Exploring potential therapeutic targets for small cell lung cancer based on transcriptomics combined with Mendelian randomization analysis

Objective: The main objective of this study was to explore and identify new genetic targets in small-cell lung cancer (SCLC) through transcriptomics analysis and Mendelian randomization (MR) analysis, which will help in the subsequent development of new therapeutic interventions.

Methods: In this study, we extracted the SCLC dataset from the Gene Expression Omnibus (GEO) database, processed the data, and screened out differentially expressed genes (DEGs) using R software. Based on expression quantitative trait loci data and the genome-wide association study data of SCLC, MR analysis was used to screen the genes closely related to SCLC disease, which intersect with DEGs to obtain co-expressed genes (CEGs), and the biological functions and pathways of CEGs were further explored by enrichment analysis. In addition, the CIBERSORT algorithm was applied to assess the level of immune cell infiltration in SCLC and to analyze the correlation between CEGs and immune cells. Meanwhile, we performed a survival analysis on these five CEGs using an independent cohort of SCLC patients. Finally, the results for the target genes were validated.

Results: In this study, 857 DEGs were identified, including 443 up-regulated and 414 down-regulated genes, and 5 CEGs (PSAT1, PSRC1, COLEC12, PLLP, HP) that were significantly associated with SCLC were identified through further intersecting. The results of enrichment analyses indicated that CEGs play important roles in several key functions and pathways. Immune-cell-related analysis revealed the unique distribution of immune cell infiltration in SCLC and the mechanism of immune cell regulation by CEGs. Survival analysis results indicated that PSRC1 was significantly correlated with the overall survival of SCLC, and the survival rate of the high-expression group was markedly lower than that of the low-expression group. Finally, the consistency of the results between the validation group analyses and MR analysis confirmed that the results of this study is reliable.

Conclusion: The CEGs and their associated functions and pathways screened in this study may be potential targets of therapeutic intervention in SCLC by targeting specific molecular pathways.

1 Introduction

Small-cell lung cancer (SCLC) is a high-grade neuroendocrine cancer with extremely high mortality and poor prognosis, accounting for about 13-15% of all lung cancer cases, with a high tendency to metastasize early, aggressive proliferation and easy to recur, and can be divided into two types: limited-stage and extensive-stage, of which 80-85% of SCLC patients are diagnosed with extensive-stage small-cell lung cancer (ES-SCLC) (1, 2). Currently chemoradiation is the key intervention in the therapeutic management of SCLC. SCLC has a good initial sensitivity to chemoradiation compared to other treatments, however, this response is mostly short-lived and patients are prone to recurrent diseases (1, 3). Whereas the emergence of immune checkpoint inhibitors in recent years has altered the clinical outcome of ES-SCLC patients to some extent, and the regimen of platinum-based chemotherapy in combination with anti-programmed cell death protein 1 and its ligand 1 has increased the survival of ES-SCLC patients and represents the current standard of first-line therapy, the overall survival of ES-SCLC patients is still very limited (4, 5). More research is urgently needed to explore effective therapeutic alternatives for SCLC. However, the complex and unclear mechanisms of SCLC development pose a serious challenge to studying therapeutic options. Therefore, an in-depth understanding of the molecular basis of SCLC, exploration of the complex mechanisms of SCLC, and identification of potential therapeutic targets are of great importance for the subsequent treatment of SCLC.

Mendelian randomization (MR) analysis is a method that uses genetic variation as instrumental variables (IVs) to analyze the causal relationship between exposure factors and disease, which can circumvent the reverse causality and environmental confounding inherent in traditional epidemiological methods (6, 7). In this study, we extracted SCLC microarray datasets from the Gene Expression Omnibus (GEO) database and screened out differentially expressed genes (DEGs) between SCLC and normal tissue samples. Based on the expression quantitative trait loci data and genome-wide association study (GWAS) data of SCLC, we used MR analysis to screen out the genes closely related to SCLC disease and intersected them with DEGs to obtain co-expressed genes (CEGs). Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analyses were further applied to explore the biological functions and pathways of CEGs. In addition, the CIBERSORT algorithm was performed to assess the level of immune cell infiltration in SCLC and to analyze the correlation between CEGs and immune cells. We further explored the activity levels of relevant functions and pathways in specific gene expression groups by Gene Set Enrichment Analysis (GSEA). Finally, validation group analysis was performed to increase the reliability of the results of this study.

2 Materials and methods

2.1 GEO data collection

The inclusion criteria for the SCLC microarray datasets were (1) the datasets contained at least 8 samples (at least 4 SCLC samples and 4 normal tissue samples) (2); the datasets did not contain any chemically treated or genetically modified samples (3); the raw data or array gene expression profiling data must be available. In this study, three SCLC microarray datasets were extracted according to the inclusion criteria (GSE43346, GSE73160 and GSE149507, respectively), which contained a total of 107 SCLC samples and 66 control samples (detailed information can be accessed from Table 1), and all the gene expression matrices and corresponding platform probe annotations were available from GEO database (https://www.ncbi.nlm.nih.gov/geo/).

Table 1

Table 1. The information of the three GEO datasets.

2.2 Methodology

2.2.1 Identification of DEGs

The three datasets were read, preprocessed, and calibrated individually using the R software (version 4.3.3), then combined into one total dataset and batch-corrected, and normalized and standardized using gene expression matrices and annotation files from the GEO database. To eliminate the batch effect, principal component analysis was performed on the total processed dataset using the “prcomp” function, and the results were visualized. DEGs were identified using the classical Bayesian data analysis method in the “limma” package (7). The filtering conditions were set to corrected P-value < 0.05 and LogFoldChange (LogFC) > 1, and heatmaps and volcano maps of DEGs were generated using the “peatmap” package.

