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. 2023 Jul 21;149(14):13163–13173. doi: 10.1007/s00432-023-05157-4

Comprehensive analysis of anoikis-related genes in prognosis and immune infiltration of gastric cancer based on bulk and single-cell RNA sequencing data

Xiaobo Yang 1,#, Zheng Zhu 2,#, Tianyu Liang 2,✉, Xiaoju Lei 1,✉
PMCID: PMC11797372  PMID: 37474682

Abstract

Background

Accumulating evidence suggests that anoikis resistance is a key process in cancer cell metastasis, making it an attractive therapeutic target. Therefore, anoikis may become a new treatment for gastric cancer.

Methods

We used the univariate Cox regression method to screen gastric cancer-related anoikis genes, and a prognostic risk model was established. We analyzed differences between high- and low-risk groups in terms of tumor infiltrating immune cells, gene mutation signatures, and treatment of gastric cancer. Analysis of model associated genes at single-cell resolution was performed.

Results

We filtered to 12 anoikis-related genes and built a prognostic risk model using seven of them, which performed well in multiple datasets. Patients with CCDC178 mutations had a worse prognosis. We also found that patients at low risk were more likely to benefit from chemotherapy and immunotherapy. ERBB2 was found to be highly expressed in epithelial cells and fibroblasts. Our analysis also indicated that gastric cancer samples with high infiltration of iCAFs had a worse prognosis.

Conclusion

Seven anoikis-related genes were selected to establish a risk model. The model can be used to predict the prognosis of patients and guide the drug treatment, which provides a new idea for the evaluation and treatment of gastric cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-023-05157-4.

Keywords: Gastric cancer, Anoikis, Subtypes, Single-cell sequencing, Prognosis

Introduction

Gastric cancer (GC) is a major disease affecting people’s health around the world. The 2020 epidemiological survey showed that GC is the fifth common cancer and third leading cause of cancer mortality worldwide. More than 1 million new cases of gastric cancer are diagnosed each year (Smyth et al. 2020). In 2018 alone, 784,000 people died from GC (Bray et al. 2018). Worryingly, the number is increasing year by year. The risk factors of gastric cancer mainly include Helicobacter pylori infection, special age, unhealthy diet, smoking and alcohol abuse (Bray et al. 2018; Zhang et al. 2022; Thrumurthy et al. 2013). At present, the genetic characteristics of gastric cancer have been more clearly studied, including Epstein Barr virus positive (EBV+), microsatellite install (MSI), genomically stable, and chromosomal unstable (CIN) (Cancer Genome Atlas Research N 2014). In the clinical treatment of gastric cancer, surgery is still the main treatment (Thrumurthy et al. 2015). Chemotherapy is also known for a variety of chemotherapy drugs and chemotherapy regimens, but the adverse reactions and drug resistance are serious (Choi et al. 2015). With the gradual deepening of targeted drugs research in recent years, the treatment options for patients with gastric cancer are more refined and diversified, and the survival time is also prolonged (Chen et al. 2020). At present, immunotherapy is a popular choice for research and clinical treatment. Compared with traditional treatment, immunotherapy is more specific, with fewer complications and fewer side effects (Hogner and Moehler 2022; Kole et al. 2022).

Anoikis is a special form of apoptosis caused by the loss of intercellular matrix, and then the body will eliminate the wrong cells to maintain tissue homeostasis (Han et al. 2021). There are two mechanisms that can trigger anoikis: mitochondrial dysfunction or the activation of apoptotic mechanisms on the cell surface (Amoedo et al. 2014; Zhong and Rescorla 2012). The mechanism of endogenous pathway is that apoptosis promoting proteins Bax and Bak transfer from the cytoplasm to the outer mitochondria membrane and the mitochondrial outer membrane channel formation. The permeability of mitochondria increases and cytochrome C is released, leading to the formation of apoptotic bodies and disrupting the function of mitochondria (Shimizu et al. 1999). The mechanism of the exogenous pathway is the formation of death-inducing signaling complex (DISC) leading to cell death (Taylor et al. 2008). In turn, both mechanisms lead to caspase and its associated downstream pathways activation, and ultimately DNA endonuclease activation leading to cell death (Wang et al. 2022a, b). Anoikis is a critical obstacle to cancer metastasis. Cancer cells must develop anoikis resistance so that tumors during metastasis are not recognized and eliminated by the immune system before metastatic foci are formed (Kim et al. 2012; Simpson et al. 2008).

