Nomograms for Predicting Overall and Recurrence-free Survival From Pathologic Stage IA and IB Lung Cancer After Lobectomy

High-risk features associated with recurrence in stage I lung adenocarcinoma

OBJECTIVE

There is a lack of knowledge regarding the use of prognostic features in stage I lung adenocarcinoma (LUAD). Thus, we investigated clinicopathologic features associated with recurrence after complete resection for stage I LUAD.

METHODS

We performed a retrospective analysis of patients with pathologic stage I LUAD who underwent R0 resection from 2010 to 2020. Exclusion criteria included history of lung cancer, induction or adjuvant therapy, noninvasive or mucinous LUAD, and death within 90 days of surgery. Fine and Gray competing-risk regression assessed associations between clinicopathologic features and disease recurrence.

RESULTS

In total, 1912 patients met inclusion criteria. Most patients (1565 [82%]) had stage IA LUAD, and 250 developed recurrence: 141 (56%) distant and 109 (44%) locoregional only. The 5-year cumulative incidence of recurrence was 12% (95% CI, 11%-14%). Higher maximum standardized uptake value of the primary tumor (hazard ratio [HR], 1.04), sublobar resection (HR, 2.04), higher International Association for the Study of Lung Cancer grade (HR, 5.32 [grade 2]; HR, 7.93 [grade 3]), lymphovascular invasion (HR, 1.70), visceral pleural invasion (HR, 1.54), and tumor size (HR, 1.30) were independently associated with a hazard of recurrence. Tumors with 3 to 4 high-risk features had a higher cumulative incidence of recurrence at 5 years than tumors without these features (30% vs 4%; P < .001).

CONCLUSIONS

Recurrence after resection for stage I LUAD remains an issue for select patients. Commonly reported clinicopathologic features can be used to define patients at high risk of recurrence and should be considered when assessing the prognosis of patients with stage I disease.

Optical Nanobiosensor Based on Surface-Enhanced Raman Spectroscopy and Catalytic Hairpin Assembly for Early-Stage Lung Cancer Detection via Blood Circular RNA

Lung cancer has become the leading cause of cancer-related deaths globally. However, early detection of lung cancer remains challenging, resulting in poor outcomes for the patients. Herein, we developed an optical biosensor integrating surface-enhanced Raman spectroscopy (SERS) with a catalyzed hairpin assembly (CHA) to detect circular RNA (circRNA) associated with tumor formation and progression (circSATB2). The signals of the Raman reporter were considerably enhanced by generating abundant SERS "hot spots" with a core-shell nanoprobe and 2D SERS substrate with calibration capabilities. This approach enabled the sensitive (limit of detection: 0.766 fM) and reliable quantitative detection of the target circRNA. Further, we used the developed biosensor to detect the circRNA in human serum samples, revealing that patients with lung cancer had higher circRNA concentrations than healthy subjects. Moreover, we characterized the unique circRNA concentration profiles of the early stages (IA and IB) and subtypes (IA1, IA2, and IA3) of lung cancer. These results demonstrate the potential of the proposed optical sensing nanoplatform as a liquid biopsy and prognostic tool for the early screening of lung cancer.

Factors influencing postoperative recurrence of early-stage non-small cell lung cancer

PURPOSE

This study aims to explain the factors that may influence recurrence after surgical resection for early non-small cell lung cancer (NSCLC).

METHODS

A retrospective analysis was made of 302 patients who underwent lung resection for stage I-IIA NSCLC in our clinic between January 2014 and August 2021.

RESULTS

The recurrence rate was higher in patients with squamous cell carcinoma (SCC) than in those with adenocarcinoma (AC) (p = 0.004). Disease-free survival (DFS) was shorter in SCC (p = 0.004). According to histopathological subtypes, the presence of lymphovascular invasion (LVI), vascular invasion (VI), visceral pleural invasion (VPI) and tumor spread through air spaces (STAS) caused an increased risk of recurrence ((p = 0.004), (p = 0.001), (p = 0.047), (p = < 0.001)) and shorter DFS ((p = 0.002), (p = < 0.001), (p = 0.038), (p = < 0.001)). LVI and VI was more common in patients with distant recurrence (p = 0.020, p = 0.002), while the STAS was more common with locoregional recurrence (p = 0.003).

CONCLUSION

The presence of LVI, VI, VPI, and STAS are negative risk factors for recurrence and DFS in all patients and in patients with AC. In patients with SCC, the diagnosis of SCC itself and the presence of STAS were risk factors for recurrence and DFS. Moreover, the risk of distant recurrence is higher in the presence of LVI or VI, and the risk of locoregional recurrence in the presence of STAS is higher.