2.2.2 Exposure factors and outcome variables

In this study, we extracted the expression quantitative trait loci data of the Võsa U team’s study from the IEU open GWAS database (https://gwas.mrcieu.ac.uk/) as an exposure factor (8). Then we screened for single nucleotide polymorphisms (SNPs) that met the following criteria (1): strong correlation with the exposure factor (P < 5e-08) (2), no linkage disequilibrium (parameters set to r2 < 0.001, kb=10000) (3), To exclude the SNPs with weak associations or insufficient explanation of phenotypic variance, the F-value of each SNP needs to be >10 [F= β²/SE² (β stands for the allelic effect value and SE stands for standard error)]. Screened SNPs will be analyzed by MR as IVs representing exposure factors.

We extracted the GWAS data of the McKay team’s study from the GWAS catalog database (https://www.ebi.ac.uk/gwas/home, accession number GCST004746), containing a total of 2,664 SCLC samples with 21,444 control samples, which will be analyzed as an outcome variable for MR (9).

2.2.3 MR analysis

In this study, MR analyses were conducted between IVs and outcome variables using the “TwoSampleMR” package in R software. Inverse variance weighting (IVW) was used as the primary analytical method, and MR Egger, weighted median, simple mode and weighted mode were used as secondary analytical methods (7). Genes that met the following criteria were screened based on the MR analysis results (1): IVW results showed P-value <0.05 (2); the results of the above five analysis methods met the criterion of directional consistency [Odds ratios (ORs) were directionally consistent] (3); the IVW results were corrected using the false discovery rate method (the adjusted P-value was <0.05) (4); the pleiotropic analysis results showed no pleiotropic tendency (P>0.05). According to the direction of OR value, the screened genes were respectively intersected and identified with DEGs thereby obtaining CEGs, including up-regulated and down-regulated genes. In addition, heterogeneity tests, pleiotropic and sensitivity analyses were performed to determine the reliability of the MR results. Funnel, scatter and leave-one-out plots were developed to visualize and support the MR results.

2.2.4 GO/KEGG enrichment analysis

To further explore the potential biological functions and pathways of CEGs, we performed GO functional annotation and KEGG pathway enrichment analyses of the CEGs using the “clusterProfiler” package with a filter of P<0.05.

2.2.5 Analysis of immune cell infiltration

In this study, the “CIBERSORT” package was performed to analyze and assess the differences in the infiltration of 22 immune cells between SCLC and normal tissue samples, and to further analyze the correlation between CEGs and immune cells for exploring the mechanism of CEGs on immune cells (10).

2.2.6 GSEA

GSEA can sort all genes from largest to smallest according to the multiplicity of differences after treatment, and if there is a trend of up-regulation or down-regulation of functions and pathways associated with gene expression then it will show top or bottom enrichment in the gene list (11). Therefore, in this study, we further used the GSEA method to explore the activity levels of the relevant functions and pathways in a specific gene set with a statistical significance criterion of P<0.05.

2.2.7 Survival analysis

This study retrieved the overall survival data of SCLC from the George team (12). After excluding the missing values, the data was calibrated and processed. Subsequently, the “survival” package was utilized to perform survival analysis on CEGs, thereby obtaining genes associated with SCLC overall survival (with a significance criterion of P < 0.05).

2.2.8 Validation group analysis

In this study, the GSE40275 dataset, which met the inclusion criteria, was selected from the GEO database as the validation group (15 SCLC and 43 normal tissue samples), and was processed and analyzed using the same methodology as described above, as well as the results of the analyses were compared with the results of the MR analyses, to verify whether there were differences in the CEGs between the SCLC and normal tissue samples.

3 Results

3.1 Identification of DEGs

We performed a series of pre-processing on each of the three datasets and corrected the expression of each gene in the data, and finally merged them into one total dataset and removed the batch effect by principal component analysis. The data before and after batch correction are shown in Figures 1A, B. In this study, 857 DEGs were identified from the three GEO datasets, including 443 up-regulated genes and 414 down-regulated genes (specific information can be obtained from Supplementary Table S1). The heatmap shows the expression status of the top 50 up-regulated DEGs and the top 50 down-regulated DEGs, as shown in Figure 2. The volcano plot shows the expression pattern of DEGs in the total dataset, as shown in Figure 3.

Figure 1

Figure 1. Principal component analysis of principal component analysis of three small cell lung cancer datasets. (A) represents that there are clear batch effects in the three datasets before batch correction. (B) represents, after batch correction, all samples in the dataset achieved acceptable homogeneity following PCA analysis.

Figure 2

Figure 2. Differential gene expression heatmap. Treat: small-cell lung cancer tissue samples, control: normal tissue samples.

Figure 3

Figure 3. The volcano plot of DEGs with the three datasets. If the log fold change (logFC) is greater than 1, it represents an up-regulated differentially expressed gene, which is the red part; if the logFC is less than -1, it represents a down-regulated differentially expressed gene, which is the blue part.

3.2 MR analysis

Based on the three main hypothesis criteria of MR analysis and the F-value filtering criteria (7, 13), we finally filtered 26,512 SNPs as instrumental variables (specific information can be obtained from Supplementary Table S2). MR analysis was performed between IVs and outcome variables. According to the filtering criteria, we filtered out 102 genes with OR-value <1 and 100 genes with OR-value >1 (specific information can be obtained from Supplementary Table S3), further intersected with DEGs, and finally obtained 2 co-expression up-regulated genes (PSAT1, PSRC1) and 3 co-expression down-regulated genes (PLLP, COLEC12, HP), as shown in Figure 4.

Figure 4

Figure 4. Venn plots. Two circles respectively represent the differential expression analysis results and Mendelian randomization analysis results of small cell lung cancer. The overlapping part represents the co-expressed genes. (A) 2 up-regulated co-expressed genes. (B) 3 down-regulated co-expressed genes.

The IVW results showed that PSAT1 and PSRC1 all had significant positive correlation with SCLC, PSAT1 [P=0.007, OR=1.156, 95% confidence interval (CI): 1.041-1.283], PSRC1 (P=0.031, OR=1.131, 95% CI: 1.011-1.264); whereas COLEC12, PLLP, and HP all had significant negative correlation with SCLC, COLEC12 (P=0.005, OR=0.798, 95% CI: 0.682-0.933), PLLP (P=0.008, OR=0.606, 95% CI: 0.420-0.875), HP (P=0.011, OR=0.885, 95% CI: 0.805-0.972), as shown in Figure 5. In the results of the analysis of multiplicity all P-values were >0.05, indicating that there was no effect of multiplicity. In the test for heterogeneity all p-values were >0.05, indicating that there was no effect of heterogeneity. Results of leave-one-out sensitivity analyses showed that the effect value of each IV were close to the overall effect size. Forest, scatter, funnel, and leave-one-out plots can be obtained from Supplementary Figure S1. In addition, we localized the CEGs on chromosomes and visualized them as shown in Figure 6.

