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/).

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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.

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.

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).

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.