In this study, we obtained 362 anoikis-related genes. Then, a prognostic risk model was established based on 7 anoikis-related genes. The high-risk group has a higher level of tumor promotion-related immune cell infiltration. We also found that the low-risk group seemed to be more sensitive to multiple chemotherapy drugs and immunotherapy. In conclusion, we found that loss of nesting-related risk score can predict the prognosis of gastric cancer patients and can be used to guide the treatment of gastric cancer patients.

Materials and methods

Data acquisition and processing

Gene expression profiles and clinical data for the TCGA-STAD cohort from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) were download using the TCGA biolinks package in R (Colaprico et al. 2016). We converted the count values of TCGA-STAD cohort samples to transcripts per million (TPM) values. Somatic mutations and copy number variants in the TCGA-STAD cohort were downloaded using the TCGA biolinks package in R. Somatic mutation data analysis was also performed using R-pack maftools (Mayakonda et al. 2018).

Multiple data (GSE62254 (Cristescu et al. 2015), GSE15459 (Ooi et al. 2009), GSE26942 (Oh et al. 2018), GSE57303 (Qian et al. 2014) and GSE34942 (Chia et al. 2015)) were downloaded from the Gene-Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/gds/). Four datasets (GSE62254 (Cristescu et al. 2015), GSE15459 (Ooi et al. 2009), GSE57303 (Qian et al. 2014) and GSE34942 (Chia et al. 2015)) from the GPL570 platform were merged as one dataset named as GPL570 using “oligo” package in R (Carvalho and Irizarry 2010). The gastric cancer immunotherapy cohort (PRJEB25780) was downloaded from European Nucleotide Archive (ENA, https://www.ebi.ac.uk) (Kim et al. 2018). The 362 anoikis-related genes with relevance score > 1 were downloaded from the GeneCard database (https://www.genecards.org/) (Stelzer et al. 2016).

The establishment and validation of anoikis-related risk model

Anoikis-related genes were subjected to univariate Cox regression to find genes associated with prognosis (P < 0.05). Differential expression analysis between tumor and para carcinoma was then performed using the DESeq2 package (Varet et al. 2016). Finally, we take the intersection of these genes. Because the various sequencing platforms expression values differed across orders of magnitude, we used the scale function in R for Z-score transformation of the expression values of the genes. The optimal prognostic model was then obtained using the lasso Cox method in the glmnet package with a tenfold cross-validation set. The prognostic risk model was calculated by the combination of the multiplication of all risk factors and factor-related coefficients. The patient cohort was divided into a high-risk group and a low-risk group according to the maxstat package calculating the optimal cut-off value. The prognosis differences between the two subgroups were determined using the Kaplan Meier (KM) method. The logrank test method was used to evaluate the significance of the prognostic difference between samples in different groups. The time-dependent receiver operating characteristic (ROC) analysis was used to evaluate the performance of the risk model. We constructed a nomogram by integrating some clinical features and risk scores.

Estimation of immune cells

The CIBERSORT algorithm (https://cibersortx.stanford.edu/) with relative mode and 1000 permutations was used to estimate the fraction of 22 immune cell types in GC samples (Newman et al. 2019). The estimation of stromal and immune cells in tumor tissues was performed by ESTIMATE algorithm (Yoshihara et al. 2013).

Pathway enrichment analyses

We used GSEA software (version 4.3.2) to analyze the differences of pathway enrichment between high and low risk groups. NOM P value < 0.05 was considered statistically significant.