Figure 5

Figure 5. MR forest plot of co-expressed genes. The results of MR analysis between co-expressed genes and SCLC disease. OR, odds ratio; CI, confidence interval; snp, single-nucleotide polymorphism.

Figure 6

Figure 6. Circos plot of co-expressed genes. The figure shows a circular chromosome diagram. The regions of different colors in the diagram represent different chromosomes, and the locations of five co-expressed genes on the chromosomes are labeled.

3.3 GO/KEGG enrichment analysis

GO enrichment analysis showed that these genes mainly affect the biological functions of endocytic vesicle, haptoglobin-hemoglobin complex, compact myelin, myelin sheath, structural constituent of myelin sheath, as shown in Figure 7. KEGG enrichment analysis showed that these genes mainly affect glycine, serine, and threonine metabolism; cysteine and methionine metabolism; biosynthesis of amino acids; carbon metabolism, biosynthesis of cofactors, and phagosome, as shown in Figure 8. Specific information can be obtained from the Supplementary Table S7.

Figure 7

Figure 7. GO enrichment analysis of co-expressed genes. The figure illustrates the functional enrichment status of co-expressed genes within the GO enrichment analysis, covering biological process (BP), cellular component (CC), and molecular function (MF). Each segment showcases the top ten functions. A deeper red color in “p.adjust” indicates a higher level of significance. The white circles signify the quantity of genes where genes are concentrated within a particular function. Larger circles denote a greater number of enriched genes.

Figure 8

Figure 8. KEGG enrichment analysis of co-expressed genes. This figure presents the pathway enrichment status of co-expressed genes in KEGG enrichment analysis, showing the top ten pathways. A deeper red color in “p.adjust” indicates a higher level of significance. The white circles represent the number of genes where differentially expressed genes (DEG) are concentrated in a specific pathway. Larger circles imply a greater number of enriched genes.

3.4 Immune cell infiltration analysis

The results of GO/KEGG enrichment analysis showed that the functions and pathways of CEGs may be related to immune infiltration; therefore, in this study, we used the CIBERSORT algorithm to assess the differences in the levels of immune cell infiltration between SCLC and control samples and further explored the correlation between CEGs and immune cell infiltration. We observed significant differences between SCLC and control samples on T cells CD4 memory resting, T cells CD4 memory activated, T cells follicular helper, T cells regulatory, NK cells activated, and Macrophage M2, as shown in Figure 9A. Specifically, the proportion of T cell CD4 memory resting and T cell CD4 memory activated were significantly lower. T cell follicular helper, T cells regulatory, NK cell activated, and Macrophages M2 were significantly higher in SCLC compared with control, as shown in Figure 9B.

Figure 9

Figure 9. Analysis of Immune Cell Infiltration in small-cell lung cancer. Treat: small-cell lung cancer group, control: normal tissue group. (A) Stacked histogram of the ratio of immune cells in the treat and control groups. (B) Box plots comparing the levels of 22 types of immune cell infiltration in the treat group versus the control group. *P<0.05, **P<0.01. (C) Heatmap of the correlation between co-expressed genes and 22 types of immune cell.

Correlation analysis of CEGs with 22 immune cells showed that PSRC1 was positively correlated with T cells follicular helper, NK cells activated, Macrophages M1, and negatively correlated with NK cells resting, Monocytes, Macrophages M2, Eosinophils, and Neutrophils; PSAT1 was positively correlated with Macrophages M0, and negatively correlated with Monocytes; COLEC12 was positively correlated with Monocytes, Macrophages M2 and Neutrophils, and negatively correlated with T cells follicular helper and Macrophages M1; PLLP was positively correlated with Neutrophils and negatively correlated with T cell CD4 memory activated and Macrophages M1; HP was negatively correlated with B cells native, as shown in Figure 9C.

3.5 GSEA

Based on the results of differential gene expression status, immune cell infiltration assessment, and correlation analysis, we found that PSRC1 and COLEC12 were associated with significantly differential infiltrating immune cells. Therefore, this study further explored the activity levels of the relevant functions and pathways of PSRC1 and COLEC12 in SCLC using GSEA. GSEA results showed that, in the PSRC1 high expression group, the top five active biological functions were chromosome segregation, mitotic sister chromatid segregation, nuclear chromosome segregation, regulation of chromosome segregation, and sister chromatid segregation; the top five active pathways were cell cycle, DNA replication, mismatch repair, oocyte meiosis, and P53 signaling pathway. In the PSRC1 low-expression group, the top five active biological functions were adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains, phagocytosis, T cell-mediated immunity, specific granule, and immune receptor activity; the top five active pathways were the complement and coagulation cascades, cytokine-cytokine receptor interaction, and drug-metabolism cytochrome P450, hematopoietic cell lineage, and metabolism of xenobiotic by cytochrome P450.

In the COLEC12 high expression group, the top five active biological functions were humoral immune response, phagocytosis, positive regulation of Extracellular-regulated kinase 1 and Extracellular-regulated kinase 2 cascade, response to chemokine, and immune receptor activity; the top five active pathways were chemokine signaling pathway, complement and coagulation cascades, cytokine-cytokine receptor interaction, hematopoietic cell lineage, and vascular smooth muscle contraction; In the COLEC12 low-expression, the top five active biological functions group were cell cycle checkpoint signaling, mitotic sister chromatid separation, chromosome region, chromosome centromeric region, and condensed chromosome; the top five active pathways are aminoacyl tRNA biosynthesis, cell cycle, DNA replication, protein export, and spliceosome. Details can be obtained from Supplementary Figure S2.

