KLF4 as a Novel Prognostic Biomarker and Its Correlation with Immune Infiltrates in Lung Adenocarcinoma

doi:10.21203/rs.3.rs-2994102/v1

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KLF4 as a Novel Prognostic Biomarker and Its Correlation with Immune Infiltrates in Lung Adenocarcinoma

https://doi.org/10.21203/rs.3.rs-2994102/v1

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Background: Improving the treatment of lung adenocarcinoma (LUAD) requires urgent exploration of molecular mechanisms underlying its progression and new prognostic predictors due to low long-term survival rates. Carcinogenesis studies have highlighted the clinical implications of KLF4, a member of the Krüppel-like transcription factor family. In this study, we aimed to investigate the expression of KLF4 in LUAD, assess its correlation with clinicopathological characteristics, prognosis and tumor immune cell infiltration, and analyze its potential advantages as a prognostic marker for LUAD patients.

Methods: This study examined KLF4 expression and clinical characteristics in normal and LUAD patients using TCGA and GEO datasets. 25 pairs of LUAD and normal tissues were then validated by IHC. Then we utilized analyses including PPI, GO, KEGG, GSEA and assessment of methylation status, to explore the potential mechanism of KLF4 in LUAD. Differences in the abundance of immune cell infiltration with high and low KLF4 expression were assessed by ssGSEA. Finally, univariate and multivariate Cox regression analysis were performed to construct a nomogram for evaluating the correlation between KLF4 expression and clinical prognosis.

Results: KLF4 was downregulated among tumors compared with normal tissue in multiple LUAD cohorts (P < 0.001). KLF4-related genes were then identified through differential analysis, and functional annotation revealed its significant enrichment in the processes of cornification and peptidase activity. GSEA indicated a potential involvement of KLF4 in tumor immunoregulation. Further analysis revealed a significant correlation between the expression of KLF4 and the infiltration levels of neutrophils, mast cells, eosinophils, and NK CD56bright cells (P < 0.001). Additionally, KLF4 in LUAD was found associated with a wide range of immune markers. Notably, higher level of KLF4 significantly reduced prognostic factors including OS, DSS and PFI. Finally, a nomogram incorporating KLF4, pathologic T and N stage, and residual tumor was built and exhibited promising predictive power for assessing the survival outcomes of LUAD patients.

Conclusion: High expression of KLF4 is an independent adverse prognostic factor in LUAD, and is strongly associated with aggressive clinical features and unfavorable immune inﬁltration. Therefore, KLF4 can be used as a novel prognostic biomarker for predicting patient outcomes.

KLF4

lung adenocarcinoma

prognostic marker

immune infiltration

nomogram

Lung cancer is the leading contributor to morbidity and mortality worldwide[1]. Lung adenocarcinoma (LUAD) is the most common and aggressive pathological type of non-small cell lung cancer (NSCLC), accounting for up to 40% of cases[2]. Compared with squamous cell carcinoma, the clinical presentation of LUAD is characterized by nonspecific symptoms, often resulting in delayed diagnosis, precluding the chances of radical surgery. Despite improvements in 5-year overall survival (OS) and quality of life, the prognosis for patients with LUAD remains suboptimal. Fortunately, LUAD treatment has been at the forefront of global research, and new drugs and therapeutic modalities continue to emerge[3, 4]. Targeted therapies have been especially promising, benefiting patients with sensitive mutations, including EGFR, BRAF V600E, MET, and ALK[5]. Nonetheless, for patients without sensitive mutations and unavailable targeted drugs, traditional radiotherapy and chemotherapy are still the only choice.

In the past decade, immunotherapy has significantly transformed the treatment of LUAD by leveraging the body's own immune system to combat cancer cells. One of the most promising immunotherapies is immune checkpoint blockade, which targets crucial T cell surface proteins that curtail the immune response against cancer cells[6]. The most extensively studied checkpoint inhibitors in LUAD are PD-1 and PD-L1 inhibitors, with PD-L1 expression on tumor cells serving as a predictive biomarker for response to PD-1/PD-L1 inhibitors. Clinical trials have demonstrated the efficacy of PD-1/PD-L1 inhibitors alone or in combination with other therapies like chemotherapy, targeted therapy, or other immune checkpoint inhibitors[7]. However, it is noteworthy that many patients fail to elicit a response to immunotherapy agents due to resistance mechanisms such as target protein downregulation or alternative immune checkpoint pathway upregulation[8]. Therefore, the need for biomarkers to predict prognostic value and response to immunotherapy is critical to identify patients who could benefit the most from the treatment.

