A novel anoikis-related gene signature predicts prognosis in patients with LUAD and reveals immune infiltration

doi:10.21203/rs.3.rs-2093035/v1

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A novel anoikis-related gene signature predicts prognosis in patients with LUAD and reveals immune infiltration

https://doi.org/10.21203/rs.3.rs-2093035/v1

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Background: To generate a signature based on anoikis-related genes (ARGs) and reveals immune infiltration for LUAD patients.

Methods: On the basis of TCGA dataset, we identified specific anoikis-related genes in LUAD. The possible biological pathways of anoikis-related genes were analyzed by KEGG. Lasso and Cox-relative regression methods were used to generate an anoikis-related signature. We constructed a nomogram and GES13213 and GES31210 were used to validate the established risk model. The clinical potency and immune status of ARGs were analyzed by CIBERSORT method and ssGSEA algorithm.

Results: 8 anoikis-related genes (BUB1, CDKN3, IL17A, KIF18A, PCNA, PLK1, UBE2C and TIMP1) were determined to develop a prognostic ARGs. The ARGs could accurately classify LUAD cases with different clinical outcome, and survival analysis revealed that high-risk groups had a lower overall survival rate than low-risk groups. In addition, nomogram also has a high predictive value, studies on immune infiltration and tumor microenvironment indicate that immune cells and their functions may play an important role in tumorigenesis and development. Conclusion: ARGs can be used to stratify the risk and forecast the survival outcome of LUAD patients and provide prominent reference for individualized treatment in LUAD.

LUAD

ARGs

Immunotherapy

TCGA

TISCH

In 2020, the increasing population of lung cancer(LC) reached 2.2 million, and it become the second largest cancer, nevertheless, LC accounts for about 1.8 million of the 9.96 million cancer deaths, and its’ mortality rate ranks the highest all over the world^[1]. Despite the emergence of new therapeutic agents that are designed to improve the survival of patients with this type of cancer, such as immunotherapies, the overall survival rate of LUAD patients remains at 16%^[2]. Therefore, it is urgent to further discover specific prognostic prediction methods for LUAD patients in order to find new therapeutic targets and improve patients’ survival.

In recent studies, it has been shown that cancer cells carry extracellular matrix (ECM) during metastasis, and anoikis occurs when tumor cells detach from the ECM during metastasis^[3]. Anoikis is a particular type of cell apoptosis brought on by the detachment of cells from the ECM. Originally recognized in epithelial and endothelial cells, anoikis is idea to be a physiological manner related with improvement and tissue homeostasis. Apoptosis prevents indifferent cells from readhering to different substrates for ordinary proliferation and as a result performs a central section in defending the organism^[4].However, the incapacity to provoke the loss-of-nest apoptosis process may additionally end result in the survival of adherent cells in suspension or proliferation in an ECM distinct from the in situ one. Currently, diminished capability to provoke apoptosis has come to be a hallmark of most cancers and contributes to the accelerate of distal metastases from tumor^[5].

This paper concentrates on exploring the viable roles of anoikis-related genes (ANRGs) in cancer improvement and development as nicely tumor immunology in LUAD. Within these differentially expressed anoikis-related genes, we recognized eight hub ANRGs with non-negligible correlation with LUAD prognosis. Furthermore, we built a prognostic ANRGs signature and structured a novel nomogram to more exact predict the average survival of LUAD individuals. We additionally carried out immune cell infiltration and tumor microenvironment analyses in unique high- and low-risk group. In addition, CIBERSORT and ssGSEA evaluation had been used for immune characteristic evaluation. Therefore, in this study, we targeted on exploring the prognostic significance of ANRGs in LUAD and developed a prognostic scoring model primarily based on ANRGs. Further to discover the variations in tumor microenvironment of sufferers beneath this danger rating typing.

2.1 Gene expression and clinical data acquisition: All data of LUAD patients (tumor = 539 and normal = 59) were downloaded from TCGA database (http://portal.gdc.cancer.gov/). In total, 788 anoikis-related genes were searched from GeneCards (https://www.genecards.org) with a relevance score ≥0.4^[6]. Further preprocessed with the “limma” package in R ((FDR) < 0.05 and |log2 fold change (FC)|≥1), 244 differentially expressed ANRGs were identified. LUAD sufferers with lacking universal survival values and brief normal survival values (<30 days) had been eliminated to decrease statistical bias. A whole of 493 samples in the training set had been used to increase a predictive hazard model. In addition, the transcriptome and clinical information of 2 external validation cohorts were downloaded from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database, GES13213^[7] and GES31210^[8] were used to validate the established risk model.

