Abstract
Purpose
Our study first explored the expression differences and prognostic significance of Cx genes in pan-cancer and then focused on LUAD. Our objectives were to conducted a comprehensive analysis of the expression profile, prognostic significance, genetic alterations, potential biological functions and drug sensitivity of the Connexin gene family in LUAD.
Methods
We developed a comprehensive prognostic model for LUAD by combining risk scores with clinical features and created a nomogram to predict 1-, 3-, and 5-year overall survival. Using single-cell sequencing, we examined the expression and biological functions of the identified prognostic markers.
Results
Our risk model revealed that GJB2-5 play a critical role in the prognosis of LUAD patients, associated with many biological processes such as cell cycle, DNA damage, EMT, hypoxia, invasion, and metastasis. Furthermore, the connexin gene family is linked to transcriptional mechanisms such as the extracellular matrix (ECM), migration, mobility, angiogenesis, and the epithelial-mesenchymal transition (EMT) genetic program.
Conclusion
The risk model can be used as a potential prognostic factor for LUAD patients and may provide new insights into cancer treatment from perspective of the expression of Cx genes.
Similar content being viewed by others
Data availability
All data generated or analysed during this study are included in this published article and its supplementary information files.
References
Aasen T, Mesnil M, Naus CC et al (2016) Gap junctions and cancer: communicating for 50 years. Nat Rev Cancer 16:775–788. https://doi.org/10.1038/nrc.2016.105
Aasen T, Leithe E, Graham SV et al (2019) Connexins in cancer: bridging the gap to the clinic. Oncogene 38:4429–4451. https://doi.org/10.1038/s41388-019-0741-6
Bai D (2016) Structural analysis of key gap junction domains—lessons from genome data and disease-linked mutants. Semin Cell Dev Biol 50:74–82. https://doi.org/10.1016/j.semcdb.2015.11.015
Bruzzone R, Hormuzdi SG, Barbe MT et al (2003) Pannexins, a family of gap junction proteins expressed in brain. Proc Natl Acad Sci USA 100:13644–13649. https://doi.org/10.1073/pnas.2233464100
Camps J, Noël F, Liechti R et al (2023) Meta-analysis of human cancer single-cell RNA-seq datasets using the IMMUcan database. Can Res 83:363–373. https://doi.org/10.1158/0008-5472.CAN-22-0074
Duggan MA, Anderson WF, Altekruse S et al (2016) The Surveillance, epidemiology, and end results (SEER) program and pathology: toward strengthening the critical relationship. Am J Surg Pathol 40:e94–e102. https://doi.org/10.1097/PAS.0000000000000749
Ezumi K, Yamamoto H, Murata K et al (2008) Aberrant expression of connexin 26 is associated with lung metastasis of colorectal cancer. Clin Cancer Res 14:677–684. https://doi.org/10.1158/1078-0432.CCR-07-1184
Friedl P, Wolf K (2003) Tumour-cell invasion and migration: diversity and escape mechanisms. Nat Rev Cancer 3:362–374. https://doi.org/10.1038/nrc1075
Geeleher P, Cox N, Huang RS (2014a) pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS ONE 9:e107468. https://doi.org/10.1371/journal.pone.0107468
Geeleher P, Cox NJ, Huang RS (2014b) Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol 15:R47. https://doi.org/10.1186/gb-2014-15-3-r47
Goldstraw P, Ball D, Jett JR et al (2011) Non-small-cell lung cancer. The Lancet 378:1727–1740. https://doi.org/10.1016/S0140-6736(10)62101-0
Goodenough DA, Paul DL (2009) Gap junctions. Cold Spring Harb Perspect Biol 1:a002576–a002576. https://doi.org/10.1101/cshperspect.a002576
Gu Z, Gu L, Eils R et al (2014) circlize implements and enhances circular visualization in R. Bioinformatics 30:2811–2812. https://doi.org/10.1093/bioinformatics/btu393
Huo Y, Zhou Y, Zheng J et al (2022) GJB3 promotes pancreatic cancer liver metastasis by enhancing the polarization and survival of neutrophil. Front Immunol 13:983116. https://doi.org/10.3389/fimmu.2022.983116
Ito A, Koma Y, Uchino K et al (2006) Increased expression of connexin 26 in the invasive component of lung squamous cell carcinoma: significant correlation with poor prognosis. Cancer Lett 234:239–248. https://doi.org/10.1016/j.canlet.2005.03.049
Johnson RG, Sheridan JD (1971) Junctions between cancer cells in culture: ultrastructure and permeability. Science 174:717–719. https://doi.org/10.1126/science.174.4010.717
Kyo N, Yamamoto H, Takeda Y et al (2008) Overexpression of connexin 26 in carcinoma of the pancreas. Oncol Rep 19:627–631
Laird DW, Lampe PD (2018) Therapeutic strategies targeting connexins. Nat Rev Drug Discov 17:905–921. https://doi.org/10.1038/nrd.2018.138
Laird DW, Naus CC, Lampe PD (2017) SnapShot: connexins and disease. Cell 170:1260-1260.e1. https://doi.org/10.1016/j.cell.2017.08.034
Lin Y-P, Wu J-I, Tseng C-W et al (2019) Gjb4 serves as a novel biomarker for lung cancer and promotes metastasis and chemoresistance via Src activation. Oncogene 38:822–837. https://doi.org/10.1038/s41388-018-0471-1