KLF4, or Krüppel-like factor 4, is a transcription factor that plays a critical role in regulating cell proliferation, differentiation, and apoptosis[9]. Its dual functions as both a tumor suppressor and an oncogene have been observed across cancers from our previous studies, including LUAD. In LUAD, KLF4 has been shown to act as a tumor suppressor by inhibiting cell proliferation and inducing apoptosis. Conversely, some studies have suggested that KLF4 promotes the growth and metastasis of LUAD cells through regulating various signaling pathways[10]. The mechanism by which KLF4 mediates these effects in LUAD and its impact on the efficacy of immunotherapy are yet to be fully understood. This study aimed to investigate the relationship between KLF4 and clinical features of LUAD patients and to determine whether it could be targeted for therapeutic purposes.

Patient Characteristics

We retrieved clinical and RNA-sequencing data for 594 patients from the Cancer Genome Atlas (TCGA) datasets, including 58 patients with matched normal tissue samples. The clinicopathological characteristics of these patients were shown in Additional file 1. We also obtained gene expression data of normal lung tissues (n = 288) from the Genotype Tissue Expression Project (GTEx) datasets to increase sample size. Two more datasets from the Gene Expression Omnibus database (GEO) with normal vs LUAD tissues were introduced: GSE10072 contains normal lung tissues (n = 48) and LUAD tissues (n = 58), and GSE115002 contains normal lung tissues (n = 51) and LUAD tissues (n = 52).

Low Expression of KLF4 in LUAD

According to the pan-cancer analysis, KLF4 expression was found generally low in cancers including ACC, BLCA, BRCA, CESC, COAD, HNSC, KICH, LUAD, LUSC, OV, READ, SKCM, THCA, UCEC and UCS (Fig. 1A). Specifically, in LUAD samples, KLF4 expression was significantly lower than that in normal lung tissues (p < 0.001) (Fig. 1B). This trend was also observed in 58 paired LUAD tissues (p < 0.001) (Fig. 1C). Similarly, the level of KLF4 expression were found significantly lower in LUAD than normal tissues in two GEO datasets: GSE10072 (Fig. 1D) and GSE115002 (Fig. 1E).

To further investigate the expression of KLF4, immunohistochemical (IHC) staining was conducted on a cohort comprising 25 cases of LUAD tissues paired with noncancerous tissues, collected from our hospital unit (details seen Additional file 2). Of the 25 samples initially collected, 2 were subsequently excluded due to lacking of cancer tissues from the paraffined tissues. The final cohort consisted of 23 patients with a mean age of 50.4 years (range 26–78 years). Consistent with previous analyses, KLF4 expression was detected a rather lower density staining and significant downregulation (P = 0.001) in LUAD as compared to the noncancerous tissues (Fig. 2).

Association of KLF4 Expression Levels with the Clinical Characteristics

We used TCGA database to analyze how different pathologic features of LUAD samples affected KLF4 transcription. As shown in Table 1 and Fig. 3, KLF4 expression was consistently lower in LUAD patients than in controls, regardless of the sub-group. The results of sub-group analysis, including pathologic stage, T stage, N stage, M stage, age, gender, ethnicity, smoking status, residual tumor, OS event, DSS event, and PFI event also indicated a significant reduction in KLF4 expression in LUAD patients compared to the controls (Fig. 3). Moreover, univariate logistic regression analyses revealed statistically significant differences between the high and low KLF4 expression groups in terms of T stage (T3 & T4 vs T1 & T2) and gender (male vs. female), with ORs of 1.875 (95% CI = 1.117–3.209) and 1.538 (95% CI = 1.094–2.167), respectively (Table 2).

