Pan-cancer analysis reveals igfl2 as a potential target for cancer prognosis and immunotherapy

ABSTRACT _Insulin-like growth factor like family member 2_ (_IGFL2_) is a gene in the _IGFL_ family, located on chromosome 19, whose role in cancer is unclear, and the aim of this study was

to investigate the relevance of _IGFL2_ expression, prognosis, immunity, and mutation in pan-cancer. Obtaining information from The Cancer Genome Atlas and The Genotype-Tissue Expression

Project (GTEx) databases for expression analysis and combining with The Gene Expression Profile Interaction Analysis database for prognostic aspects. Analysis of immune cell infiltration by

TIMER and CIBERSORT algorithms. Calculation of correlation of immune-related genes with _IGFL2_ expression and tumor mutational burden and microsatellite instability. Mutations and DNA

methylation were analyzed using the cBioPortal database and the UALCAN database, and functional enrichment was performed using Gene set enrichment analysis (GSEA). _IGFL2_ expression is

significantly elevated in tumor tissue and high expression has a worse prognosis in most cancers. In immune correlation analysis, it was associated with most immune cells and immune-related

genes. In most cancers, _IGFL2_ methylation is lower and the group with mutations in _IGFL2_ has a worse prognosis than the normal group. The GSEA analysis showed that _IGFL2_ was

significantly enriched in signaling and metabolism. _IGFL2_ may be involved in the development of many types of cancer, influencing the course of cancer with different biological functions.

It may also be a biomarker for tumor immunotherapy. SIMILAR CONTENT BEING VIEWED BY OTHERS PAN-CANCER ANALYSIS REVEALS IL32 IS A POTENTIAL PROGNOSTIC AND IMMUNOTHERAPEUTIC BIOMARKER IN

CANCER Article Open access 07 April 2024 CONSTRUCTION OF AN IMMUNE-RELATED RISK SCORE SIGNATURE FOR GASTRIC CANCER BASED ON MULTI-OMICS DATA Article Open access 16 January 2024 COMPREHENSIVE

ANALYSIS OF CLEC FAMILY GENES IN GASTRIC CANCER PROGNOSIS IMMUNE RESPONSE AND TREATMENT Article Open access 18 February 2025 INTRODUCTION Cancer remains a disease with very high morbidity

and mortality in epidemiological studies. In 2020, there will be 19.3 million cancer patients and more than 10 million deaths worldwide, with breast cancer being the most common cancer and

lung cancer having the highest mortality rate at 18 percent. 28.4 million people will have cancer by 20401. Tumor immunotherapy has been shown to be effective in cancer treatment by using

immune cells to eliminate tumor cells2.Therefore, predicting biomarkers and identifying tumor treatment targets are crucial in cancer treatment. The human _IGFL_ gene encodes a protein of

approximately 100 amino acids and contains 11 conserved cysteine residues, including two CC motifs. This family consists of four genes and two pseudogenes, _IGFL1-IGFL4_, _IGFL1P1_ and

_IGFL1P2_, all clustered on chromosome 19 at 35 kb intervals, which have structural homology with _the insulin-like growth factor_ (_IGF_) family3. The _IGF_ family of genes is a systemic

growth factor and a major regulator of cell proliferation, differentiation and apoptosis 4. Its dysfunction or dysregulation may destabilize tissues and act on target organs in an autocrine,

paracrine and endocrine manner, while activating various intracellular signaling pathways to promote cell proliferation, transformation and inhibit apoptosis, leading to the development of

malignant tumors 5. _IGF_ family members have been shown to play an important role in a variety of tumorigenesis, such as gastric cancer6, colorectal cancer7, and lung cancer8.Among the

relevant studies on the IGFL family, IGFL2 is particularly well represented and deserves analysis.Studies on _IGFL2_ have found that its expression is upregulated in many cancers, and as a

homolog of the _IGF_ family, this pattern may be consistent with _IGF_ family members. However, the mechanism of _IGFL2_ in various carcinogenesis is unclear and there is a lack of

correlation analysis of _IGFL2_. Herein, we have comprehensively analyzed the expression, prognosis, immunological and biological roles of _IGFL2_ in cancer based on TCGA database data to