Liu Y, Pandey PR, Sharma S et al (2019) ID2 and GJB2 promote early-stage breast cancer progression by regulating cancer stemness. Breast Cancer Res Treat 175:77–90. https://doi.org/10.1007/s10549-018-05126-3
Liu C-J, Hu F-F, Xie G-Y et al (2023) GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. Brief Bioinf 24:bbac558. https://doi.org/10.1093/bib/bbac558
Loewenstein WR, Kanno Y (1966) Intercellular communication and the control of tissue growth: lack of communication between cancer cells. Nature 209:1248–1249. https://doi.org/10.1038/2091248a0
Lorusso G, Wyss CB, Kuonen F et al (2022) Connexins orchestrate progression of breast cancer metastasis to the brain by promoting FAK activation. Sci Transl Med. 14:eaax8933. https://doi.org/10.1126/scitranslmed.aax8933
McNutt NS, Weinstein RS (1969) Carcinoma of the cervix: deficiency of nexus intercellular junctions. Science 165:597–599. https://doi.org/10.1126/science.165.3893.597
Naoi Y, Miyoshi Y, Taguchi T et al (2007) Connexin26 expression is associated with lymphatic vessel invasion and poor prognosis in human breast cancer. Breast Cancer Res Treat 106:11–17. https://doi.org/10.1007/s10549-006-9465-8
Osswald M, Jung E, Sahm F et al (2015) Brain tumour cells interconnect to a functional and resistant network. Nature 528:93–98. https://doi.org/10.1038/nature16071
Payton BW, Bennett MVL, Pappas GD (1969) Permeability and Structure of Junctional Membranes at an Electrotonic Synapse. Science 166:1641–1643. https://doi.org/10.1126/science.166.3913.1641
Plante I, Stewart MKG, Barr K et al (2011) Cx43 suppresses mammary tumor metastasis to the lung in a Cx43 mutant mouse model of human disease. Oncogene 30:1681–1692. https://doi.org/10.1038/onc.2010.551
Riggi N, Aguet M, Stamenkovic I (2018) Cancer metastasis: a reappraisal of its underlying mechanisms and their relevance to treatment. Annu Rev Pathol Mech Dis 13:117–140. https://doi.org/10.1146/annurev-pathol-020117-044127
Ritchie ME, Phipson B, Wu D et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47–e47. https://doi.org/10.1093/nar/gkv007
Saito-Katsuragi M, Asada H, Niizeki H et al (2007) Role for connexin 26 in metastasis of human malignant melanoma: communication between melanoma and endothelial cells via connexin 26. Cancer 110:1162–1172. https://doi.org/10.1002/cncr.22894
Siegel RL, Miller KD, Jemal A (2018) Cancer statistics 2018. CA A Cancer J Clin. 68:7–30. https://doi.org/10.3322/caac.21442
Szklarczyk D, Gable AL, Lyon D et al (2019) STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–D613. https://doi.org/10.1093/nar/gky1131
Udaka N, Miyagi Y, Ito T (2007) Connexin expression in mouse lung tumor. Cancer Lett 246:224–229. https://doi.org/10.1016/j.canlet.2006.02.020
Valastyan S, Weinberg RA (2011) Tumor metastasis: molecular insights and evolving paradigms. Cell 147:275–292. https://doi.org/10.1016/j.cell.2011.09.024
Wolf K, Wu YI, Liu Y et al (2007) Multi-step pericellular proteolysis controls the transition from individual to collective cancer cell invasion. Nat Cell Biol 9:893–904. https://doi.org/10.1038/ncb1616
Wu Y, Fu L, Wang B et al (2022) Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family-related genes. Clin Lab Anal. https://doi.org/10.1002/jcla.24419
Yang J, Qin G, Luo M et al (2015) Reciprocal positive regulation between Cx26 and PI3K/Akt pathway confers acquired gefitinib resistance in NSCLC cells via GJIC-independent induction of EMT. Cell Death Dis 6:e1829–e1829. https://doi.org/10.1038/cddis.2015.197
Yu G, Wang L-G, Han Y, He Q-Y (2012) clusterprofiler: an r package for comparing biological themes among gene clusters. OMICS J Integr Biol. 16:284–287. https://doi.org/10.1089/omi.2011.0118
Yuan H, Yan M, Zhang G et al (2019) CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res 47:D900–D908. https://doi.org/10.1093/nar/gky939
Zhang D, Chen C, Li Y et al (2012) Cx31.1 acts as a tumour suppressor in non-small cell lung cancer (NSCLC) cell lines through inhibition of cell proliferation and metastasis. J Cell Mol Med 16:1047–1059. https://doi.org/10.1111/j.1582-4934.2011.01389.x
Zhou Y, Zhou B, Pache L et al (2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10:1523. https://doi.org/10.1038/s41467-019-09234-6
Funding
This work was supported by a grant obtained from the Qilu leader training project (Na Zhou).
Author information
Authors and Affiliations
Contributions
PJ and NZ: conceived the study. Material preparation, data collection and analysis were performed by PJ, XH and BD. The first draft of the manuscript was written by PJ and all authors commented on previous versions of the manuscript. XZ and NZ: completed model guidance, critical review, and funding support. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Jiang, P., Huo, X., Dong, B. et al. Multi-omics analysis of expression profile and prognostic values of connexin family in LUAD. J Cancer Res Clin Oncol 149, 12791–12806 (2023). https://doi.org/10.1007/s00432-023-05075-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00432-023-05075-5