Table 1

Clinicopathological characteristics of high- and low-KLF4 expression groups.
Characteristics	Low expression of KLF4	High expression of KLF4	p
n	267	268
Age, median (IQR)	65 (59, 71)	67 (59, 73)	0.183
Age, n (%)			0.376
<=65	135 (26.2%)	120 (23.3%)
> 65	127 (24.6%)	134 (26%)
T stage, n (%)			0.041
T1	91 (17.1%)	84 (15.8%)
T2	151 (28.4%)	138 (25.9%)
T3	15 (2.8%)	34 (6.4%)
T4	10 (1.9%)	9 (1.7%)
N stage, n (%)			0.508
N0	171 (32.9%)	177 (34.1%)
N1	50 (9.6%)	45 (8.7%)
N2	34 (6.6%)	40 (7.7%)
N3	2 (0.4%)	0 (0%)
M stage, n (%)			0.257
M0	180 (46.6%)	181 (46.9%)
M1	9 (2.3%)	16 (4.1%)
Pathologic stage, n (%)			0.234
Stage I	156 (29.6%)	138 (26.2%)
Stage II	57 (10.8%)	66 (12.5%)
Stage III	41 (7.8%)	43 (8.2%)
Stage IV	9 (1.7%)	17 (3.2%)
Primary therapy outcome, n (%)			0.13
PD	30 (6.7%)	41 (9.2%)
SD	22 (4.9%)	15 (3.4%)
PR	1 (0.2%)	5 (1.1%)
CR	169 (37.9%)	163 (36.5%)
Gender, n (%)			0.017
Female	157 (29.3%)	129 (24.1%)
Male	110 (20.6%)	139 (26%)
Race, n (%)			0.762
Asian	3 (0.6%)	4 (0.9%)
Black or African American	30 (6.4%)	25 (5.3%)
White	203 (43.4%)	203 (43.4%)
Residual tumor, n (%)			0.386
R0	183 (49.2%)	172 (46.2%)
R1	5 (1.3%)	8 (2.2%)
R2	1 (0.3%)	3 (0.8%)
Histological type, n (%)			0.623
Lung Acinar Adenocarcinoma	9 (1.8%)	9 (1.8%)
Lung Adenocarcinoma Mixed Subtype	58 (11.8%)	51 (10.4%)
Lung Adenocarcinoma-NOS	170 (34.6%)	170 (34.6%)
Lung Bronchioloalveolar Carcinoma Mucinous	1 (0.2%)	4 (0.8%)
Lung Bronchioloalveolar Carcinoma Nonmucinous	11 (2.2%)	8 (1.6%)
Anatomic neoplasm subdivision, n (%)			0.473
Left	98 (18.8%)	107 (20.6%)
Right	162 (31.2%)	153 (29.4%)
Anatomic neoplasm subdivision2, n (%)			0.53
Central Lung	27 (14.3%)	35 (18.5%)
Peripheral Lung	63 (33.3%)	64 (33.9%)
number_pack_years_smoked, n (%)			0.28
< 40	85 (23%)	103 (27.9%)
>=40	93 (25.2%)	88 (23.8%)
Smoker, n (%)			0.422
No	41 (7.9%)	34 (6.5%)
Yes	218 (41.8%)	228 (43.8%)
OS event, n (%)			0.063
Alive	182 (34%)	161 (30.1%)
Dead	85 (15.9%)	107 (20%)
DSS event, n (%)			0.023
Alive	202 (40.5%)	177 (35.5%)
Dead	49 (9.8%)	71 (14.2%)
PFI event, n (%)			0.271
Alive	161 (30.1%)	148 (27.7%)
Dead	106 (19.8%)	120 (22.4%)
IQR, interquartile range; NOS, Not Otherwise Specified

Table 2

Associations of KLF4 expression with clinicopathological characteristics of patients (n = 535).
Characteristics	Total(N)	Odds Ratio(OR)	P value
T stage (T3&T4 vs. T1&T2)	532	1.875 (1.117–3.209)	0.019
N stage (N2&N3 vs. N0&N1)	519	1.106 (0.679–1.806)	0.685
M stage (M1 vs. M0)	386	1.768 (0.776–4.275)	0.185
Pathologic stage (Stage III&Stage IV vs. Stage I&Stage II)	527	1.253 (0.823–1.915)	0.294
Primary therapy outcome (SD&PR&CR vs. PD)	446	0.697 (0.415–1.161)	0.168
Gender (Male vs. Female)	535	1.538 (1.094–2.167)	0.014
Race (Asian&Black or African American vs. White)	468	0.879 (0.512–1.501)	0.636
Age (> 65 vs. <=65)	516	1.187 (0.840–1.678)	0.331
Residual tumor (R1&R2 vs. R0)	372	1.951 (0.726–5.768)	0.198
Anatomic neoplasm subdivision (Right vs. Left)	520	0.865 (0.608–1.230)	0.419
Anatomic neoplasm subdivision2 (Peripheral Lung vs. Central Lung)	189	0.784 (0.423–1.441)	0.434
number_pack_years_smoked ( > = 40 vs. <40)	369	0.781 (0.518–1.175)	0.236
Smoker (Yes vs. No)	521	1.261 (0.773–2.070)	0.354

Identification of DEGs in LUAD and Protein-protein Interaction (PPI) Network Analysis

