 inhibitors, and the four subtypes differing in their sensitivity to common chemotherapeutic agents such as Bexarotene, Bortezomib, Camptothecin, Cytarabine, Cisplatin, Cytarabine, Docetaxel, Gefitinib, Methotrexate, and Temsirolimus. Cisplatin, Cytarabine, Docetaxel, Gefitinib, Methotrexate, Temsirolimus, and other common chemotherapeutic agents, with CS4 showing lower IC50 values for these chemotherapeutic agents, indicating that CS4 is more sensitive to these chemotherapies. These results provide new findings for the immunotherapy of COAD as well as chemotherapy.

This article relies on publicly available databases for data collection, and while these databases are widely used and provide a valuable resource, they have inherent limitations, including potentially inconsistent, biased, and incomplete data. Clinical validation and further experimental evidence to support these findings would be beneficial. Also, gene sets and datasets are not large enough to draw broad conclusions or generalize findings to the entire population. The limited sample size may limit the statistical efficacy and significance of the results.

Our study obtained for the first time four pain subtypes of COAD as well as the prognosis and immunogenicity of different subtypes, which provides new ideas for clinical personalised treatment of COAD patients.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

All authors declare no conflicts of interest.

Funding

This study was supported by the Henan Province technology innovation outstanding young talent training project (YXKC2021018).

Authors' Contributions

DK Y, LL L and ZS L contributed to conception and design of the study, DK Y and LL L analyzed data and drafted the manuscript, YW X and LL L contributed to the execution of experiments and the statistical analysis, DK Y, ZS L and YW X reviewed and edited the manuscript for intellectual content.

Acknowledgements

Not applicable

Availability of materials and data

All data generated or analyzed during this study are included in this published article.

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No competing interests reported.

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Acquisition and validation of four painful subtypes of colon adenocarcinoma and prognostic analysis

Status:

Version 1

Abstract

Background:

Methods:

Results:

Conclusions:

Figures

INTRODUCTION

MATERIALS AND METHODS

Data acquisition and preprocessing

Acquisition of COAD pain subtypes and classical subtypes

Validation of pain subtypes

Calculation of differences in specific top pathways

Differences in the immune microenvironment of different subtypes

Sensitivity of different subtypes of immunotherapy

RESULTS

Acquisition of pain-related gene enrichment pathways

Obtained and validated four pain subtypes

Internal and external datasets to validate subtypes and analyze the prognosis of subtypes

Pathways of Maker Gene Enrichment by Subtypes

Distinctive pathways of different subtypes

Differences in the tumor microenvironment between subtypes

Sensitivity of different subtypes to immunotherapy as well as chemotherapies

DISCUSSION

CONCLUSION

Declarations

References

Additional Declarations

Status:

Version 1