2.2 GO and KEGG Pathway Enrichment Analyses: GO enrichment and Kyoto encyclopedia of genes and genomes (KEGG)^[9-11] pathway analysis were perform to explore the effect of those identified differentially expressed genes with the help of several packages including “clusterProfiler”, “ggplot2”, “enrichplot” and “org.Hs.eg.db” in R. P and FDR values < 0.05 represent statistically significant.

2.3 Protein–Protein Interaction (PPI) Network Construction: In addition to discover the Interaction of all differentially expressed genes, we built PPI network primarily based on the STRING database (https://cn.string-db.org/), their interactions had been imported into the Cytoscape 3.9.1 software program to assemble and visualize a PPI network. The Molecular Complex Detection (MCODE) plug-in was utilized to recognized these crucial modules in the PPI network. P value was much less than 0.05 indicating a sizeable difference.

2.4 Construction and Validation of the Risk Model: Via the survival R package, we built-in these chosen hub genes in the PPI network into univariate Cox regression evaluation to inspect their affect to patient’s survival result and survival time, and the candidate genes have been recognized as prognosis associated hub genes (P<0.05). Subsequently, these candidate OS genes had been delivered into the least absolute shrinkage and selection operator (LASSO) regression^[12] and multivariate Cox regression analyses collectively to recognized the hub OS genes that have been used to assemble a risk score in LUAD. Patients have been divided into two subgroups (low-risk group and high-risk one) according to the median hazard score. Next, we carried out Kaplan-Meier take a look at and Log-Rank take a look at to analyze the overall survival of two group. Additionally, the use of the Survival ROC and timeROC package, we carried out a receiver operating characteristic (ROC) curve^[13] evaluation to consider the normal predictive overall performance of the model.

2.5 Nomogram and Survival Analysis: Univariate Cox and multivariate Cox regression analyses were conducted to verify whether risk scores and clinical characteristics were independent variables. The nomogram was created to better predict the survival by way of the use of the “RMS” packages in R. The concordance index and calibration plot have been utilized to check the reliability of the nomogram. We used the “survival” package to perform Kaplan-Meier (K-M) survival analysis for the low-risk and high-risk groups.

2.6 The Investigation of the Immune Microenvironment: The CIBERSORT and ssGSEA algorithms were used to analyze the infiltration status of immune cells. Stromal score, immune score, and ESTIMATE score of patients were calculated using the “ESTIMATE” package to further explore the tumor microenvironment (TME) in LUAD patients.

2.7 Single cell association analysis: The Tumor Immune Single-Cell Hub (TISCH; http://tisch.comp-genomics.org) is alarge-scale online database of single-cell RNA-seq focused on the TME^[14]. This database was used to systematically investigate the TME heterogeneity through different data sets and cell types.

2.8 Statistical Analysis: All statistical analyses and records visualization had been carried out in R (https://www.r-project.org/). When no exclusive guidelines have been given for the aforementioned strategies of analysis, p < 0.05 was once regarded statistically significant.

3.1 Differential Expression Analyses and Functional Enrichment Analyses: A total of 788 ARGs expression data of LUAD samples and normal control in TCGA were analyzed. A whole of 244 differentially expressed genes, comprising 68 downregulated and 176 upregulated genes, met the screening standards and were identified as differentially expressed anoikis-related genes (DEARGs). The expression proportion of these DEARGs was displayed in the volcano plot and heatmap (Figure 1A and B). We sought to further investigate the underlying molecular mechanisms by which these identified DEARGs their functional effects on development of LUAD the use of GO and KEGG analyses (Figures 1C-F). Enrichment analyses results revealed that these DEARGs were mainly enriched in anoikis-related pathways, such as the PI3K-AKT signaling pathway, MicroRNAs in cancer, Fluid shear stress and atherosclerosis and Focal adhesion.

3.2 PPI network: Using Cytoscape 3.9.1 software and data from the STRING database, the PPI network and top 40 genes from PPI network were constructed (Figure 2 A-B).