By comparing the gene expression profiles of LUAD samples with high and low KLF4 levels, we identified a total of 1582 differentially expressed genes (DEGs), consisting of 1100 (69.53%) upregulated genes and 482 (30.47%) downregulated genes (adjusted p value < 0.05, |Log2-FC| > 1) (Fig. 4A and Additional file 3). Subsequently, relationship between the top 10 DEGs (including REG4, TFF2, MUC17, ANXA10, DPPA2, UPK1B, ONECUT3, TM4SF20, MUC5AC, PSCA) and KLF4 are presented in Fig. 4B. We utilized online STRING tool to construct a PPI network that aimed to investigate the possible interactions among the top 50 DEGs. Additionally, we identified a set of hub genes from the PPI network by using the same tool. The network exhibited a high degree of complexity, and the top 10 hub genes were identified as SPRR1B, SPRR3, SPRR2A, LCE3E, CASP14, PI3, RPTN, LCE3A, SPRR2E, and TCHH (Additional file 4).

Functional Enrichment Analysis Including GO, KEGG, and GSEA Analysis

We performed GO enrichment analysis for biological processes, cellular components, and molecular functions. The results revealed that DEGs were enriched in various GO terms such as epidermis development, cornified envelope, and peptidase inhibitor activity (Fig. 4C and Additional file 5). KEGG pathway analysis showed that the significantly DEGs-enriched pathways included neuroactive ligand-receptor interaction, retinol metabolism and chemical carcinogenesis (Fig. 4D and Additional file 5).

Subsequently, GSEA analysis using GSEA/MSigDB was performed, and the results indicated significant enrichment in immune-related biological processes, including immunoregulatory interactions, humoral immune response, and immunoglobulin production. These results suggested that KLF4 may have an increased immune phenotype in LUAD (Figs. 5 and Additional file 6).

Correlation Between Methylation and Expression of KLF4

To elucidate the underlying mechanisms of KLF4 overexpression in LUAD tissues, we investigated the correlation between KLF4 expression levels and methylation status, utilizing online tools. First, we found that the promoter of KLF4 had a significantly lower level of DNA methylation in LUAD tissues than in normal lung tissues using the UALCAN database (P = 0.0208) (Fig. 6A). We found most of the methylation sites in the DNA sequences of KLF4 were hypomethylated in LUAD (Fig. 6B). Notably, the level of methylation observed at these specific sites showed a significant correlation with the outcomes of the patients. Specifically, patients who exhibited low levels of KLF4 methylation had poorer overall survival rates when compared to those with high levels of KLF4 methylation, as depicted in Figs. 6CD.

Correlation Between KLF4 Expression and Immune Infiltration

In order to elucidate the correlation between the KLF4 expression landscape and immune infiltrates in LUAD, we employed TIMER2.0 as our analytical tool. Our findings showed a positive correlation between KLF4 expression and the levels of various immune infiltrating cells, including neutrophils, macrophages, monocytes, myeloid dendritic cells, mast cells, eosinophils, hematopoietic stem cells, NK cells, and CD4 + T cells, while a negative correlation was observed with B cells and Tregs (Additional file 7). Next, through the use of single-sample GSEA, we observed a positive correlation between KLF4 expression and the levels of immune infiltrating cells, specifically neutrophils (r = 0.370, P < 0.001), mast cells (r = 0.250, P < 0.001), eosinophils (r = 0.246, P < 0.001), and NK CD56bright cells (r = 0.188, P < 0.001) (Fig. 7AB). Meanwhile, the enrichment scores obtained for these four cell types were significantly higher in the KLF4 high expression group compared to the KLF4 low expression group (all P < 0.001) (Figs. 7C). Moreover, we examined the relationship between KLF4 expression and immune biomarkers, including tumor mutational burden (TMB) and immune modulators. The result showed that TMB scores was negatively correlated with the expression of KLF4 in LUAD (r = -0.13, P = 0.003) (Figs. 8A). Additionally, we noticed that KLF4 expression positively correlated with immunoinhititors of IL10, PDCD1LG2, CD274 (Fig. 8B), and immunostimulators of IL6, C10orf54 while negatively correlated with TNFRSF18 and TNFRSF25(Figs. 8C).

Prognostic Value of KLF4 in LUAD

KLF4 expression was shown to have better predictive power in distinguishing LUAD from normal tissues, with an area under the curve (AUC) of 0.963 (95% confidence interval [CI] = 0.951–0.975) as indicated by the ROC curve (Fig. 9A). Then we used the Kaplan-Meier method to estimate the relationship between KLF4 expression and the survival of patients with LUAD. The median value of KLF4 expression was utilized to stratify patients into high and low expression groups. Compared to the low expression group, the high KLF4 expression group displayed a significantly worse prognosis for overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) (OS: hazard ratio [HR] = 1.45, 95% [CI] = 1.09–1.94, P = 0.011; DSS: HR = 1.65, 95% CI = 1.14–2.39, P = 0.008; PFI: HR = 1.36, 95% CI = 1.04–1.77,P = 0.023), as demonstrated by the corresponding Kaplan-Meier curves (Figs. 9BCD).