3.6 Survival analysis

This study extracted and sorted out the survival data of 66 patients with SCLC. Specific information can be obtained from the Supplementary Table S8. The results demonstrated that PSRC1 was significantly associated with the overall survival time of SCLC patients (P = 0.015). The survival rate in the group with high expression of PSRC1 was significantly lower than that in the group with low expression of PSRC1, as shown in Figure 10.

Figure 10

Figure 10. Survival analysis of co-expressed genes. The figure shows the relationship between five CEGs (PSRC1, PSAT1, PLLP, COLEC12, HP) and the overall survival of patients with SCLC. The red represents the group with high gene expression, and the blue represents the group with low gene expression. The horizontal axis represents overall survival time, and the vertical axis represents survival probability. The table beneath it enumerates the number of patients who remain alive at different time points spanning from 0 to 15 years.

3.7 Validation group analysis

The results of the validation group analysis showed a significant increase (P<0.001) in the expression of PSAT1 and PSRC1, and showed a significant decrease (P<0.001) in the expression of COLEC12, PLLP, and HP in SCLC group, as shown in Figure 11. The expression levels of CEGs in the validation group were consistent with the results of this study, which increased the reliability and robust of the results of MR analysis.

Figure 11

Figure 11. Validation group differential analysis. Box plots comparing the expression status of co-expressed genes between the treat group and control group in the validation set. Treat: small cell lung cancer group, control: normal tissue group. ***P<0.001.

4 Discussion

In the early stage of our research, we attempted to search for the information of each patient through the source literature of the datasets, so as to conduct a stratified analysis of factors such as age, gender, and disease stage. However, only a small portion of the datasets had information in this regard. Considering this limitation, we were worried that the research results could not fully cover every patient in the datasets. Therefore, we have not taken this aspect as the main objective of our research for the time being.

In our study, three SCLC microarray datasets were extracted from the GEO database, including 107 SCLC samples and 66 normal tissue samples. Through in-depth analysis of the datasets, we successfully identified 857 DEGs (442 up-regulated genes and 407 down-regulatedgenes) that may be critically involved in the pathophysiology of SCLC. DEGs further intersect with the closely related genes of SCLC from the results of MR analysis, we obtained two up-regulated CEGs (PSAT1, PSRC1) and three down-regulated CEGs (COLEC12, PLLP, HP), and MR analysis confirmed that increased expression of PSAT1 and PSRC1 were associated with increased risk of SCLC, while decreased expression of COLEC12, PLLP, and HP were associated with increased risk of SCLC, suggesting that they may play a key role in the pathogenesis of SCLC. Furthermore, an independent cohort of patients with SCLC was employed to investigate the correlation between CEGs and overall survival duration. The findings indicated that PSRC1 was significantly related to the overall survival of SCLC patients and the survival rate in the high-expression group was markedly lower compared to that in the low-expression group, with a P-value of 0.015, suggesting a potential prognostic role of PSRC1 expression levels in SCLC survival.

4.1 Up-regulated genes

PSAT1 (Phosphoserine aminotransferase 1), located on chromosomal locus 9q21.2, is involved in the serine synthesis pathway and is a member of the class V pyridoxal phosphate-dependent aminotransferase family (14). The critical role of PSAT1 as a key enzyme in the synthesis of serine and glycine has been verified and the overactivation of the serine/glycine metabolic pathway may promote tumorigenesis by facilitating the cell cycle process (15). Previous studies have confirmed that PSAT1 is an oncogene that is usually over-expressed in malignant tumors, especially in non-small cell lung cancer (NSCLC) (16). Many studies have suggested that PSAT1 may be play a key role in NSCLC, Yang et al. concluded that over-expressed PSAT1 promoted cell cycle protein D1 activity and inhibited its degradation, thereby promoting the proliferation of NSCLC cells; Chan et al. found that over-expressed PSAT1 promoted lung adenocarcinoma (LUAD) metastasis and led to poor prognosis by inhibiting several biological factors; Luo et al. suggested that over-expressed PSAT1 promoted LUAD metastasis and was also responsible for resistance to epidermal growth factor receptor inhibitors in LUAD patients (15, 17, 18). The results of this study showed that PSAT1 expression was significantly up-regulated in SCLC consistent with most malignant tumors and the results of enrichment analysis also indicated that PSAT1 is involved in multiple pathways in the cell cycle process, such as glycine, serine and threonine metabolism. However, there are limited studies related to PSAT1 in SCLC, our results may reveal a new research direction for an in-depth exploration of the pathogenesis of SCLC.

PSRC1 (Proline and serine rich coiled-coil 1), located at chromosomal locus 1p13.3, is a novel microtubule-associated protein encoding proline-and serine-rich proteins and is a down-regulated target of p53 and is responsible for p53-mediated gene repression with oncogenic features (19). It has been confirmed that PSRC1 promotes cell growth through multiple pathways, such as enhancing β-catenin dependent transactivation and cyclin D1 production by binding to adenomatous polyposis coli 2 (20). Previous studies have confirmed that PSRC1 plays an important role in a variety of cancers, Liu et al. suggested that over-expression of PSRC1 in patients with low-grade gliomas was an independent risk factor for shortening their overall survival; Long et al. concluded that PSRC1 is a novel biomarker for the diagnosis and treatment of pancreatic cancer; Han et al. observed that PSRC1 was highly expressed in LUAD and Lung squamous cell carcinoma and they concluded that high expression of PSRC1 was an independent risk factor for overall survival and progression-free survival in patients with LUAD (21–23). The outcomes of this research have verified that PSRC1 exhibits high expression levels in SCLC. Based on these findings, we speculate that its elevated expression may promote cell cycle progression, thereby accelerating the development of small cell lung cancer. Moreover, the survival analysis has demonstrated that the elevated expression of PSRC1 correlates with a lower survival rate in SCLC, which aligns with the circumstances observed in non-small cell lung cancer. In view of these findings, we believe that PSRC1 may be a potential therapeutic target for SCLC.

