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. 2022 Jul 8:2022:9935705.
doi: 10.1155/2022/9935705. eCollection 2022.

A Hypoxia-Related lncRNA Signature Correlates with Survival and Tumor Microenvironment in Colorectal Cancer

Xinyang Zhong  1   2 , Xuefeng He  1   2 , Yaxian Wang  1   2 , Zijuan Hu  2   3   4   5 , Deshui Yu  6 , Huixia Huang  2   3   4   5 , Senlin Zhao  1   2 , Ping Wei  2   3   4   5 , Dawei Li  1   2
Affiliations

A Hypoxia-Related lncRNA Signature Correlates with Survival and Tumor Microenvironment in Colorectal Cancer

Xinyang Zhong et al. J Immunol Res. .

Abstract

The hypoxic tumor microenvironment and long noncoding RNAs (lncRNAs) are pivotal in cancer progression and correlate with the survival outcome of patients. However, the role of hypoxia-related lncRNAs (HRLs) in colorectal cancer (CRC) development remains largely unknown. Herein, we developed a hypoxia-related lncRNA signature to predict patients' survival and immune infiltration. The RNA-sequencing data of 500 CRC patients were obtained from The Cancer Genome Atlas (TCGA) dataset, and HRLs were selected using Pearson's analysis. Next, the Cox regression analysis was applied to construct a risk signature consisting of 9 HRLs. This signature could predict the overall survival (OS) of CRC patients with high accuracy in training, validation, and entire cohort. This signature was an independent risk factor and exerted predictive ability in different subgroups. Functional analysis revealed different molecular features between high- and low-risk groups. A series of drugs including cisplatin showed different sensitivities between the two groups. The expression pattern of immune checkpoints was also distinct between the two clusters in this model. Furthermore, the high-risk group had higher immune, stromal, and ESTIMATE score and a more repressive immune microenvironment than the low-risk group. Moreover, MYOSLID, one of the lncRNAs in this signature, could significantly regulate the proliferation, invasion, and metastasis of CRC.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Construction of a hypoxia-related lncRNA signature. (a) Work flow chart of this study. (b) Forest plot of the 27 lncRNAs that correlated with the OS of CRC patients. (c and d) LASSO Cox regression analysis. (e) Coefficients of 9 lncRNAs. (f) Heat map that showed the relationship between hypoxia-related genes and 9 lncRNAs.
Figure 2
Figure 2
Predictive ability of the lncRNA signature. (a–c) The Kaplan-Meier plots in training, validation, and total cohort. (d–f) ROC curves in training, validation, and total cohort. (g–i) Risk curves and scatter plots in training, validation, and total cohort. (j–l) Heat map that showed the expression pattern of 9 lncRNAs in high- and low-risk group.
Figure 3
Figure 3
Predictive ability of the lncRNA signature. (a–c) Forest plot of the univariate Cox regression in training, validation, and total cohort. (d–f) Forest plot of the multivariate Cox regression in training, validation, and total cohort. (g–r) The Kaplan-Meier plots in different subgroups.
Figure 4
Figure 4
Nomogram. (a and b) Nomogram of training and total cohort. (c–j) The 1-, 3-, 5-, and 7-year calibration curves in training and total cohort.
Figure 5
Figure 5
Functional analysis. (a–c) Bar plots that include representative GO terms in biological process (a), molecular function (b), and cellular component (c). (d) Bubble plot that describes KEGG terms. (e and f) Enrichment plots that showed enriched pathways in high- (e) and low-risk groups (f).
Figure 6
Figure 6
Treatment prediction. (a) Box plots that showed the IC50 of 6 drugs in high- and low-score group. (b) Box plots that showed the expression of immune checkpoints in high- and low-score group. (c) TIDE prediction score in high- and low-score group. (d) Bar chart that showed the proportion of MSS/MSI-L and MSI-H patients in high- and low-score group. (e) The Kaplan-Meier plots that compared the OS of MSS/MSI-L+high-risk, MSS/MSI-L+low-risk, MSI-H+high-risk, and MSI-H+low-risk group. (f) TMB levels of high- and low-score group. (e) The Kaplan-Meier plots that compared the OS of TMB-H+high-risk, TMB-H+low-risk, TMB-L+high-risk, and TMB-L+low-risk group.
Figure 7
Figure 7
Tumor microenvironment estimation. (a–c) Beeswarm plots that compare the stromal score, immune score, and ESTIMATE score between high- and low-risk groups. (d) Box plots that showed the proportion of 22 immune cells in high- and low-score group. (e) Scatter plot that showed the correlation between T cell CD4 memory resting and risk score. (f) Scatter plot that showed the correlation between macrophage M0 and risk score. (g) Relationship between risk score and the proportion of stromal cells. (h) Correlation heat map that showed the relationship between risk score and the expression of cytokines. (i) Correlation heat map that showed the relationship between risk score and the activities of immunotherapy response-related pathways.
Figure 8
Figure 8
The role of MYOSLID in CRC. (a) The Kaplan-Meier plot that compared the OS between high- and low-MYOSLID groups. (b) Box plots that compared the expression of MYOSLID in subgroups with different clinical features. (c) Relationship between risk score and VIM, SNAI2, CDH2, TGFB1, and HIF1A. (d) The expression of MYOSLID (RNA level) in different cell lines. (e) The expression of MYOSLID under hypoxia. (f) The expression of MYOSLID (RNA level) before and after knocking down MYOSLID. (g) The OD450 value of HCT15 before and after knocking down MYOSLID. (h–k) Relative cell number in transwell assay. (i and j) Relative migration distance in wound healing assay.
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