Next, univariate and multivariate Cox regression analyses were performed to identify prognostic factors. The multivariate analysis showed that KLF4 expression (adjusted HR = 1.867, 95% CI = 1.265–2.755, P = 0.002), T3 stage (adjusted HR = 3.487, 95% CI = 1.555–7.822, P = 0.002), and N1 stage (adjusted HR = 2.159, 95% CI = 1.035–4.503, P = 0.04) were independent factors for OS in patients with LUAD (Fig. 9E and Additional file 8). Similarly, for DSS, KLF4 expression (adjusted HR = 1.845, 95% CI = 1.086–3.133, P = 0.024), and R1&R2 tumor (adjusted HR = 3.318, 95% CI = 1.115–9.871, P = 0.031) were identified as prognostic indicators (Additional file 9). For PFI, KLF4 expression (adjusted HR = 1.547, 95% CI = 1.057–2.265, P = 0.025), T3 stage (adjusted HR = 2.584, 95% CI = 1.106–6.038, P = 0.028), and R1&R2 tumor (adjusted HR = 3.196, 95% CI = 1.420–7.196, P = 0.005) were identified as prognostic indicators (Additional file 10). Subsequently, we also assessed the prognostic value of KLF4 expression in different subgroups. The high expression of KLF4 was consistently linked to unfavorable outcomes in various subgroups based on OS, DSS, and PFI, including T2-T4 stage, N0-N2 stage, M0 stage, stages I-III, male gender, white race, and age ≤ 60 years (all P < 0.05) (Fig. 10 and Additional file 11).

Construction and Validation of a Nomogram Based on the Independent Factors

A nomogram was developed based on the independent factors of OS to estimate the prognosis of LUAD patients. On the nomogram, a higher total number of points was associated with a worse prognosis (Fig. 11A). Additionally, calibration curves were used to assess the prediction efficacy of the nomogram (Figs. 11BD). The bootstrap corrected C-index of the nomogram was 0.692 (95% CI = 0.667–0.716), indicating that the model had a moderate predictive accuracy for OS of patients with LUAD. Furthermore, the time-dependent ROC curve was applied to evaluate the discriminative ability of KLF4 expression (Additional file 12). Overall, the results suggested that the nomogram was appropriate for predicting the prognosis of LUAD patients.

Lung adenocarcinoma (LUAD) is the most prevalent lung cancer subtype while currently no molecular markers exist to predict prognosis among LUAD patients. The identification of novel biomarkers may facilitate improved clinical diagnosis and patient stratification. KLF4, a dual-function transcription factor with the ability to activate or repress transcription depending on the target gene, cell type and context, remains unclear in terms of its expression and role in LUAD. Therefore, the objective of this study is to investigate the KLF4 profile in LUAD.

KLF4 plays a crucial role in maintaining homeostasis and morphology in the lung, making it an important regulator of cell differentiation and proliferation[11, 12]. Our study investigated the expression of KLF4 in LUAD and normal lung tissue samples from both our hospital and TCGA database, revealing higher expression in normal lung tissue[13]. Additionally, our results showed that high expression of KLF4 was associated with unfavorable clinicopathologic factors such as stage II & III & IV and T3 & T4 stage, which is consistent with previous research [14]. Moreover, we found that patients with high KLF4 expression had significantly shorter OS, DSS, and PFI compared to those with low KLF4 expression (p < 0.05). Yang Cheng's study also verified KLF4 and KRT6A as prognostic risk factors (HR > 1) in LUAD[15]. Furthermore, univariate Cox analysis revealed that KLF4, TNM stage, pathologic stage, and residual tumor were associated with OS, while multivariate analysis highlighted that T3, N1, and KLF4 had independent prognostic value, suggesting KLF4's potential as a prognostic biomarker of LUAD.