explore the multifaceted relationship between _IGFL2_ and cancer. MATERIALS AND METHODS DATA ACQUISITION & PROCESSING The expression, clinical correlation, and mutation data for a total

of 10,534 cases of 33 cancers from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) database9, 10 were obtained from the UCSC browser (http://xena.ucsc.edu/) for basic

processing of raw data; and the normal tissue information was supplemented with gene data from The Genotype-Tissue Expression Project (GTEx) (http://gtexportal.org) database11 for normal

tissues. 33 cancer types were included: Adrenocortical carcinoma (ACC), Bladder Urothelial Carcinoma (BLCA), Breast invasive carcinoma (BRCA), Cervical squamous cell carcinoma and

endocervical adenocarcinoma (CESC), Cholangiocarcinoma (CHOL), Colon adenocarcinoma (COAD), Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), Esophageal carcinoma (ESCA), Glioblastoma

multiforme (GBM), Head and Neck squamous cell carcinoma (HNSC), Kidney Chromophobe (KICH), Kidney renal clear cell carcinoma (KIRC), Kidney renal papillary cell carcinoma (KIRP), Acute

Myeloid Leukemia (LAML), Brain Lower Grade Glioma (LGG), Liver hepatocellular carcinoma (LIHC), Lung adenocarcinoma (LUAD), Lung squamous cell carcinoma (LUSC), Mesothelioma (MESO), Ovarian

serous cystadenocarcinoma (OV), Pancreatic adenocarcinoma (PAAD), Pheochromocytoma and Paraganglioma (PCPG), Prostate adenocarcinoma (PRAD), Rectum adenocarcinoma (READ), Sarcoma (SARC),

Skin Cutaneous Melanoma (SKCM), Stomach adenocarcinoma (STAD), Testicular Germ Cell Tumors (TGCT), Thyroid carcinoma (THCA), Thymoma (THYM), Uterine Corpus Endometrial Carcinoma (UCEC),

Uterine Carcinosarcoma (UCS), and Uveal Melanoma (UVM). _IGFL2_ EXPRESSION ANALYSIS The expression of _IGFL2_ in pan-cancer was analyzed using the Timer2.0 (http://timer.cistrome.org/)

online tool12 Since there is too little normal tissue in some cancers, the transcriptome RNA-seq data of TCGA and GETx and normal tissue data were log2 transformed simultaneously to match

the differential expression information between tumor and normal tissue. It was also plotted with R software to determine the changes in _IGFL2_ expression in different cancer types. _p_ 

< 0.05 is statistically significant. CLINICAL DATA CORRELATION ANALYSIS According to the expression of _IGFL2_, the groups were divided into high and low expression groups by median

expression level, and COX regression analysis was performed to investigate the correlation and hazard ratio (HR) between it and the prognosis of different cancers, and the correlation

between _IGFL2_ and survival was assessed by Kaplan–Meier curve and log-rank test. Forest plots and K-M curves were plotted. In addition, clinical staging of selected tumor patients was

analyzed using The Gene Expression Profile Interaction Analysis (GEPIA) (http://gepia.cancer-pku.cn/) database13 to investigate whether expression correlated with clinical staging.

RELATIONSHIP BETWEEN _IGFL2_ EXPRESSION AND IMMUNITY Cell type identification was performed using TIMER and the CIBERSORT algorithm14 to assess the relationship between _IGFL2_ expression

and 22 immune cell subtypes based on expression files. The most commonly used drugs in immunotherapy target and inhibit the immune checkpoint pathway to help the immune system overcome the

immune escape achieved by checkpoint overexpression and reactivate immune predation by neoantigenic cancer cells. Therefore, we performed an analysis of immune checkpoints to explore the

relationship of their associated genes and, in addition, assessed the proportion of immune substrate components in the tumor microenvironment (TME) using the ESTIMATEScore15. Tumor

mutational burden (TMB) and microsatellite instability (MSI) play important roles in the tumor microenvironment16, 17, so we evaluated the relationship between _IGFL2_ on TMB and MSI. DNA