Interestingly, a potential link between PSAT1 and PSRC1 has been found in previous studies of non-small cell lung cancer, in which increased PSAT1 expression inhibits the degradation of the cell cycle protein d1, while elevated PSRC1 expression promotes the production of this protein (17, 20). Because of the limited research on these two genes in SCLC currently, it is still uncertain whether such a combined action exists in SCLC, yet our research may offer a new research direction.

4.2 Down-regulated genes

COLEC12 (Collectin subfamily member 12), located at chromosomal locus 18p11.32, is a transmembrane scavenger receptor C-type lectin that recognizes certain bacteria and fungi, leading to phagocytosis (24). COLEC12 has been shown to be aberrantly expressed and play an important role in a variety of cancers, Kong et al. found that the expression level of COLEC12 was significantly elevated in a group of gastric cancer cells by controlled analysis; Sun et al. et al. found that COLEC12 was highly expressed in patients with advanced gastric cancer and suggested that it has the ability to promote the proliferation, migration and invasion of gastric cancer cells and inhibit gastric cancer cell and inhibit apoptosis of gastric cancer cells; Wang et al. found that COLEC12, a cancer stemness-related gene, could predict the prognosis of colon adenocarcinoma patients (25–27). The results of this study revealed that COLEC12 expression was down-regulated in SCLC. Moreover, the enrichment analysis revealed that it is involved in biological functions and pathways such as endocytic vesicle, scavenger receptor activity, and phagosome. The protein encoded by COLEC12 is a scavenger receptor, which is expressed on various types of cells, like macrophages. It can recognize the microbial antigens of pathogens invading cells, initiate phagocytosis and activate downstream immune responses to combat and eliminate the pathogens (28). This is in line with the results of our enrichment analysis, underlining its crucial role in host defense. Based on these findings, we presume that a decrease in the expression of COLEC12 may lead to a weakened ability to resist pathogens, subsequently influencing the development of SCLC. However, the research on the role of COLEC12 in SCLC is relatively scarce at present. Our findings may offer a new perspective for understanding the cellular biology of SCLC.

HP, located on chromosomal locus 16q22.2, can encode a preproprotein, which is processed to produce haptoglobin. Haptoglobin is an acute phase response protein with anti-inflammatory and antioxidant properties, which is synthesized mainly by the liver, but can also be synthesized by the lungs, spleen, kidneys, skin and adipose tissue (29). The protein produced by the HP gene has been shown to play an important role in lung cancer. In NSCLC, several studies have found significantly elevated levels of haptoglobin in tumor tissues as well as in patient serum compared to normal controls, and suggested that high levels of haptoglobin correlate with TNM staging, lymph node metastasis, and distant metastasis (29, 30). In SCLC, Shah et al. found that the mean level of α- haptoglobin and β-haptoglobin in SCLC serum was increased (31). However, our study found that HP gene expression was down-regulated in SCLC tissues. The outcomes of GO enrichment analysis demonstrated its involvement in biological functions such as endocytic vesicle, endocytic vesicle lumen, haptoglobin-hemoglobin complex, tertiary granule lumen, specific granule lumen, and antioxidant activity. Free hemoglobin exhibits toxicity on account of nitric oxide scavenging and its direct pro-oxidative and pro-inflammatory activities. Previous research has indicated that the initial step in the classical pathway for the clearance of free hemoglobin hinges upon the formation of the hemoglobin-haptoglobin complex, and subsequently, the free hemoglobin is broken down within macrophages via endocytosis (32). This is consistent with the results of our enrichment analysis, indicating the importance of HP in the antioxidant and anti-inflammatory responses. We presume that the decreased expression of HP in tumor tissues may inhibit this regulatory pathway, thereby weakening the antioxidant and anti-inflammatory effects and promoting the development of SCLC. However, the research on the role of HP in SCLC is relatively scarce at present. Our findings may offer a new perspective for understanding the cellular biology of SCLC.

PLLP (Plasmolipin), located at chromosomal locus 16q13, is a component of synaptic plasma membranes, myelin sheaths and endocytic vesicles, and is involved in myelin formation (33, 34). This is consistent with the results of our enrichment analysis. Previous studies have found a significant increase in PLLP expression in mouse models of breast cancer and melanoma brain metastasis, and speculated that it may play a role in brain metastasis of these two malignant tumors (35, 36). In glioblastoma, Luo et al. found that the expression of PLLP in the cells of peritumoral brain zone was lower than that in the cells of the core zone of the tumor (37). Recent study have reported a significant correlation between PLLP and renal smoky cell carcinoma, renal clear cell carcinoma, and endometrial carcinoma of the uterine corpus (34). In this study, we found that the expression of PLLP is down-regulated in small cell lung cancer and the results of enrichment analysis indicated its involvement in biological functions such as compact myelin, myelin sheath, and structural constituent of myelin sheath. As the main component of myelin, PLLP promotes myelin formation by inducing myelin precursor domains in the Golgi apparatus after phosphorylation (38).Based on our findings, we speculate that the reason why the decreased expression of PLLP can promote the development of SCLC might be related to the neuroendocrine characteristics of SCLC. However, there are limited studies related to the role of PLLP in SCLC and further studies are still needed to explore its specific mechanism of action in SCLC.

In addition to exploring gene-specific effects on SCLC, we expanded our study to the immunological level of SCLC. We performed an analysis of immune cell infiltration levels by the CIBERSORT algorithm. We found significantly different immune cell infiltration levels between SCLC and normal tissue samples, which is broadly in line with previous findings (39, 40). Based on the results of differential expression analysis, we further analyzed the correlations between the five co-expressed genes and 22 immune cell subpopulations, and the results showed that all five co-expressed genes were correlated with some immune cells, especially PSRC1 and COLEC12. Specifically, we found that in the up-regulated cancer-enriched genes (CEGs), PSRC1 was positively correlated with T cells follicular helper and activated NK cells. Moreover, in the down-regulated CEGs, COLEC12 was negatively correlated with T cells follicular helper. Compared with the control group, the proportion of T cells follicular helper in SCLC was significantly increased. Therefore we further explored the activity levels of PSRC1 and COLEC12 in SCLC by GSEA method, and the results showed that PSRC1 and COLEC12 play a multifaceted and complex role in the pathogenesis of SCLC. The expression levels of PSRC1 may be associated with changes in biological functional activities related to immune responses, chromosome segregation, and others. The active pathways in the PSRC1 high-expression group indicated the possible presence of cell division process which is mediated by the regulatory role of PSRC1 in SCLC, and the active pathways in the PSRC1 low-expression group indicated the possible presence of humoral immune responses and the metabolic processes of cytochrome P450 which are mediated by the regulatory role of PSRC1 in SCLC. While the expression levels of COLEC12 may be associated with changes in biological functional activities related to cell cycle, immune response, and others. The active pathways in the COLEC12 high-expression group indicated the possible presence of immune response which is mediated by the regulatory role of COLEC12 in SCLC, and the active pathways in the COLEC12 low-expression group indicated the possible presence of process of cell division which is mediated by the regulatory role of COLEC12 in SCLC.