3.3 Prognosis-Related Candidate Genes Screening and Construction of ARGs Signature for LUAD: A total of 17 genes were identified in univariate Cox regression analyses as candidate OS genes with P < 0.05 (Figure 3A). Subsequently, 8 prognosis hub genes were selected by the LASSO analyses and multivariate Cox regression analyses (Figure 3B-D). The risk score of this ARGs signature was calculated using the following linear formula:

where Expgene represents the relative expression value of OS genes, and β represents the regression coefficient. Patients have been labeled into low- and high-risk corporations the use of the medium risk score. Patients’ survival status and survival time in the high-risk and low-risk companies in the training set are described in Figure 3E. The sensitivity of the risk model used to be evaluated the usage of time-dependent receiver working characteristics (ROCs). (Figure 3F).

3.4 Assessment of the Risk Model: The distribution patterns of risk scores of LUAD patients between the high-risk and low-risk groups in the training set are shown in Figure 4A. Figure 4B details the patient survival status and time in the high-risk and low-risk categories in the training set. Figure 4C shows the relative expression levels of 8 anoikis-related genes for each patient. We assessed the differences of LUAD patients between the low-risk and high-risk groups based on the common clinicopathological traits. The results showed that the low-risk group's OS was longer than that of the high-risk group's patients after grouping patients according to gender, age, stage, or TNM (Figure 4D-K).

3.5 Nomogram and Independent Prognostic Factor Analysis: Through the use of univariate and multivariate Cox regression analysis, it was determined if the risk model could be utilized as a standalone prognostic factor for LUAD. According to univariate Cox regression analysis, prognosis was correlated with risk score, disease stage, and TNM stage (Figure 5A). The risk score was also shown to be an independent factor affecting prognosis by multivariate Cox regression analysis (Figure 5B). The aforementioned findings led to the conclusion that the risk model based on the eight anoikis-related genes was an independent prognostic factor that significantly affected the survival and prognosis of LUAD patients. We developed a nomogram incorporating stage, TNM, and risk score to more accurately predict the 1-, 3-, and 5-year survival for LUAD patients (Figure 5C). Using calibration curve analysis, the prediction accuracy of the nomogram was assessed (Figure 5D-F).

3.6 Validation of the Anoikis-Related Prognostic Signature in Independent GEO Cohorts：The anoikis-related prognostic signature of LUAD was externally validated using two different NSCLC cohorts from the GEO database to confirm its stability and reproducibility (GES31210 and GES13213). The expression levels of anoikis-related genes and prognostic information of LUAD patients were obtained from these two cohorts. The risk scores of LUAD patients in the GEO validation cohorts also were determined using the formula acquired from the TCGA training cohort. In two GEO validation cohorts, K-M survival analysis revealed that the OS of LUAD patients in the high-risk group was considerably poorer than that of the low-risk group (Figure 6A and F). When evaluating the risk model's sensitivity, time-dependent receiver working characteristics were used (ROCs). (Figure 6B and G). The calibration curve was used to rate the nomogram's predictive power (Figure 6C-E and H-J).

3.7 The Investigation of the Immune Microenvironment and immune activity: The development of cancer and the effectiveness of immunotherapy are both significantly influenced by the immune microenvironment. In order to do this, we further investigated the tumor microenvironment (TME) landscape in high- and low-risk groups of LUAD patients. To calculate the relative proportion of immune cells invading, the CIBERSORT R script was utilized. First, LUAD risk scores are ranked from lowest to greatest, revealing the proportion of various immune cells for each risk score (Figure 7A). The results showed that many immune cells and immune responses were related to the risk score. The immune functions like Check−point, B cells naive, T cells CD4 memory, NK cells and Eosinophils were higher in the low-risk group. The infiltration of Plasma cells, T cells CD4 and Macrophages were significantly higher in the high-risk group (Figure 7B). We further assessed the relative abundance of 23 tumor-infiltrating immune cells in each patient to investigate better the underlying molecular mechanisms of 8 anoikis-related genes and their correlations with tumor immunity using the ssGSEA algorithm (Figures 7D). Immunological cell correlation in LUAD patients may offer hints for a better comprehension of the immune milieu in particular types of malignancies (Figure 7C). The eight gene signature that was utilized to build the ANRGscore model exhibits distinct expression patterns in high- and low-risk groups and is strongly correlated with several immune cell infiltrations (Figure 7E). Additionally, we discovered that the stromal, immunologic, and ESTIMATE scores of the LUAD patients in the low-risk group were significantly higher than those of the high-risk group, indicating that the TME was distinct from that of the high-risk group (Figures 7F-H).

