Immunohistochemical Signatures as Predictors of Postsurgical Prognosis in Pulmonary Squamous Cell Carcinoma Patients

Background We aimed to evaluate the prognostic value of immunohistochemistry (IHC) markers and tumor-node-metastasis (TNM) stages in patients with pulmonary squamous cell carcinoma (SQCC). Methods From January 2010 to December 2014, aA total of 556 patients with SQCC who underwent radical resection were included. The patients were grouped into a discovery group (n = 334) and a validation group (n = 222). Using the least absolute shrinkage and selection operator regression model, we extracted IHCs that were associated with progression-free survival (PFS) and then built a classier. Clinicopathologic variables and the IHC-based classier were analysed using univariable and multivariable logistic regression analysis. A nomogram to predict PFS was constructed and validated using bootstrap resampling. analysis the IHC-based classiers independently of patients The


Introduction
Lung cancer is one of the leading causes of death worldwide (1,800,000 deaths per year), with its incidence increasing every year (1) (1). Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for 85%-90% of all lung cancer cases. The two major histologic types of lung cancers are squamous cell carcinoma and adenocarcinoma (2) (2). Radical resection is still the main treatment for squamous cell lung carcinoma (3) (3). Although the tumor-node-metastasis (TNM) stage can predict the prognosis of patients, the prognosis for high-risk lung cancer patients is not hopeful (4) (4). Therefore, it is necessaryThis paper aims to improve patients' 3-year postoperative prognosis progression-free survival (PFS) and 5-year PFS by integrating multiple biomarker predictive models to guide patient management after surgery.
Microscopic morphological characteristics of tumors have always been the gold standard for lung cancer classi cation by the World Health Organization. In cases of poor tumor differentiation and in the absence of lung adenocarcinoma and cytological characteristics, classi cation is more di cult. Due to the need for precise tumor typing, reliance solely on morphology for diagnosis has been challenged in recent years.
Owing to this, immunohistochemistry (IHC) detection is important in the subtyping of NSCLC. IHC is widely used because it is simple, relatively inexpensive, and highly reliable. In clinical practice, in addition to p63, p40, and CK5/6, there are many epithelial cell markers, including 34BE12, desmocollin-3, S100A2, S100A7, SOX2, glypican 3, and miR-205, which have been used to identify squamous cell carcinoma (SQCC). A growing body of evidence suggests that IHC is a highly effective aid in predicting survival in patients with various cancer types. For example, High Ki67 has a close association with poor prognosis in colorectal cancer (5)(5), A three-gene IHC panel has been reported to predict the prognosis of patients with esophageal adenocarcinoma (6)(6). Several IHC-based biomarkers have been reported as predicting various cancers (7)(7), but there are few reports pertaining to squamous cell carcinoma. The integration of multiple biomarkers into a single predictive model may enable clinicians to optimize patient treatment and reduce mortality.
The purpose of this study was to develop and validate IHC-based classi ers using the least absolute shrinkage and selection operator (LASSO) Cox regression model and to establish a prognosis for postoperative patients with pulmonary squamous cell carcinoma based on clinical-pathological parameters and IHC biomarkers.

Patients and Samples
Patients who underwent squamous cell carcinoma resection at Li Huili Hospital, a liated with Ningbo University, from Januaryune 20104 to DecemberJune 20149 with complete clinical and immunological data were included (N = 556). The study was approved by the ethics committee of Li hHuili Hospital and was conducted in accordance with the Helsinki Declaration. Informed consent was obtained from all patients prior to inclusion. Patients were randomly divided into the discovery group (n = 334) and the validation group (n = 222), a ratio of 6:4.

Immunohistochemistry
The specimens studied were formalin-xed and para n-enucleated tissue slices. Specimens were rst analyzed by the primary pathologist then independently by two practicing pathologists. Specimens were then immunogrouped by the use of antibodies at the Ningbo Pathology Center. The antibodies used were from Fu Zhou Maixin Biotech and include CK pan, CK7, TTF-1, NapsinA, CK5/6, p63, p40, CD56, Syn, CgA, and Ki-67. All the specimens were xed with 10% neutral formalin, embedded in para n, and stained with 3 μ m section HE. Immunohistochemical staining was performed with EnVision.