Finally, we used a validation group for difference expression analysis, and the results were consistent with those of the MR analysis, which confirmed the reliability of our findings. The elevated expression of PSRC1 may promote the development of SCLC by influencing the cell cycle process and immune cell infiltration, while the increased expression of PSAT1 may contribute to the development of SCLC by affecting the cell cycle process. The decreased expression of COLEC12 may suppress the host defense function and immune cell infiltration, thereby promoting the development of SCLC; The decreased expression of HP may inhibit the clearance pathway of free hemoglobin, leading to the enhancement of oxidative stress and inflammatory response, thereby promoting the development of SCLC; and the decreased expression of PLLP may promote the development of SCLC by influencing the neuroendocrine characteristics. A graphical summary is visible in Figure 12. However, all these possibilities need to be verified by further experiments to elucidate the specific roles of these genes in SCLC. Certainly, the potential therapeutic targets identified in the latest research merit further attention. For instance, the research team found that the expression of Three prime repair exonuclease 1 (TREX1) is up-regulated in SCLC. Through fundamental experiments, it was discovered that suppressing its expression can enhance anti-tumor immunity and augment the efficacy of chemotherapy and/or immunotherapy for chemotherapy-resistant SCLC (41). The other research team analyzed the genome-wide loss-of-function screening database to look for vulnerabilities in SCLC and they believed that S-phase kinase-associated protein 2 (SKP2) could be regarded as a new therapeutic target for SCLC, regardless of whether it is RB1 wild-type or mutant SCLC (42).

Figure 12

Figure 12. Graphic abstract. The left part illustrates the pathogenesis model of small cell lung cancer suggested by five co-expressed genes. The right part shows the infiltration of immune cells in small cell lung cancer as well as the relationships between PSRC1, COLEC12 and the infiltration of immune cells.

In summary, this study performed a detailed analysis of SCLC using bioinformatics as well as statistical methods to identify key genes and pathways, focusing on the association between co-expressed genes (PSAT1, PSRC1, COLEC12, PLLP, and HP) and SCLC. These genes and their associated functions and pathways may be potential targets for targeting specific molecular pathways for the treatment of SCLC, which lays a theoretical foundation for the subsequent development of new therapeutic interventions. There are some limitations in this study. First, only a single bioinformatics analysis was performed, lack of biological experiments for validation. Second, due to the incomplete information in the original datasets, at present, this study has not yet carried out hierarchical analyses on factors such as age, gender, and disease stage. In the future, we will conduct deeper clinical studies and laboratory validation.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author/s.

Author contributions

ZL: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. PJ: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Visualization, Writing – original draft, Writing – review & editing. YL: Methodology, Project administration, Supervision, Writing – review & editing. ZZ: Supervision, Validation, Writing – review & editing. JZ: Funding acquisition, Validation, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was funded by the Natural Science Foundation of Zhejiang Province(project number LY20H290001) and by Zhejiang Chinese Medical University Special Research Projects of Affiliated Hospitals(project number 2022FSYYZZ29).

Acknowledgments

We express our gratitude for the valuable data resources provided by the researchers and the databases.

Conflict of interest

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.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2024.1464259/full#supplementary-material

Abbreviations

SCLC, small-cell lung cancer; MR, Mendelian randomization; GEO, gene Expression Omnibus; DEG, differentially expressed genes; CEGs, co-expressed genes; ES-SCLC, extensive-stage small-cell lung cancer; IVs, instrumental variable; GWAS, genome-wide association study; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genome; GSEA, Gene Set Enrichment Analysis; IVW, Inverse variance weighting; OR, Odds ratio; CI, confidence interval; NSCLC, non-small cell lung cancer; LUAD, lung adenocarcinoma.

References

1. Rudin CM, Brambilla E, Faivre-Finn C, Sage J. Small-cell lung cancer. Nat Rev Dis Primers. (2021) 7:3. doi: 10.1038/s41572-020-00235-0

PubMed Abstract | Crossref Full Text | Google Scholar

2. Yuan M, Zhao Y, Arkenau HT, Lao T, Chu L, Xu Q. Signal pathways and precision therapy of small-cell lung cancer. Signal Transduct Target Ther. (2022) 7:187. doi: 10.1038/s41392-022-01013-y

PubMed Abstract | Crossref Full Text | Google Scholar

3. Megyesfalvi Z, Gay CM, Popper H, Pirker R, Ostoros G, Heeke S, et al. Clinical insights into small cell lung cancer: Tumor heterogeneity, diagnosis, therapy, and future directions. CA Cancer J Clin. (2023) 73:620–52. doi: 10.3322/caac.21785

PubMed Abstract | Crossref Full Text | Google Scholar

4. Zugazagoitia J, Paz-Ares L. Extensive-stage small-cell lung cancer: first-line and second-line treatment options. J Clin Oncol. (2022) 40:671–80. doi: 10.1200/JCO.21.01881

PubMed Abstract | Crossref Full Text | Google Scholar

5. Rudin CM, Reck M, Johnson ML, Blackhall F, Hann CL, Yang JC, et al. Emerging therapies targeting the delta-like ligand 3 (DLL3) in small cell lung cancer. J Hematol Oncol. (2023) 16:66. doi: 10.1186/s13045-023-01464-y