METHYLATION AND GENE MUTATION CORRELATION ANALYSIS The UALCAN (http://ualcan.path.uab.edu/analysis.html) database18 was used to analyze _IGFL2_ methylation levels in different tumors and

normal tissues, with Student's t-test for difference assessment; The online cBioPortal database (http://www.cbioportal.org/) for cancer genomics was used to obtain gene mutation

profiles and prognosis19. GSEA ENRICHMENT ANALYSIS _IGFL2_ expression was divided into high and low expression groups and analyzed for significant biological pathways by Gene set enrichment

analysis (GSEA) enrichment analysis using the gene set KEGG and the immune-related HALLMARK gene set20,21,22, where gene sets with |NES|> 1, NOM _p_ < 0.05 and FDR q < 0.25 were

considered to be significantly enriched. STATISTICAL ANALYSIS Analyses were all based on R (3.6.3) using R packages including tidyverse, survival, ggplot2, fmsb, limma, estimate, etc. _p_ 

< 0.05 was considered statistically significant. RESULTS EXPRESSION OF _IGFL2_ IN CANCER According to the TIMER2.0 database results, the difference in _IGFL2_ expression between cancer

and normal tissues was significant in most cancers, including BLCA, BRCA, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, SKCM, HNSC, STAD THCA, and UCEC, while _IGFL2_ expression

was higher in normal tissues than in cancerous tissues in GBM. Since some cancers lacked normal tissue controls, we obtained basic information from the TCGA database and supplemented the

normal tissue controls from the GTEx database, which showed (Fig. 1) that, in addition to the above cancers, the expression of ACC, CESC, DLBC, LAML, LGG, OV, PAAD, READ, TGCT, THYM and UCS

differed significantly, suggesting that _IGFL2_ may be a participant in oncogenic events. In contrast, _IGFL2_ expression was higher in normal than tumor tissues of SKCM, TGCT, UCEC, and

UCS. THE PROGNOSTIC VALUE OF _IGFL2_ IN CANCER We comprehensively analyzed the prognosis-related information in pan-cancer based on TCGA clinically relevant information, where survival

indicators included overall survival (OS), disease-free survival (DFS). the results of COX regression analysis showed (Fig. 2A) that _IGFL2_ was associated with a variety of tumors,

including KIRP, KIRC, BLCA, MESO, PAAD and other cancers. And the K-M survival curves showed (Fig. 2A) that the HR and confidence intervals of KIRC,BLCA,KIRP,MESO,PAAD were 1.55 (1.14–2.10),

1.51 (1.11–2.05), 2.70 (1.18–6.17), 1.69 (1.05–2.73), 1.54 (1.02–2.34), respectively, representing that high expression of _IGFL2_ was associated with poor prognosis. After changing the

survival index to DFS (Fig. 2B), the statistically significant cancers were PAAD, KIRP, and in the K-M survival curve analysis (Fig. 2B), the elevated expression of _IGFL2_ in PAAD 3.31

(1.33–8.22) and KIRP 2.32 (1.02–5.27) affected the prognosis. We further analyzed the relationship between _IGFL2_ expression and different clinical stages of cancer (stage0-IV) using the

GEPIA database, and the results are shown in the figure (Fig. 2C). It can be found that there is a trend of elevated _IGFL2_ expression in BLCA, KIRC, KIRP, LICH, and SKCM. Then we analyzed

the samples of KIRC, KIRP, and KICH simultaneously to obtain the results of mixed renal carcinoma, which showed that _IGFL2_ expression was significantly higher in the advanced stage of

cancer than in the early clinical stage, and was a risk factor affecting prognosis. IMMUNOCORRELATION ANALYSIS ANALYSIS OF _IGFL2_ EXPRESSION AND IMMUNE CELL INFILTRATION Based on the

TIMER2.0 database, the relationship between B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells and _IGFL2_ expression was analyzed, and the results are shown

in the figure (Fig. 3A). It can be found that _IGFL2_ was correlated with most immune cells, B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and Myeloid dendritic cell were

correlated with 9, 7, 13, 12, 15, and 24 cancers, respectively. We further calculated the correlation between _IGFL2_ and immune cell subsets using the CIBERSORT algorithm (Fig. 3B),

including B cells naive, B cells memory, Plasma cells,T cells CD8,T cells CD4 naive,T cells CD4 memory resting, T cells CD4 memory activated,T cells follicular helper,T cells regulatory