Development and Validation of IHC Markers
The LASSO Cox regression model was used. Signi cant prognostic markers with non-zero coe cients were determined from the test group. The prognostic score for each patient was calculated using a linear combination of the identi ed markers. In the test group, a multimarker classi er to predict 3-and 5-year PFS in SQCC patients was established. The LASSO Cox regression model analysis was performed using the "glmnet" function in R software 3.0.1 (R Foundation for Statistical Computing, Vienna, Austria).

Statistical Analysis
We compared the two data sets using the t-test for continuous variables and the chi-square test for categorical variables. The Kaplan-Meier survival analysis and log-rank were used to estimate the survival time of patients in the different risk groups strati ed according to IHC markers. A quantile plot was used to select the best cut-off point for patient survival time.
ROC curve analysis was performed, statistically signi cant prognostic markers were analyzed using univariate and multivariate Cox regressions, and Cox regression coe cients were used to build a column chart to predict PFS probability. Based on the regression analysis, a calibration plot was obtained. We evaluated the clinical utility of the charts through decision curve analysis (DCA). R's "rms" function was used for line and calibrations plots. The two-tailed p-value was 0.05.

Patients' Clinical Characteristics
All patients had undergone SQCC resection. The clinical stage of patients was determined according to the TNM staging of lung cancer in the 8th edition of the UICC (8) (8). Detailed clinical-pathological characteristics of the discovery group (n = 334) and the validation group (n = 222) are in Table 1.

Validation of Signi cance
There were signi cant differences in survival rates among patients in different risk groups. PFS was signi cantly better in the low-risk subgroups than in the high-risk subgroups in the discovery group, the validation group, and the cohort as a whole. The same analysis was carried out in the validation group (n = 222). Using the risk score, we classi ed these patients into a high-risk group (n = 132, 59.5%) and a lowrisk group (n = 90, 40.5%). The ve-year PFS was () for the high-risk group and () for the low-risk group. Similar differences between the two groups were noted in the combined discovery and validation cohort (Fig. 32).

Prediction Accuracy
Pleural invasion, staging, and IHC markers were signi cant prognostic factors in the univariate analysis. There was no statistically signi cant difference in other clinical-pathological factors. The multivariate analysis identi ed tumor-stage markers and IHC markers to be independent PFS predictors. Furthermore, combining IHC-based classi ers with staging provided better predictive value than IHC-based classi ers or staging alone. Therefore, IHC-based classi ers can increase the prognostic value of staging in postresection SQCC patients ( Fig. 43 and Fig. 54).

Nomogram and clinical application
To provide a clinically relevant tool for predicting prognosis, we built a nomogram that integrates a variety of clinical-pathological risk factors based on IHC markers and PFS. Predictive factors include pleural invasion, tumor stage, and IHC markers. The calibration curve showed optimal performance of the nomogram with a high degree of consistency between the PFS and the Kaplan-Meier estimates. (Fig. 65).
DCA was used to evaluate the clinical utility of a nomogram based on IHC markers by quantifying net bene ts. The threshold probability of a patient choosing treatment tells us how the patient weighs the relative harm of false positive and false negative predictions. Here, the relative harm from treatment is equal to that of avoiding the expected bene ts of treatment. Net bene t was calculated by subtracting the proportion of false positives from the sum of false and true positives. The nomogram proved to be of clinical value as it ensured a better net bene t in comparison to full or no treatment options . (Fig. 7 and