Single-cell RNA seq (scRNA-seq) analysis

The scRNA-seq data of GC (GSE183904) was downloaded from GEO (Kumar et al. 2022). Each sample was considered for genes/features shared by three or more cells, and cells showing 300 or more features. Cells with mitochondrial RNA percentages of > 20 were filtered out. We use the “DoubletFinder” package to remove the “doublets cell” (McGinnis et al. 2019). We calculate an average value for each gene expression of all cells in each sample during single cell sequencing, so that we obtain approximate gene expression data at the bulk level of the single cell sequencing sample. Based on the obtained risk model, we calculated the risk score for each sample and divided 26 samples into high-risk and low-risk groups based on the median. We assessed the proportion of individual cell types in each sample of TCGA-STAD using BayesPrism method (Chu et al. 2022).

Gene expression verification

To verify the gene expression, we collected 20 pairs of GC samples and adjacent normal samples in our hospital to analyze the RNA expression differences of these genes using rt-PCR, all of which were approved by the patient's informed consent and the ethics committee of Zhejiang Provincial people’s hospital. Using Trizol (Thermofisher, USA) to extract the total RNA in the sample and the reverse transcription kit RR047A kit (Takara, Japan) to convert it into cDNA. Finally, using the RR820A kit (Takara, Japan) to perform rt-PCR analysis on the 7900HT system (Thermofisher, USA), and using the ACTB gene as the internal reference gene to calculate the expression of hub genes with each pair of tissues.

Additional bioinformatic and statistical analyses

Half of the maximal inhibitory concentration (IC50) was estimated by the R package pRRophetic (Geeleher et al. 2014). Statistical differences not specifically stated were set at P < 0.05.

Result

Establishment and validation of the anoikis-related prognostic risk model in gastric cancer

A total of 362 anoikis-related genes were obtained from the Genecards. To identify anoikis-related genes that may play a role in gastric cancer, we performed differential expression analysis between tumor and para carcinoma tissues. Then, univariate Cox regression analysis was also performed in order to screen out prognosis-related genes. Finally, we got 12 genes that may play a role in the anoikis process of gastric cancer.

To derive the optimal prognostic risk model, the lasso Cox method was used to identify the most robust prognostic genes and model. The optimal λ value of 0.02 is picked (Fig. 1A and B). Finally, the anoikis-related prognostic risk score (APRS) for each sample was calculated as follows: APRS = 0.1782*CCDC178 + 0.1935*EGF + 0.1535*ERBB2 + 0.05540*ITGA8 + 0.1716*NOX4 + 0.0187*OLFM3 + 0.0041*PLG. The KM plots indicated a worse prognosis in the high APRS group (P = 3.9e-6, Fig. 1C). The distribution of risk score and the survival status of each GC patient are shown in Fig. 1D. We also performed validation of our prognostic risk model in two independent datasets, which showed clear discrimination of prognosis (GPL570: HR = 1.44, 95% CI = 1.15–1.80, P = 1.3e–3; GSE26942: HR = 2.07, 95% CI = 1.35–3.20, P = 7.3e–4; Fig. 1E–F).

Fig. 1.

Fig. 1

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Construction of anoikis associated prognostic model. A and B Partial likelihood deviance for the lasso regression and Lasso regression analysis. C Kaplan–Meier survival curves of OS in TCGA-STAD. D Distribution of risk score and OS status and heatmap of the 7 anoikis-related genes in TCGA-STAD. E and F The prognostic difference was validated in GPL570 cohort and GSE26942

Anoikis-related prognostic signature can be utilized as an independent prognostic factor in gastric cancer

Considering the importance of anoikis-related genes in gastric cancer, we wondered whether APRS was an independent prognosis risk factor in gastric cancer. We integrated the APRS and some clinical features (including: age, sex, Lauren, pathological T stage, pathological N stage, pathological M stage, stage and grade) to construct a nomogram (Fig. 2A). Each patient will be assigned a total of points by adding points for each prognostic parameter. The KM plot and ROC plot indicated that the prognostic risk model became more robust after the addition of clinical features (Fig. 2B and C). In the calibration analysis, the 1-, 3- and 5 year prediction curves of the nomogram were quite close to ideal performance (Fig. 2D, E and F).