(Tregs),T cells gamma delta,NK cells resting,NK cells activated,Monocytes,Macrophages M0,Macrophages M1,Macrophages M2,Dendritic cells resting,Dendritic cells activated,Mast cells

resting,Mast cells activated,Eosinophils,Neutrophils. _IGFL2_ expression correlated with most immune cells in LUAD, BRCA, HNSC, LIHC, THCA, OV, and BLCA, with mostly negative correlations in

HNSC. Among immune cell subgroups, monocytes demonstrated negative correlation with _IGFL2_, including LAML, BRCA, ESCA, SARC, KIRP, PRAD, HNSC, KIRC, LUSC, LIHC, THCA, OV, and BLCA.

ANALYSIS OF IMMUNE-RELATED GENES The importance of immunosurveillance in determining the prognosis of various types of cancer has been widely accepted. Tumors can evade immune responses by

exploiting immune checkpoint genes. To further investigate the association between _IGFL2_ and the degree of immune infiltration in different cancers, we investigated the correlation between

_IGFL2_ and immune checkpoint gene expression (Fig. 4). _IGFL2_ expression was positively correlated with immune checkpoint genes in almost all cancers, with positive correlations with most

immune checkpoints demonstrated in BLCA, BRCA, KIRC, KIRP, LIHC, LUAD, OV, THCA UCEC, UCS and UVM. Furthermore, it is noteworthy that in HNSC, _IGFL2_ expression showed a broad negative

correlation with immune checkpoint genes. Notably, CD70 correlated relatively strongly with _IGFL2_ in ACC, while TNFSF14,IDO2 showed a relatively significant positive correlation with

ICOSLG in UCS. Immune checkpoint BTLA appeared positively correlated with CD160 in UVM. IMMUNOSUBSTRATE COMPOSITION, TMB AND MSI ANALYSIS The ESTIMATE score allows the calculation of stromal

and immune cell ratios in tumor samples to infer tumor purity15. We assessed the proportion of immune matrix components using three scores, StromalScore, ImmuneScore and ESTIMATEScore,

showed that 24 cancers were positively correlated with _IGFL2_ expression, including CHAD (r = 0.39, 0.35, 0.40), LUSC (r = 0.46, 0.21, 0.34), and THCA (r = 0.51, 0.44, 0.50), _p_ < 0.001

(Fig. 5A), indicating that _IGFL2_ expression was closely associated with the degree of immune infiltration in cancer. In the coming study, we found that tumor mutational load and

microsatellite instability have an important influence in the tumor microenvironment, and the assessment of TMB and MSI in tumors has some significance for the analysis of subsequent tumor

studies. To explore the influence of _IGFL2_ on the tumor microenvironment, we analyzed the correlation between TMB and MSI in pan-cancer (Fig. 5B). In the TMB analysis, there were positive

correlations for COAD, THYM, and UCEC, and negative correlations for DLBC, HNSC, LUAD, LUSC, SARC, and UVM. In the MSI analysis, there were positive correlations with COAD and UCEC and

negative correlations with KIRC and LUSC. PAN-CANCER ANALYSIS OF _IGFL2_ METHYLATION LEVELS AND GENETIC ALTERATIONS Both hypo- and hypermethylation of DNA contribute to the development of

tumors. We investigated the DNA methylation of _IGFL2_ using the UALCAN and TCGA databases (Fig. 6A). According to the UALCAN database, significantly lower levels of _IGFL2_ methylation were

observed in BLCA, COAD, HNSC, KIRC, LIHC, LUAD, PAAD, READ, TGCT, THCA and UCEC tissues compared to normal tissues. And methylation levels were increased in KIRP. Meanwhile, we analyzed the

_IGFL2_ mutation levels using cBioPortal (TCGA, Pan-Cancer Atlas) (Fig. 6B) and found that the highest mutation levels were found in UCS, over 6%, all of which were gene amplifications. The

prognostic impact of mutations was further demonstrated with K-M curves, using OS, DFS, disease-specific survival (DSS) and progression-free survival (PFS) as survival indicators, and

dividing the cases into mutation and normal groups, and found that the prognosis of the mutation group was significantly worse in all survival indicators, with statistically significant

results (_p_ < 0.05). It is suggested that the mutation of _IGFL2_ may have influenced the survival rate of cancer. AGGREGATION ANALYSIS To investigate the potential mechanism of _IGFL2_

involvement in carcinogenesis, GSEA was performed to identify the functional enrichment of _IGFL2_ high and low expression, and the gene sets KEGG and HALLMARK were used. the KEGG and