Most studies have depicted KLF4 as a tumor suppressor in malignant cell types of lung cancer, particularly in NSCLC where LUAD accounts for 80% of cases. However, the oncogenic nature of KLF4 is not completely free of contradictions. For instance, KLF4 has been found to hinder the growth, invasiveness, and mobility of NSCLC cells via deactivation the MSI2/JAK2/STAT3 signaling pathway and reversing EMT and metastasis through the SIRT6/snail pathway[16, 17]. However, the role of KLF4 as a tumor suppressor or oncogene varies depending on the type of cells, context, and subcellular localization. It is noteworthy that in LUAD patients, high levels of KLF4 expression in the nucleus were linked to a negative prognosis, while elevated expression in the cytoplasm correlated with a favorable outcome[18]. Mechanically, KLF4 was able to promote tumorigenesis partly due to its activation in M2 polarization of macrophages[19]. Another study indicated KLF4 directly binds to the Rictor promoter and enhances Rictor expression, leading to transcriptional upregulation and contributing to LUAD metastasis[20]. In addition, higher expression of KLF4 in LUAD cells and tissues were found co-overexpression of c-MET. Knockdown of KLF4 has been shown to reduce tumorigenic properties and increase gefitinib sensitivity in gefitinib-resistant LUAD cells with c-Met overexpression[21]. In summary, identifying the precise roles of KLF4 in LUAD is crucially important for clinical purposes and able to guide the development of targeted treatment approaches in subsequent investigations.

Despite the findings mentioned earlier, the mechanisms that determined KLF4’s role in LUAD remains elusive, highlighting the need for additional exploration into its biological functions and signaling pathways. In our study, we identified significant enrichment in pathways related to the processes of cornification, regulation of peptidase activity, neuroactive ligand-receptor interaction, hormone metabolic processes, and immunoregulatory interactions, among others. However, experimental validation is necessary to confirm these findings and to expand our understanding of KLF4-related biological functions in LUAD.

Looking at it from a different perspective, DNA methylation is a common epigenetic mechanism that regulates gene expression[22]. Methylation of the KLF4 promoter is closely linked to various developmental processes in both normal and cancerous tissues, including the necessity of demethylation for transcriptional regulation of KLF4 during monocyte/macrophage differentiation by PU.1[23, 24]. In cervical carcinogenesis, promoter hypermethylation of KLF4 results in the inactivation of its tumor suppressor function[25]. Conversely, in prostate cancer, the silencing of LINC00673 is accomplished by decreasing KLF4 promoter methylation, which inhibits proliferation and drug resistance of cancer cells[26]. In this study, we sought to determine the DNA methylation levels of KLF4 in LUAD and its relationship with the overall survival of patients. Our findings revealed that most of the DNA sequences of KLF4 were hypomethylated in LUAD, and the degree of hypomethylation was associated with poor prognosis in patients with LUAD, specifically in KLF4. Further exploration of the mechanism underlying this association is required.

Immunotherapy has emerged as a promising cancer treatment, but its efficacy is heavily influenced by the complex tumor microenvironment, particularly immune cell infiltration. In their study, Zhang and colleagues investigated five lung adenocarcinoma (LUAD) datasets to analyze the immune infiltration landscape and identify relevant epigenetic and transcriptomic mechanisms. They discovered that seven immune cell types, including B cells, eosinophils, iDCs, mast cells, T cells, Tem, and Tfh cells, were significantly associated with LUAD prognosis and acted as risk factors[27]. Additionally, KLF4 was identified as a key regulator in M2 macrophage polarization, which is known to enhance immunity[28, 29]. Our study further revealed a high correlation between KLF4 and eosinophils, mast cells, and macrophages, suggesting that KLF4 may be a regulatory factor in the tumor immune microenvironment and may respond in LUAD. Furthermore, Zhang and colleagues found that KLF4 was one of the key genes for quantifying a scoring scheme to estimate patients' prognostic outcomes more accurately in LUAD. In this study, we sought to explore the role of KLF4 in LUAD immunotherapy by analyzing the relationship between KLF4 and immune modulators. Our results show that KLF4 is positively correlated with IL10, PDCD1LG2, CD274, IL6 and C10orf54, while negatively correlated with TNFRSF18 and TNFRSF25, indicating that KLF4 may play a role in coordinating the effect of immunotherapy in LUAD (Fig. 7C).

Although our study provides novel insights into the relationship between KLF4 expression and the prognostic significance of LUAD patients, certain limitations must be acknowledged. Firstly, our study only analyzed a single dataset, which may have resulted in a selection bias. Secondly, due to the reliance on publicly available databases for data collection, crucial clinical information such as the specific chemotherapy regimens received by patients was not obtainable. Finally, additional investigation and rigorous experimental validation utilizing both in vitro and in vivo experimental models are warranted to comprehensively unravel the biological functions and molecular mechanisms underlying KLF4 involvement in LUAD.