PubMed Abstract | Crossref Full Text | Google Scholar

6. Levin MG, Klarin D, Assimes TL, Freiberg MS, Ingelsson E, Lynch J, et al. Genetics of smoking and risk of atherosclerotic cardiovascular diseases: A mendelian randomization study. JAMA Netw Open. (2021) 4:e2034461. doi: 10.1001/jamanetworkopen.2020.34461

PubMed Abstract | Crossref Full Text | Google Scholar

7. Birney E. Mendelian randomization. Cold Spring Harb Perspect Med. (2022) 12(4):a041302. doi: 10.1101/cshperspect.a041302

PubMed Abstract | Crossref Full Text | Google Scholar

8. Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. (2021) 53:1300–10. doi: 10.1038/s41588-021-00913-z

PubMed Abstract | Crossref Full Text | Google Scholar

9. McKay JD, Hung RJ, Han Y, Zong X, Carreras-Torres R, Christiani DC, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet. (2017) 49:1126–32. doi: 10.1038/ng.3892

PubMed Abstract | Crossref Full Text | Google Scholar

10. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol Biol. (2018) 1711:243–59. doi: 10.1007/978-1-4939-7493-1_12

PubMed Abstract | Crossref Full Text | Google Scholar

11. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. (2005) 102:15545–50. doi: 10.1073/pnas.0506580102

PubMed Abstract | Crossref Full Text | Google Scholar

12. George J, Lim JS, Jang SJ, Cun Y, Ozretić L, Kong G, et al. Comprehensive genomic profiles of small cell lung cancer. Nature. (2015) 524:(7563)47–53. doi: 10.1038/nature14664

PubMed Abstract | Crossref Full Text | Google Scholar

13. Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. (2011) 40:755–64. doi: 10.1093/ije/dyr036

PubMed Abstract | Crossref Full Text | Google Scholar

14. Zhang Y, Li J, Dong X, Meng D, Zhi X, Yuan L, et al. PSAT1 regulated oxidation-reduction balance affects the growth and prognosis of epithelial ovarian cancer. Onco Targets Ther. (2020) 13:5443–53. doi: 10.2147/OTT.S250066

PubMed Abstract | Crossref Full Text | Google Scholar

15. Chan YC, Chang YC, Chuang HH, Yang YC, Lin YF, Huang MS, et al. Overexpression of PSAT1 promotes metastasis of lung adenocarcinoma by suppressing the IRF1-IFNγ axis. Oncogene. (2020) 39(12):2509–22. doi: 10.1038/s41388-020-1160-4

PubMed Abstract | Crossref Full Text | Google Scholar

16. Feng M, Cui H, Tu W, Li L, Gao Y, Chen L, et al. An integrated pan-cancer analysis of PSAT1: A potential biomarker for survival and immunotherapy. Front Genet. (2022) 13:975381. doi: 10.3389/fgene.2022.975381

PubMed Abstract | Crossref Full Text | Google Scholar

17. Yang Y, Wu J, Cai J, He Z, Yuan J, Zhu X, et al. PSAT1 regulates cyclin D1 degradation and sustains proliferation of non-small cell lung cancer cells. Int J Cancer. (2015) 136:E39–50. doi: 10.1002/ijc.v136.4

PubMed Abstract | Crossref Full Text | Google Scholar

18. Luo MY, Zhou Y, Gu WM, Wang C, Shen NX, Dong JK, et al. Metabolic and nonmetabolic functions of PSAT1 coordinate signaling cascades to confer EGFR inhibitor resistance and drive progression in lung adenocarcinoma. Cancer Res. (2022) 82:3516–31. doi: 10.1158/0008-5472.CAN-21-4074

PubMed Abstract | Crossref Full Text | Google Scholar

19. Hsieh PC, Chang JC, Sun WT, Hsieh SC, Wang MC, Wang FF. p53 downstream target DDA3 is a novel microtubule-associated protein that interacts with end-binding protein EB3 and activates beta-catenin pathway. Oncogene. (2007) 26(34):4928–40. doi: 10.1038/sj.onc.1210304

PubMed Abstract | Crossref Full Text | Google Scholar

20. Yang JD, Seol SY, Leem SH, Kim YH, Sun Z, Lee JS, et al. Genes associated with recurrence of hepatocellular carcinoma: integrated analysis by gene expression and methylation profiling. J Korean Med Sci. (2011) 26:1428–38. doi: 10.3346/jkms.2011.26.11.1428

PubMed Abstract | Crossref Full Text | Google Scholar

21. Liu Z, Liang W, Zhu Q, Cheng X, Qian R, Gao Y. PSRC1 regulated by DNA methylation is a novel target for LGG immunotherapy. J Mol Neurosci. (2023) 73:516–28. doi: 10.1007/s12031-023-02133-y

PubMed Abstract | Crossref Full Text | Google Scholar

22. Long J, Wu XD, Liu Z, Xu YH, Ge CL. Integrated regulatory network involving differently expressed genes and protein-protein interaction on pancreatic cancer. Eur Rev Med Pharmacol Sci. (2015) 19:2423–8.

Google Scholar

23. Han R, Guan Y, Tang M, Li M, Zhang B, Fei G, et al. High expression of PSRC1 predicts poor prognosis in lung adenocarcinoma. J Cancer. (2023) 14:3321–34. doi: 10.7150/jca.88635

PubMed Abstract | Crossref Full Text | Google Scholar

24. Ma YJ, Hein E, Munthe-Fog L, Skjoedt MO, Bayarri-Olmos R, Romani L, et al. Soluble collectin-12 (CL-12) is a pattern recognition molecule initiating complement activation via the alternative pathway. J Immunol. (2015) 195:3365–73. doi: 10.4049/jimmunol.1500493

PubMed Abstract | Crossref Full Text | Google Scholar

25. Kong W, Wang Z, Wang B. Unveiling DNA damage repair-based molecular subtypes, tumor microenvironment and pharmacogenomic landscape in gastric cancer. Front Genet. (2023) 14:1118889. doi: 10.3389/fgene.2023.1118889