Fig. 2.

Fig. 2

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The nomogram was generated to improve risk stratification and estimate survival probability. A The comprehensive nomogram for predicting probabilities of gastric cancer patients with 1-, 3- and 5 year OS in TCGA-STAD cohort. B and C Kaplan–Meier analysis and ROC curves of 1-, 3 and 5 year OS for this nomogram. D, E and F The calibration plots for predicting gastric cancer patients with 3-, 5- and 7 year OS in TCGA-STAD cohort

Mutation landscape between different risk groups

The top 20 differentially mutated genes in two groups are shown in Fig. 3A. Among them, ERBB4 has a significantly higher mutation rate in the high-risk group and there are previous studies demonstrating that mutations of ERBB4 are associated with cancer (Segers et al. 2020). One of his additional family members, ERBB2, was included in our risk model, suggesting that the epidermal growth factor receptor may be associated with anoikis. We found that patients with CCDC178 mutation had a worse prognosis (Fig. 3B).

Fig. 3.

Fig. 3

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Comprehensive analyses of different risk groups. A Top 20 differentially mutated genes between two risk subgroups in all gastric cancer patients of TCGA-STAD. B KM analysis between CCDC178 mutant and wild-type samples. C GSEA results indicated the enriched tumor-related pathways in the high-risk group. D Stromal score, Immune score and ESTIMATE score between two risk subgroups of TCGA-STAD. E Relative proportion of 22 infiltrating immune cells estimated by CIBERSORT between two risk subgroups of TCGA-STAD

Difference of biological function between two risk groups

To investigate differences in biological function between the two risk subgroups, we performed GSEA. We found that the base excision repair and mismatch repair pathways were significantly enriched in the low-risk group, which indicated that the low-risk group had a more stable gene set and was also consistent with low mutations of genes in low-risk group (Table S1), whereas there was a significant enrichment of tumor-related pathways in the high-risk group (including: ECM receptor interaction, focal adhesion, gap junction, PI3K Akt signaling pathway, Rap1 signaling pathway, cell adhesion molecules, Ras signaling pathway, and MAPK signaling pathway), which explained the worse prognosis in the high-risk group (Fig. 3C, Table S2).

The patients in different risk groups show different immune status

We calculated stromal and immune scores using the estimate algorithm. The high-risk group was higher in all three scores, indicating that the high-risk group had a more abundant stromal cell and immune cell infiltration (Fig. 3D). We also analyzed the infiltration levels of 22 immune cells in tumors (Fig. 3E). There were seven types of immune cells with differences of infiltration in the two risk groups. Among them, M2 macrophages had a higher infiltration level in the high-risk group (Fig. 3E). Previous studies have all shown that such cells promote tumor cell growth, which is also consistent with the poorer prognosis of the high-risk group.

APRS-based treatment strategy for gastric cancer

We used the Cancer Genome Project database to predict chemotherapy response in patients with gastric cancer. Among the 5 gastric cancer-related chemotherapeutic agents, only three classes of drugs differed between the two subgroups. The low-risk group was more sensitive to 5-fluorouracil, mitomycin C, and paclitaxel (Fig. 4A–E).

Fig. 4.

Fig. 4

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The estimation of chemotherapy response and immunotherapy response for gastric cancer. A–E The chemotherapy response of two risk groups for 5 common chemotherapy drugs. F and G Proportion of patients with response to immunotherapy in two groups in the PRJEB25780 cohort. CR complete response; PR partial response; SD stable disease; PD progressive disease. CR/PR was identified as responder, and SD/PD was identified as non-responder. H The difference in the calculated risk score between the CR/PR and SD/PD groups

To investigate whether risk score can be used to guide immunotherapy, we downloaded the PRJEB25780 cohort data for analysis. Importantly, we found that the low-risk group had a higher proportion of patients with CR/PR (Fig. 4F and G). The CR/PR group also had a significantly lower risk score (Fig. 4H). All of the above demonstrated that our risk model was able to be used in the decision-making of immunotherapy.