HALLMARK enrichment items indicated that the high expression of _IGFL2_ was mainly related to signaling and metabolism-related activities, including JAK/STAT signaling pathway, inflammatory

response, olfactory conduction, etc. (Fig. 7). The results are shown in more detail in the supplementary files (Supplementary Files). DISCUSSION In this study, we comprehensively analyzed

the effects of _IGFL2_ expression, prognosis, immunity, methylation, mutation and pathways in pan-cancer. In terms of gene expression, _IGFL2_ may be widely elevated as an oncogenic molecule

in a variety of cancers, while in prognosis-related analysis, high _IGFL2_ expression likewise resulted in shorter survival in patients with KIRC,BLCA,KIRP,MESO,PAAD.Notably, in terms of

prognosis, _IGFL2_ was particularly prominent in urologic tumors (KIRC,KIRP,BLCA), and one study found that insulin-like growth factor-II (_IGF2_) mRNA-binding protein _IMP3_ expression was

closely associated with clinical grade and prognosis of renal clear cell carcinoma23. _IGFL2_, as a homolog of insulin-like growth factor, may be a marker affecting KIRC prognosis. After

establishing that _IGFL2_ expression is broadly associated with pan-cancer prognosis, we analyzed whether it influences tumor progression in the tumor microenvironment. Tumor cells, stromal

cells, and inflammatory cells in TME suppress lymphocyte and effector cell infiltration, leading to tumor growth24 and profoundly affecting tumor prognosis25. Tumor progression is

accompanied by tumor escape from the immune system26, and tumor cells can adopt different strategies to survive and grow, thus limiting the immune system, therefore we comprehensively

analyzed the major tumor infiltrating immune cell (TIIC) landscape. This study found that _IGFL2_ expression was positively correlated with most immune cells in most cancers. In TIIC

analysis, monocytes appeared negatively correlated with IGFL2 in LAML, BRCA, ESCA, SARC, KIRP, PRAD, HNSC, KIRC, LUSC, LIHC, THCA, OV, and BLCA. Monocytes have been found to link innate and

adaptive immune responses and can influence TME through various mechanisms, inducing immune tolerance, angiogenesis and increasing tumor cell dissemination27. Further collection of more than

40 immune-related genes and analysis of the correlation between _IGFL2_ expression and the expression of these genes showed that _IGFL2_ was associated with a variety of immune-related

genes and showed a positive correlation. The above suggests whether _IGFL2_ could be an emerging target for immunotherapy. Interestingly, in immune cell infiltration analysis and immune

checkpoint analysis, _IGFL2_ showed a negative relationship with most immune cells and immune-related genes in head and neck squamous cell carcinoma, where genes such as PD-L1, PD-L and

interferon-γ were reported to have the potential to predict the therapeutic benefit of checkpoint inhibitors28, this suggests that _IGFL2_ may act on genetic checkpoints to affect cancer.

The causes affecting tumor immunity are multifaceted and include factors internal to the tumor and complex interactions between cancer cells and various components of TME. The complexity of

TME is also reflected by the stimulatory factors, inhibitory factors, and positive correlations among immune checkpoints in the same group of patients.TMB has now been shown to be a useful

biomarker for selective immune checkpoint blockade in a wide range of cancers16, and MSI has been confirmed by many authors as a prognostic indicator and a predictor of treatment efficacy29,

30, and the results show that _IGFL2_ in COAD, THYM, UCEC, DLBC, HNSC, LUAD, LUSC, SARC, UVM may serve as biomarkers for tumor immunotherapy. Methylation is one of the molecular

modifications, methylation of DNA and RNA can regulate the expression of genes involved in the differentiation and function of pro- and anti-cancer immune cells, thus affecting the

development of cancer31, while abnormal DNA methylation can lead to the development of cancer, hypermethylation in the promoter region can suppress the expression of oncogenes, and reduced