In summary, the present study has contributed novel insights into the role of KLF4 expression in LUAD prognosis. Our results demonstrated that high levels of KLF4 expression were an independent predictor of poor prognosis in LUAD, significantly associated with aggressive clinical characteristics and unfavorable immune infiltration. The identification of KLF4 may aid as a promising novel prognostic biomarker for predicting patient outcomes. However, further investigations are required to elucidate the underlying mechanisms by which KLF4 regulates the development and progression of LUAD, both in vitro and in vivo. Overall, the identification of KLF4 as a prognostic factor in LUAD underscores the importance of developing more effective targeted therapies for this disease and highlights the potential of KLF4 as a promising therapeutic target for improving patient outcomes.

To summarize, the study showed that a high level of KLF4 expression is an independent factor that predicts poor prognosis in LUAD. It is closely linked with aggressive clinical characteristics and unfavorable immune infiltration. These findings provide valuable insights into the role of KLF4 in the development of LUAD, and may lead to new approaches for predicting patient outcomes. Nonetheless, additional research is required to fully elucidate the mechanisms underlying the impact of KLF4 on LUAD tumorigenesis and progression.

Data Collection and Processing

We obtained mRNA expression profiles and corresponding clinical characteristics of patients with LUAD from the Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov), the Genotype Tissue Expression Project (GTEx) database (n = 288) (https://xenabrowser.net/datapages/) and the Gene Expression Omnibus database (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The level 3 HTSeq-FPKM format data were normalized as transcripts per million reads (TPM). We also obtained the RNA-sequencing data in TPM format from the UCSC Xena3 database and the GTEx database for pan-cancer analysis.

Pathological Sample Collection and Immunohistochemistry

A total of 25 paired samples of paraffin-embedded LUAD tissues and their matched para-cancerous tissues were collected between January 2020 and December 2022 at the Pathology Department of Cancer Hospital of Shantou University Medical College. Additional file 2 listed basic information (gender and age) of all these samples collected. Hematoxylin and eosin staining was performed for each malignant and non-malignant sample and reviewed by a pathologist. Tumor specimens were micro-dissected to contain at least 70% tumor cell content, while non-malignant samples were verified histologically normal. This study was approved by the Medical Ethics Committees of Cancer Hospital of Shantou university Medical College (Approval Number: 2021042) and was conducted strictly in line with the Declaration of Helsinki.

KLF4 tissues and para-cancerous tissues were fixed in 10% formalin, paraffin-embedded, sliced into 4 ~ 6 µm thick sections, and placed onto slides. The slides were deparaffinized to water. After natural cooling, the sections were placed in a 3% H2O2 solution and incubated for 10 min. After microwave antigen retrieval, the slides were incubated at 4°C overnight with anti-KLF4 rabbit monoclonal antibody (Abcam, Cat #ab215036) at 1:2000 dilution. On the next day, the slides were incubated with secondary antibody at room temperature for 20 min. After DAB developed color, the slides were rinsed with distilled water, followed by hematoxylin counterstaining.

Functional enrichment analysis of DEGs

The cohort of patients with LUAD from TCGA was stratified into high and low KLF4 expression groups based on the median KLF4 expression score. The DEG analysis between the two groups was conducted using the R package DESeq2[30, 31], with a significance threshold set at adjusted P value < 0.05, and |log2-fold-change (FC)|>1. The Spearman's correlation analysis was utilized to assess the relationship between the top 10 DEGs and KLF4 expression. Then the DEGs were resulted to establish a PPI network, utilizing the Retrieval of Interacting Genes (STRING) online database (https://www.string-db.org/), with a confidence score threshold set at > 0.7 and other parameters held at default, visualized by Cytoscape software (version 3.9.1)[32]. A plugin “CytoHubba” was utilized to identify the top 10 hub genes of these DEGs[33]. Additionally, The R packages “ggplot2(version 3.3.6)” “clusterProfiler (version 4.2.1)[34, 35]” were implemented for more functional enrichment analyses of KLF4, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and gene set enrichment analysis (GSEA). An adjusted P value < 0.05 and false discovery rate (FDR) < 0.25 were regarded as statistically significantly enriched function or pathway terms.

Immune Infiltration Analysis

In order to explore the level of immune infiltration in LUAD, we employed 24 immune cells to conduct a comprehensive analysis. The relative enrichment score of these immune cells was evaluated using the single-sample GSEA technique, accomplished by the GSVA R package[36, 37]. The correlation between KLF4 expression and these immune cells was achieved via the Spearman's correlation analysis. The Wilcoxon rank sum test was utilized to determine differences in immune infiltration levels between groups of high and low KLF4 expression. The relevance between KLF4 and immune infiltration levels was predicted via the Tumor Immune Estimation Resource 2.0 (TIMER2.0; http://timer.cistrome.org/) [38, 39]. R package “ggstatsplot” were used to evaluate the correlation between KLF4 expression and TMB enrichment score for tumor immunity. The Tumor Immune System Interacting Database (TISIDB) was used for immune modulators analysis obtained from TCGA[40].