Discussion
Surgical resection is the most effective treatment for lung cancer, but predicting postoperative patients' likelihood of recurrence or PFS is di cult (9)(9). The current method does not provide viable clinical prognostic markers in postoperative patients with squamous cell lung carcinoma (10)(10). To help guide decision-making in the management of SQCC patients, we used a group of SQCC patients to analyze IHC markers that were associated with prognosis and developed a nomogram to estimate 3-and 5-year PFS in postoperative patients.
A LASSO Cox regression was used to identify statistically signi cant prognostic factors; this regression builds models based on both predictors and selected characteristics. In a recent study, the LASSO Cox regression method was used in multivariate analysis panels such as preoperative prediction of lymph node metastasis in colorectal cancer (11) (11). Our study used the LASSO method to reduce the regression coe cient and reduce 19 characteristics to 4 potential predictors. Patients were divided into low-risk and high-risk groups by incorporating 4 IHC markers into the classi er. The survival rate of patients in the low-risk group was signi cantly higher than that in the high-risk group. In addition, we validated the potential value of IHC markers in predicting patient prognosis using the validation group. Multivariate analysis showed that IHC markers are independent prognostic factors of PFS after adjustment is made for clinical-pathological variables. Combined with staging, IHC markers provide better predictive value than staging alone. IHC and clinical-pathological variables including invasion status and staging were included in the nomogram. The calibration graph showed signi cant correlation between the predicted survival probability and the observed survival rate. DCA showed great potential for clinical applicability of the nomogram.
IHC technology has advanced from traditional cell microscopic examination technology. It provides a better understanding of enzyme activity and the shape and structure of the patient's cells, as well as a more accurate understanding of the histopathology of different lesions (12) (12). It is highly accurate and speci c. SOX2 is an important member of the SOX family, which regulates the development of embryos and tissues by binding to the HMG domain of the target gene, and it maintains the pluripotent, undifferentiated state of the stem cells, regulates their proliferation, and determines their cell fate (13) (13). Numerous studies have found SOX2 expression abnormalities in many solid tumors, such as breast cancer and malignant gliomas (14) (14). One study has shown that ampli cation of SOX2 loci can be detected in non-small cell lung cancer, which is associated with median overall survival in early patients with the disease but not in patients with advanced tumors (15) (15). Another study showed that the prognosis of tumor patients with a high expression of SOX2 was signi cantly better than that of patients with a low expression (16) (16). Interestingly, Chou and his colleagues reported that the expression of SOX2 was associated with the staging of NSCLC (17) (17). As for lymph node metastasis and TNM stage, the lower the degree of tumor differentiation, the earlier the metastasis to the lymph nodes; the later the TNM stage, the higher the expression rate of SOX2 (18) (18). This suggests that the expression of SOX2 may be closely related to the proliferation and metastasis process of NSCLC and a relevant factor in poor tumor prognosis. Therefore, it is proposed that SOX2 expression can be used to predict NSCLC.
The human p63 gene, located on chromosomes 3q27-3q29, consists of 15 exons that each consist of two independent initiators. At present, the p63 (monoclonal antibody cloning system 4A4) used in domestic and foreign pathology laboratories can identify TAp63 and xenon Np63, and it is considered to be a "broad-spectrum" p63 antibody, while the p40 antibody Np63 only recognizes p-Np63 (19) (19). The protein p63 is often expressed at the base of the epithelial tissue, playing an important role in the formation of normal endothelial tissue, and it is expressed in a variety of malignant tumors of squamous cell origin, especially in squamous cell carcinoma tissue. Its frequency and expression distribution are related to the degree of SQCC differentiation (20) (20). In breast cancer, the expression of p63 is demonstrated to be related to tumor grading and is signi cant for the prognosis and treatment index of tumors (21)(21).
The protein p27Kip1 is a cell cycle inhibitor with a relative molecular mass of 27,000 discovered in a 1994 study of cell suppression mechanisms. Studies have found that p27Kip1 is reduced or missing in a variety of malignant tumor tissues, including esophageal, colon, prostate, bladder, liver, breast, and nonsmall cell lung cancer (22-28) (22)(23)(24)(25)(26)(27)(28). The expression of p27 as identi ed via RT-PCR was signi cantly lower than that in adjacent paracancerous tissues in 230 cases of NSCLC. There were signi cant differences (p < 0.050) in lymph node metastasis, TNM staging, and differentiation classi cation (29) (29).
VEGF is the most consequential cytokine known to promote the growth of the endothelium of blood vessels. It can increase vascular permeability, promote tumor vascular formation, and play an important role in the proliferation and migration of tumor vascular endothelial cells. In vitro animal model experiments have shown that inhibition of the expression of VEGF in tumors can signi cantly inhibit the formation of blood vessels in lung cancer cell tissue, reduce tumor volume, and improve the survival time in mice (28)

Authors' contributions
Chengbin Lin, Weiyu Shen, Yong Xi participated in the study design, and Hongyan Yu, Xiaohan Chen statistically analyzed the data. The manuscript was drafted by Chengbin Lin and revised by Yong Xi and Feng Weiyu Shen. The authors read and approved the nal manuscript.

Availability of data and materials
The datasets supporting the conclusion of this article are included within the article.

Declarations
Ethics approval and consent to participate Not applicable