DNA methylation occurs in the early stages of tumors, activating proto-oncogenes and leading to the development of cancer32. In the analysis of DNA methylation, we found that _IGFL2_ was

reduced in methylation in a variety of cancers. The possible reason for the appearance of elevated methylation in KIRP is the silencing or inactivation of tumor suppressor genes in cancer

cells.Gene mutations are also known to be one of the main causes affecting cancer progression, and our study found that the expected survival in the _IGFL2_ mutant group was significantly

lower than that in the normal group, again consistent with previous studies. To explore the biological role of genes in tumorigenesis, we performed GSEA analysis. The long-stranded noncoding

RNA of _IGFL2_ regulates the Wnt/β-catenin signaling pathway by increasing _SATB1_ expression to promote cancer development33, and in the results of GSEA enrichment analysis, we identified

a significant enrichment of _IGFL2_ in the Wnt signaling pathway in thyroid cancer. In contrast, Toll-like receptor signaling, cell adhesion molecules (CAMs), and JAK/STAT signaling pathways

all appeared more frequently in GSEA enrichment results, suggesting that _IGFL2_ may be involved in related pathways affecting cancer progression.Toll-like receptor signaling is involved in

innate and adaptive immune processes and plays an important role in tumorigenesis and progression34, 35. CAMs can play a structural role in cell or extracellular matrix adhesion and

activate related pathways to enhance cell survival, while the tumor environment leads to proliferation and metastasis of tumor cells due to disruption of CAMs36. Aberrant activation of the

JAK/STAT signaling pathway has also been demonstrated in a variety of tumors37, 38. And in the study of bladder cancer and JAK/STAT signaling pathway, IGF family-related genes were found to

activate JAK/STAT signaling pathway to proliferate tumors39. In the immune-related HALLMARK set, the pathways of inflammatory response, epithelial mesenchymal transition, and interferon

alpha response were enriched. A recent study on IGFL2 found that miR-802 is a tumor growth suppressor and the long-stranded non-coding RNA of _IGFL2_ (_IGFL2-AS1_) can promote the

progression of gastric cancer by inhibiting the expression of miR-80240, 41, while in breast cancer studies, _IGFL2-AS1_ acts as a factor mediating the _KLF5/IGFL1_ axis and inhibits

miR4795-3p expression thereby affecting breast cancer proliferation42. Cen et al. found a significant decrease in proliferation of COAD cells after knocking down _IGFL2-AS1_43. In some

database-based studies, _IGFL2_ was found as a potential pathogenic gene or gene affecting prognosis in liver, breast, renal clear cell, and bladder cancers44,45,46,47, which is consistent

with the results of our analysis. However, although the present study did a multifaceted pan-cancer analysis of _IGFL2_, it still has some limitations; First, the study was based on data

analysis performed in a database, so its causal argument is less powerful than direct experimental studies, and further mechanistic studies would be beneficial to elucidate the role at the

molecular and cellular levels of _IGFL2_; Second, there are fewer studies on _IGFL2_ and lack of corresponding results to support it. CONCLUSION Upregulation of _IGFL2_ expression was

associated with poor patient prognosis and correlated with the level of immune cell infiltration in several cancers. Also, IGFL2 was significantly associated with the expression of immune

checkpoint markers, and reduced methylation of IGFL2 was observed in many types of cancers.In conclusion, this study is the first to analyze the multifaceted relationship between _IGFL2_ and

pan-cancer, and the results of the analysis suggest that _IGFL2_ may be a new research direction for tumor therapy. DATA AVAILABILITY Publicly available datasets were analyzed in this

study. This data can be found here: The Cancer Genome Atlas (https://portal.gdc.cancer.gov/). ABBREVIATIONS * ACC: Adrenocortical carcinoma * BLCA: Bladder Urothelial Carcinoma * BRCA:

Breast invasive carcinoma * CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma * CHOL: Cholangiocarcinoma * COAD: Colon adenocarcinoma * DLBC: Lymphoid Neoplasm Diffuse

Large B-cell Lymphoma * ESCA: Esophageal carcinoma * GBM: Glioblastoma multiforme * HNSC: Head and Neck squamous cell carcinoma * KICH: Kidney Chromophobe * KIRC: Kidney renal clear cell

carcinoma * KIRP: Kidney renal papillary cell carcinoma * LAML: Acute Myeloid Leukemia * LGG: Brain Lower Grade Glioma * LIHC: Liver hepatocellular carcinoma * LUAD: Lung adenocarcinoma *