Analysis of ERBB2 at the single cell resolution

For further exploration of model genes and risk model, single-cell sequencing data was analyzed. All cells were annotated with 8 cell types based on relevant markers (detailed markers in Table S3, Fig. 5A), including epithelial cells, T cells, B cells, fibroblasts, smooth muscle cells, endothelial cells, mast cells and myeloid cells. We found that among these seven model genes, ERBB2 was highly expressed mainly in epithelial cells and fibroblasts (Fig. 5A and B). So, our subsequent analyses focused on ERBB2. We annotated these fibroblasts as myo-cancer associated fibroblasts (myCAFs), inflammatory cancer-associated fibroblasts (iCAFs) (detailed markers in Table S4; Fig. 5C). We found that both iCAFs were more represented in the high-risk group, while myCAFs were more represented in the low-risk group (Table S5, Fig. 5D). KM analysis also suggested that GC patients with high iCAFs infiltration have poor OS (P = 3.5e–3; Fig. 5E). The above results suggest that the poor prognosis of anoikis-related GC patients may be correlated with the association of iCAFs that highly express ERBB2.

Fig. 5.

Fig. 5

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Analysis of single cell resolution. A The t-SNE plot of single cells profiled in the presenting work colored by major cell types. B The t-SNE plot color-coded (gray to blue) to represent the expression levels of ERBB2. C The t-SNE plot of fibroblasts profiled colored by myo-cancer associated fibroblasts (myCAFs), inflammatory cancer-associated fibroblasts (iCAFs). D The proportion of myCAFs and iCAFs between high and low risk groups. E Kaplan–Meier analysis of high- and low iCAFs infiltration in TCGA-STAD

Verification of 7 genes in this model

We verified the expression levels of 7 genes from mRNA levels. In the 20 pairs of clinical samples we collected, rt-PCR results showed that EGF, ERBB2, NOX4 and PLG were highly expressed in tumor tissues. And CCDC178, ITGA8 and OLFM3 were lowly expressed in tumor tissues (Fig. 6). These results are consistent with our analysis.

Fig. 6.

Fig. 6

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rt-PCR verified the expression of anoikis-related gene expression in 20 pairs of gastric cancer clinical samples. All data are displayed as means ± SD; mean values for the normal group were normalized to 1.0; Two-side unpaired Student test was applied. **P < 0.01 and *P < 0.05 vs. normal group

Discussion

Although great progress has been made in the research and treatment of gastric cancer, it is still the third leading cause of death and the fifth most common cancer (Ye et al. 2020). As a new form of programmed cell death, anoikis can prevent cells from untimely growth and distant invasion. Therefore, anoikis resistance of tumor cells is an important determinant of its occurrence and development (Wang et al. 2022a, b). Therefore, in the present study we focused on asking whether anoikis-related genes could be used as prognostic markers.

We found 12 anoikis-related genes that may have an important role in gastric cancer by univariate Cox regression, and established a prognostic risk model consisting of 7 genes. This risk model was able to stably predict the prognosis of patients with gastric cancer, suggesting that genes involved in anoikis can also serve as good prognostic markers. Combined with other clinical features, we found that the anoikis-related gene model was also an independent prognostic risk factor. Therefore, it is possible to combine anoikis-related risk models and other clinical features to improve the predictive effect on the prognosis of gastric cancer.