3.8 Single-cell tumor microenvironment: We used the single cell dataset EMTAB6149 from the TISCH database to analyze the expression of 8 ANRGs in TME. In the EMTAB6149 dataset, there are 24 cell clusters and 12 medium cell types, and the distribution and number of various cell types are shown (Figure 8A-B). PCNA is mainly expressed in malignant cells and immune cells (CD8T and CD8Rex), while it is expressed at lower levels in Fibroblasts. BUB1, CDKN3, PLK1 and UBE2C were detected at expression levels in immune cells. IL17A and KIF18A was almost not detected in tme. TIMP1 was expressed in tme High expression was found in a variety of cells, TIMP1 was mainly expressed in immune cells (Figures 8C-J).

The development of high-throughput sequencing and bioinformatics has led to an increase in the use of bioinformatics techniques to find novel biomarkers involved in tumor progression, survival, or prognosis of malignancies, which may greatly help with early diagnosis and prognosis assessment in malignant tumors^[15-17]. It is challenging to timely improve the prognosis of patients with aggressive LUAD with a single targeted method or medication therapy due to the disease's rapid progression. In order to facilitate early intervention, it may be useful to build predictive models employing genes related with prognosis. But there aren't enough of these marks. As a result, it is necessary to screen for additional biomarkers with strong predictive performance so that they can be added to the candidate list.

Anoikis, a form of programmed cell death, happens when cells detach from the appropriate extracellular matrix, interfering with integrins' ability to adhere^[18]. It is a crucial mechanism for preventing the growth or attachment of dysplastic cells to the wrong matrix. Anoikis is necessary for tissue homeostasis and development because it prevents detached epithelial cells from settling in other places^[19]. Normal epithelial cells normally lose vital survival components and die through a process known as anoikis when they separate, however LUAD metastatic tumor cells develop resistance to anoikis, allowing them to start healing distant from the main lesion^[20]. Anoikis dysregulation is presently of great interest to the scientific community because anchorage-dependent growth and the epithelial-mesenchymal transition, two characteristics linked to anoikis resistance, are essential steps in tumor progression and the metastatic dissemination of cancer cells^[21-23]. The idea of targeting anoikis-related genes to stop LUAD progression and metastasis is highlighted by the fact that LUAD can acquire anoikis resistance through a variety of mechanisms. The complicated interaction of numerous factors impacting anoikis resistance in tumor pathophysiology is shown in polygenic analysis. Therefore, in the era of precision medicine for cancer, this polygenic method might enable characterization of tumor biology to enhance clinical decision-making.