LUSC: Lung squamous cell carcinoma * MESO: Mesothelioma * OV: Ovarian serous cystadenocarcinoma * PAAD: Pancreatic adenocarcinoma * PCPG: Pheochromocytoma and Paraganglioma * PRAD: Prostate

adenocarcinoma * READ: Rectum adenocarcinoma * SARC: Sarcoma * STAD: Stomach adenocarcinoma * SKCM: Skin Cutaneous Melanoma * TGCT: Testicular Germ Cell Tumors * THCA: Thyroid carcinoma *

THYM: Thymoma * UCEC: Uterine Corpus Endometrial Carcinoma * UCS: Uterine Carcinosarcoma * UVM: Uveal Melanoma * TME: The tumor microenvironment * TMB: Tumor mutational burden * MSI:

Microsatellite instability * GSEA: Gene set enrichment analysis * HR: Hazard ratio * CI: Confidence interval * OS: Overall survival * DFS: Disease-free survival * DSS: Disease special

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via PI3K/AKT pathway in bladder cancer. _J. Exp. Clin. Cancer Res._ 41(1), 175 (2022). Article  CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank the

Inner Mongolia Autonomous Region Health Science and Technology Program for their support. FUNDING The sources of funding are as follows: Health Science and Technology Plan Project of Inner

Mongolia Autonomous Region (No. 202201228). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * School of Public Health, Inner Mongolia Medical University, Hohhot, China Yuqi Wang, Lingyan Zhao, 

Yuan Xia, Nan Zhang, Hailing Li, Dongyang Liu, Yubo Su & Yumin Gao * Department of Pathology, School of Basic Medicine, Inner Mongolia Medical University, Hohhot, China Hongwei Yuan *

Department of Urology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China Genquan Yue * Key Laboratory of Molecular Epidemiology of Chronic Diseases, Inner Mongolia

Medical University, Hohhot, China Lingyan Zhao, Yuan Xia, Nan Zhang, Hailing Li & Yumin Gao * Department of Biochemistry and Molecular Biology, School of Basic Medicine, Inner Mongolia

Medical University, Hohhot, China Haisheng Wang Authors * Yuqi Wang View author publications You can also search for this author inPubMed Google Scholar * Hongwei Yuan View author

publications You can also search for this author inPubMed Google Scholar * Genquan Yue View author publications You can also search for this author inPubMed Google Scholar * Lingyan Zhao

View author publications You can also search for this author inPubMed Google Scholar * Yuan Xia View author publications You can also search for this author inPubMed Google Scholar * Nan

Zhang View author publications You can also search for this author inPubMed Google Scholar * Hailing Li View author publications You can also search for this author inPubMed Google Scholar *

Dongyang Liu View author publications You can also search for this author inPubMed Google Scholar * Yubo Su View author publications You can also search for this author inPubMed Google

Scholar * Haisheng Wang View author publications You can also search for this author inPubMed Google Scholar * Yumin Gao View author publications You can also search for this author inPubMed

 Google Scholar CONTRIBUTIONS Y.Q.W., Y.M.G. and H.W.Y. conceived and designed the study. Y.Q.W., D.Y.L. and Y.B.S. performed the analysis. L.Y.Z., H.L.L., Y.X. and N.Z. elaborated all the

figures. Y.Q.W. and YMG wrote the main manuscript. Y.M.G., H.S.W. and G.Q.Y. revised the manuscript. The authors read and approved the final manuscript. CORRESPONDING AUTHORS Correspondence

to Haisheng Wang or Yumin Gao. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER'S NOTE Springer Nature remains

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_et al._ Pan-cancer analysis reveals IGFL2 as a potential target for cancer prognosis and immunotherapy. _Sci Rep_ 13, 6034 (2023). https://doi.org/10.1038/s41598-023-27602-7 Download

citation * Received: 04 October 2022 * Accepted: 04 January 2023 * Published: 13 April 2023 * DOI: https://doi.org/10.1038/s41598-023-27602-7 SHARE THIS ARTICLE Anyone you share the

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