We found that mutation of CCDC178 in the model conferred reduced patient survival time. It may be that mutations of CCDC178 promote the anoikis characteristic of gastric cancer, but this needs to be proven by our subsequent studies. ERBB2 was found to be highly expressed in epithelial cells and fibroblasts, implying that they may have an important role in gastric cancer. There have also been a number of previous studies suggesting an important role for iCAFs in tumors. Our analysis also indicated that gastric cancer samples with high infiltration of iCAFs had a worse prognosis. We can reasonably speculate that ERBB2 maintains the malignant features of iCAFs, such that the higher infiltration of iCAFs with high expression of ERBB2 more promotes tumor progression and thus poorer patient prognosis. The GSEA results indicated that the high-risk group had multiple tumor-related pathway enrichment, suggesting that the anoikis genes do not merely promote the anoikis signature but may also play a role in other malignant features of gastric cancer. The immune micro-environment plays a critical role in the tumorigenesis and progression. CIBERSORT in our study demonstrated that M2 macrophages had a higher level of infiltration in the high-risk group. Many studies have shown that M2 macrophages are able to promote tumor exacerbation and promote immune escape, which may be the reason why high-risk cannot better benefit from immunotherapy.

Conclusion

Seven anoikis-related genes were selected to establish a risk model. The model can be used to predict the prognosis of patients and guide the drug treatment, which provides a new idea for the evaluation and treatment of gastric cancer patients.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 18 KB) (17.7KB, docx)
Supplementary file2 (DOCX 21 KB) (21KB, docx)
Supplementary file3 (DOCX 21 KB) (21KB, docx)
Supplementary file4 (DOCX 15 KB) (15.2KB, docx)
Supplementary file5 (DOCX 15 KB) (14.7KB, docx)
Supplementary file6 (DOCX 22 KB) (21.9KB, docx)

Abbreviations

GC

Gastric cancer

TPM

Transcripts per million

KM

The Kaplan Meier

ROC

The time-dependent receiver operating characteristic

IC50

The maximal inhibitory concentration

APRS

The anoikis-related prognostic risk score

myCAFs

Myo-cancer associated fibroblasts

iCAFs

Inflammatory cancer-associated fibroblasts

Author contributions

All the authors had full access to all the data of the study, who were responsible for data integrity and accuracy. XJL worked on study concept and design. TYL were responsible for data acquisition. ZZ wrote the manuscript. XBY participated in the administrative, technical, and material support. XHT worked on supervision.

Funding

Authors did not received any funding for this work.

Data availability

The data that support the findings of this study are available in GEO (https://www.ncbi.nlm.nih.gov/geo/, GSE62254, GSE15459, GSE57303, GSE34942, GSE26942 and GSE183904), TCGA (https://portal.gdc.cancer.gov/repository, TCGA-STAD and other tumor cohorts), and the Supporting Information. Then, raw transcriptome and clinical data of immunotherapy cohort (PRJEB25780) were retrieved from European Nucleotide Archive.

Declarations

Conflict of interest

The authors declare that there was no conflict of interest in the study.

Ethical approval/patient consent

The present study was approved by the Ethics Committee of Zhejiang provincial t people’s hospital. The prostate cancer patients and healthy controls all provided informed consent. This investigation was conducted based on the principles of the declaration of Helsinki.

Consent for publication

All authors have read the manuscript and approved for publication.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Xiaobo Yang and Zheng Zhu contributed equally to this work.

Contributor Information

Tianyu Liang, Email: liangtianyu1111@163.com.

Xiaoju Lei, Email: zjsrmyy04156@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 18 KB) (17.7KB, docx)
Supplementary file2 (DOCX 21 KB) (21KB, docx)
Supplementary file3 (DOCX 21 KB) (21KB, docx)
Supplementary file4 (DOCX 15 KB) (15.2KB, docx)
Supplementary file5 (DOCX 15 KB) (14.7KB, docx)
Supplementary file6 (DOCX 22 KB) (21.9KB, docx)

Data Availability Statement

The data that support the findings of this study are available in GEO (https://www.ncbi.nlm.nih.gov/geo/, GSE62254, GSE15459, GSE57303, GSE34942, GSE26942 and GSE183904), TCGA (https://portal.gdc.cancer.gov/repository, TCGA-STAD and other tumor cohorts), and the Supporting Information. Then, raw transcriptome and clinical data of immunotherapy cohort (PRJEB25780) were retrieved from European Nucleotide Archive.

Articles from Journal of Cancer Research and Clinical Oncology are provided here courtesy of Springer

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