PubMed Abstract | Crossref Full Text | Google Scholar

26. Sun X, Zhang Q, Shu P, Lin X, Gao X, Shen K. COLEC12 promotes tumor progression and is correlated with poor prognosis in gastric cancer. Technol Cancer Res Treat. (2023) 22:15330338231218163. doi: 10.1177/15330338231218163

PubMed Abstract | Crossref Full Text | Google Scholar

27. Wang W, Xu C, Ren Y, Wang S, Liao C, Fu X, et al. A novel cancer stemness-related signature for predicting prognosis in patients with colon adenocarcinoma. Stem Cells Int. (2021) 2021:7036059. doi: 10.1155/2021/7036059

PubMed Abstract | Crossref Full Text | Google Scholar

28. Taban Q, Mumtaz PT, Masoodi KZ, Haq E, Ahmad SM. Scavenger receptors in host defense: from functional aspects to mode of action. Cell Commun Signal. (2022) 20:2. doi: 10.1186/s12964-021-00812-0

PubMed Abstract | Crossref Full Text | Google Scholar

29. Lu J, Wang Y, Yan M, Feng P, Yuan L, Cai Y, et al. High serum haptoglobin level is associated with tumor progression and predicts poor prognosis in non-small cell lung cancer. Oncotarget. (2016) 7(27):41758–66. doi: 10.18632/oncotarget.9676

PubMed Abstract | Crossref Full Text | Google Scholar

30. Najafi Z, Mohamadnia A, Ahmadi R, Mahmoudi M, Bahrami N, Khosravi A, et al. Proteomic and genomic biomarkers for Non-Small Cell Lung Cancer: Peroxiredoxin, Haptoglobin, and Alpha-1 antitrypsin. Cancer Med. (2020) 9:3974–82. doi: 10.1002/cam4.v9.11

PubMed Abstract | Crossref Full Text | Google Scholar

31. Shah A, Singh H, Sachdev V, Lee J, Yotsukura S, Salgia R, et al. DIFFERENTIAL SERUM LEVEL OF SPECIFIC HAPTOGLOBIN ISOFORMS IN SMALL CELL LUNG CANCER. Curr Proteomics. (2010) 7:49–65. doi: 10.2174/157016410790979635

PubMed Abstract | Crossref Full Text | Google Scholar

32. Braniewska A, Skorzynski M, Sas Z, Dlugolecka M, Marszalek I, Kurpiel D, et al. A novel process for transcellular hemoglobin transport from macrophages to cancer cells. Cell Commun Signal. (2024) 22:570. doi: 10.1186/s12964-024-01929-8

PubMed Abstract | Crossref Full Text | Google Scholar

33. Shulgin AA, Lebedev TD, Prassolov VS, Spirin PV. Plasmolipin and its role in cell processes. Mol Biol. (2021) 55:773–85. doi: 10.1134/S0026893321050113

PubMed Abstract | Crossref Full Text | Google Scholar

34. Labat-de-Hoz L, Rubio-Ramos A, Correas I, Alonso MA. The MAL family of proteins: normal function, expression in cancer, and potential use as cancer biomarkers. Cancers (Basel). (2023) 15(10):2801. doi: 10.3390/cancers15102801

PubMed Abstract | Crossref Full Text | Google Scholar

35. Bos PD, Zhang XH, Nadal C, Shu W, Gomis RR, Nguyen DX, et al. Genes that mediate breast cancer metastasis to the brain. Nature. (2009) 459(7249):1005–9. doi: 10.1038/nature08021

PubMed Abstract | Crossref Full Text | Google Scholar

36. Sato R, Nakano T, Hosonaga M, Sampetrean O, Harigai R, Sasaki T, et al. RNA sequencing analysis reveals interactions between breast cancer or melanoma cells and the tissue microenvironment during brain metastasis. BioMed Res Int. (2017) 2017:8032910. doi: 10.1155/2017/8032910

PubMed Abstract | Crossref Full Text | Google Scholar

37. Luo X, Xu S, Zhong Y, Tu T, Xu Y, Li X, et al. High gene expression levels of VEGFA and CXCL8 in the peritumoral brain zone are associated with the recurrence of glioblastoma: A bioinformatics analysis. Oncol Lett. (2019) 18:6171–9. doi: 10.3892/ol.2019.10988

PubMed Abstract | Crossref Full Text | Google Scholar

38. Li M, Du Y, Zhang X, Zhou W. Research advances of MAL family members in tumorigenesis and tumor progression (Review). Mol Med Rep. (2024) 29(4):57. doi: 10.3892/mmr.2024.13181

PubMed Abstract | Crossref Full Text | Google Scholar

39. Ni J, Si X, Wang H, Zhang X, Zhang L. Prognostic biomarkers and immune cell infiltration characteristics in small cell lung cancer. Cancer Pathog Ther. (2023) 1:18–24. doi: 10.1016/j.cpt.2022.09.004

PubMed Abstract | Crossref Full Text | Google Scholar

40. Yunchu Y, Miyanaga A, Matsuda K, Kamio K, Seike M. Exploring effective biomarkers and potential immune related gene in small cell lung cancer. Sci Rep. (2024) 14:7604. doi: 10.1038/s41598-024-58454-4

PubMed Abstract | Crossref Full Text | Google Scholar

41. Murayama T, Mahadevan NR, Meador CB, Ivanova EV, Pan Y, Knelson EH, et al. Targeting TREX1 induces innate immune response in drug-resistant small-cell lung cancer. Cancer Res Commun. (2024) 4:2399–414. doi: 10.1158/2767-9764.CRC-24-0360

PubMed Abstract | Crossref Full Text | Google Scholar

42. Matsumoto N, Tajima K, Takahashi F, Mitsuishi Y, Wirawan A, Hidayat M, et al. Clinical potential of SKP2 as diagnostic marker and therapeutic target in small cell lung cancer. Respir Investig. (2024) 62:901–9. doi: 10.1016/j.resinv.2024.07.014

PubMed Abstract | Crossref Full Text | Google Scholar