DNA Methylation Analysis

In order to investigate the underlying mechanism of KLF4 in LUAD, we utilized the UALCAN database (http://ualcan.path.uab.edu/index.html) to examine the status of KLF4 promoter methylation[41, 42]. Furthermore, we assessed the prognostic significance of KLF4 methylation levels using MethSurv database, an online tool for conducting multivariable survival analysis based on DNA methylation data[43].

Survival Analysis

Survival analysis was conducted using the Kaplan-Meier method and the log-rank test, with the median expression level of KLF4 serving as the cut-off value. Univariate and multivariate Cox regression analyses were employed to examine the impact of clinical variables on patient outcomes. The prognostic variables exhibiting a significance level of P < 0.1 in the univariate Cox regression analysis were entered into the multivariate Cox regression analysis. Visualization of the forest map was achieved using the R package ggplot2.

Construction and Validation of the Nomogram

A nomogram was established through multivariate Cox analysis, incorporating independent prognostic factors such as clinical characteristics and risk score of patients with LUAD, to predict overall survival probability over 1-, 3-, and 5-year periods. Calibration plots were employed to evaluate the nomogram's performance, while its discriminatory ability was quantified by the concordance index (C-index). The R package RMS (version 6.2-0) was utilized to create the nomogram and calibration plots. The predictive accuracy of the model was evaluated using the time-dependent receiver-operating characteristic (ROC) curve, generated with the time ROC package.

Statistical Analysis

All statistical analyses were performed using R software (version 3.6.3). The expression of KLF4 in non-paired and paired tissues was evaluated for statistical significance using the Wilcoxon rank-sum test and paired sample t-test, respectively. The correlation between clinical features and KLF4 expression was assessed using the Wilcoxon rank-sum test and logistic regression. All tests were two-sided, and P values < 0.05, were deemed statistically significant.

Ethics approval and consent to participate

Informed consent was obtained from all the participants and/or their LAR. This study was approved by the Medical Ethics Committees of Cancer Hospital of Shantou university Medical College (Approval Number: 2021042) and was conducted strictly in line with the Declaration of Helsinki.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and/or analyzed during the current study are publicly available in the Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov), the Genotype Tissue Expression Project (GTEx) database (https://xenabrowser.net/datapages/), the Gene Expression Omnibus database (GEO) database (http://www.ncbi.nlm.nih.gov/geo/), the Retrieval of Interacting Genes (STRING) online database (https://www.string-db.org/), the Tumor Immune Estimation Resource 2.0 (TIMER2.0; http://timer.cistrome.org/), and the UALCAN database (http://ualcan.path.uab.edu/index.html).

Competing interests

There are no conflicts of interest declared by the authors.

Funding

This work was supported by the Science and Technology Special Project of Guangdong Province under Grant number 190826155555149; the Science and Technology Special Fund Project of Guangdong Province under Grant number 210728156901680; and “Dongfeng Project” for the construction of high-level hospitals in Guangdong Province - the First Affiliated Hospital of Shantou University Medical College Supporting Funding under Grant number 202003-8.

Authors’ Contribution

JXS and XFW contributed to the study conceptualization, drafted the manuscript, analyzed and interpreted all the data. JLZ conducted sample collections and immunohistochemistry for paired LUAD. XFW, MNC, DXL and WWX prepared the figures and tables. JXS reviewed and edited the manuscript. All authors have approved the final manuscript.

Acknowledgements

Not applicable.

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No competing interests reported.

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KLF4 as a Novel Prognostic Biomarker and Its Correlation with Immune Infiltrates in Lung Adenocarcinoma

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Abstract

Figures

Background

Results

Low Expression of KLF4 in LUAD

Association of KLF4 Expression Levels with the Clinical Characteristics

Identification of DEGs in LUAD and Protein-protein Interaction (PPI) Network Analysis

Functional Enrichment Analysis Including GO, KEGG, and GSEA Analysis

Correlation Between Methylation and Expression of KLF4

Correlation Between KLF4 Expression and Immune Infiltration

Prognostic Value of KLF4 in LUAD

Construction and Validation of a Nomogram Based on the Independent Factors

Discussion

Conclusions

Methods

Pathological Sample Collection and Immunohistochemistry

Functional enrichment analysis of DEGs

Immune Infiltration Analysis

DNA Methylation Analysis

Survival Analysis

Construction and Validation of the Nomogram

Statistical Analysis

Declarations

References

Additional Declarations

Supplementary Files

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