In this study, we identified robust risk score features containing 8 genes, namely BUB1, CDKN3, IL17A, KIF18A, PCNA, PLK1, UBE2C and TIMP1. Previous studies have described certain associations between genes and the tumorigenesis and pathogenesis of cancer. A conserved mitotic Serine/Threonine kinase called BUB1 (budding uninhibited by benzimidazole 1) was initially discovered in budding yeast^[24]. Bub1 plays a function in APC/C activation as well as SAC signaling, chromosomal alignment, and alignment^[25]. CDKN3 (Cyclin-dependent kinase inhibitor-3) is a dual-specificity protein tyrosine phosphatase of the CDC14 group. Initially known as CDK-Associated Phosphatase(KAP), CDKN3 was discovered from a HeLa cDNA library as a CDK2 binding protein through a yeast two-hybrid screen^[26]. The expression of CDKN3, which regulates cell division and is more prominent during the mitotic phase, inhibits the CDK-driven cell cycle, a crucial step in the development of cancer cells^[27]. Rouvier et al. first identified IL-17A at the transcriptional level in a rodent T-cell hybridoma that resulted from the union of a mouse cytotoxic T cell clone with a rat T cell lymphoma in 1993. Through the production of IL-6, which in turn activates the oncogenic transcription factor signal transducer and activator of transcription 3 (STAT3) and upregulates pro-survival and pro-angiogenic genes in tumors, IL-17A can also accelerate tumor growth in vivo^[28]. Kinesin-8 motors consist of KIF18A, KIF18B, and KIF19. The N-kinesin-8 molecular motor protein, also known as the kinesin family member 18A (KIF18A) protein, is mostly found in the cytoplasm and nucleus and has a molecular weight of 100 kDa. By controlling microtubule dynamics, chromosomal aggregation, and cell division, KIF18A physiologically ensures the proper development of cell mitosis. Previous studies have demonstrated that deregulation of KIF18A expression may have an impact on chromosomal segregation and/or instability, causing tumors to develop^[29-31]. A DNA polymerase auxiliary protein known as PCNA, which is crucial for DNA replication, has the ability to extend DNA polymerase. It is connected to the process of cell division, takes part in DNA synthesis, and significantly regulates the cell cycle. PCNA and tumor cell proliferation are tightly associated, and a cell's expression of PCNA may indicate whether or not it is proliferating^[32-33]. A crucial regulator of mitosis and cytokinesis in eukaryotes is PLK1 (Polo-like kinase 1), the prototype member of the polo-like family of serine/threonine kinases. The course of mitosis, centrosome assembly, chromosomal segregation, spindle elongation, and cytokinesis are all crucial processes that it affects^[34]. UBE2C, an E2 ubiquitin-conjugating enzyme, collaborates with APC/C to play a crucial part in the ubiquitination system. It helps the cell cycle move from the M phase to the G1 phase by participating in the breakdown of mitotic cyclin B^[35]. TIMP1 is one of the four members of the matrix metalloproteinase (MMP) inhibitor family (TIMP1, TIMP2, TIMP3 and TIMP4). TIMP1's primary job is to prevent extracellular matrix degradation caused by MMPs^[36]. Studies conducted in vitro have shown that overexpression of TIMP1 can significantly increase the expression of genes related to signal transduction, apoptosis, and proliferation^[37]. TIMP1 also showed anti-apoptotic action and was discovered to impair tumor cell sensitivity to a number of anticancer treatments by activating downstream pathways. It has been shown, in particular, that TIMP1 may bind to the CD63/integrin-1 complex and have anti-apoptotic effects^[38].

The tumor microenvironment (TME), which is composed of the ECM, stroma cells, tumour vasculature, and different immune system cells, supports the onset and spread of cancer^[39]. Recently, immunotherapy has been found as a new treatment option for this condition. Growing evidence indicates that the presence of innate immune cells in the tumor microenvironment, such as macrophages, neutrophils, dendritic cells, innate lymphoid cells, myeloid-derived suppressor cells, and natural killer cells, as well as adaptive immune cells such as T cells and B cells, can promote tumor development^[40]. The milieu that develops from cross-talk between cancer cells and the proximal immune cells eventually encourages the growth and metastasis of tumors^[41]. In the high-risk group with poor survival, the level of Plasma cells, T cells CD4 and Macrophages was significantly upregulated, suggesting its crucial role in the development of LUAD.

Given the variety of cells, anoikis studies carried out at the single-cell level may more properly reflect the effect of ANRGs on the course and prognosis of LUAD patients, even though our riskScore and the nomogram built on it have greater predictive performance. Additionally, a bigger sample size is needed for the calibration of the prediction model due to the small amount of data in this investigation.

In conclusion, our model based on eight genes can accurately predict the survival of LUAD patients, and the nomogram based on the model can assist medical professionals in creating customized LUAD treatments in clinical settings. Future investigations into the molecular pathways connected to this trait and prospective randomized clinical trials will be crucial from a clinical standpoint and could offer a blueprint for precision medicine.

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Not applicable.

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Not applicable.

Availability of data and materials

Publicly available datasets were analyzed in this study. This data can be found here: The datasets analyzed in the current study are available in the TCGA repository (http://cancergenome.nih.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/) and TISCH (http://tisch.comp-genomics.org).

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

Not applicable.

Authors' contributions

SC Zhang and WW conceived the study. SC Zhang and AK Shao drafted the manuscript. SC Zhang and AK Shao performed the literature search and collected the data. SC Zhang and AK Shao analyzed and visualized the data. SC Zhang and WW revised the manuscript and were the supporters of the study. All authors reviewed and approved the final manuscript.

Acknowledgements

We thank each of the authors who contributed.

Footnotes

Footnotes to the text are numbered consecutively.

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A novel anoikis-related gene signature predicts prognosis in patients with LUAD and reveals immune infiltration

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