Predictors of disease recurrence after radical resection and adjuvant chemotherapy in patients with stage IIb-IIIa squamous cell lung cancer: A retrospective analysis

BACKGROUND

Lung cancer (LC) is a global medical, social and economic problem and is one of the most common cancers and the leading cause of mortality from malignant neoplasms. LC is characterized by an aggressive course, and in the presence of disease recurrence risk factors, patients, even at an early stage, may be indicated for adjuvant therapy to improve survival. However, combined treatment does not always guarantee a favorable prognosis. In this regard, establishing predictors of LC recurrence is highly important both for determining the optimal treatment plan for the patients and for evaluating its effectiveness.

AIM

To establish predictors of disease recurrence after radical resection and adjuvant chemotherapy in patients with stage IIb-IIIa lung squamous cell carcinoma (LSCC).

METHODS

A retrospective case-control cohort study included 69 patients with LSCC who underwent radical surgery at the Orenburg Regional Clinical Oncology Center from 2009 to 2018. Postoperatively, all patients received adjuvant chemotherapy. Histological samples of the resected lung were stained with Mayer's hematoxylin and eosin and examined under a light microscope. Univariate and multivariate analyses were used to identify predictors associated with the risk of disease recurrence. Receiver operating characteristic curves were constructed to discriminate between patients with a high risk of disease recurrence and those with a low risk of disease recurrence. Survival was analyzed using the Kaplan-Meier method. The log-rank test was used to compare survival curves between patient subgroups. Differences were considered to be significant at P < 0.05.

RESULTS

The following predictors of a high risk of disease recurrence in patients with stage IIb-IIa LSCC were established: a low degree of tumor differentiation [odds ratio (OR) = 7.94, 95%CI = 1.08-135.81, P = 0.049]; metastases in regional lymph nodes (OR = 5.67, 95%CI = 1.09-36.54, P = 0.048); the presence of loose, fine-fiber connective tissue in the tumor stroma (OR = 21.70, 95%CI = 4.27-110.38, P = 0.0002); and fragmentation of the tumor solid component (OR = 2.53, 95%CI = 1.01-12.23, P = 0.049). The area under the curve of the predictive model was 0.846 (95%CI = 0.73-0.96, P < 0.0001). The sensitivity, accuracy and specificity of the method were 91.8%, 86.9% and 75.0%, respectively. In the group of patients with a low risk of LSCC recurrence, the 1-, 2- and 5-year disease-free survival (DFS) rates were 84.2%, 84.2% and 75.8%, respectively, while in the group with a high risk of LSCC recurrence the DFS rates were 71.7%, 40.1% and 8.2%, respectively (P < 0.00001). Accordingly, in the first group of patients, the 1-, 2- and 5-year overall survival (OS) rates were 94.7%, 82.5% and 82.5%, respectively, while in the second group of patients, the OS rates were 89.8%, 80.1% and 10.3%, respectively (P < 0.00001).

CONCLUSION

The developed method allows us to identify a group of patients at high risk of disease recurrence and to adjust to ongoing treatment.

Estimating the cure proportion of stage IA lung adenocarcinoma: a population-based study

BACKGROUND

We aimed to investigate the factors influencing the cure, recurrence, and metastasis rates of stage IA lung adenocarcinoma, using a mixed cure model.

METHODS

A total of 1,064 patients who underwent video-assisted thoracoscopic pulmonectomy were included. Variable screening was performed using the random forest algorithm and least absolute shrinkage and selection operator approaches. The mixed cure model was used to identify factors affecting patient cure and survival, and a sequential analysis was performed on 5%, 10%, and 20% of the presentational subtype concurrently. A receiver operating characteristics curve was used to determine the best model and construct a nomogram to predict the cure rate.

RESULTS

The median follow-up time was 58 (range: 3-115) months. Results from the cure part of the mixed model indicated that the predominant subtype, presentational subtype, and tumor diameter were the main prognostic factors affecting cure rate. Therefore, the nomogram to predict the cure rate was constructed based on these factors. The survival part indicated that the predominant subtype was the only factor that influenced recurrence and metastasis. A sequential analysis of the presentational subtype showed it had no significant effect on survival (P > 0.05). Regardless of the recording mode, no significant improvement was observed in the model's discriminative ability. Only a few postoperative pathological specimens showed lymphovascular invasion (LVI); however, the survival curve suggested a significant effect on patient survival.

CONCLUSIONS

After excluding the existence of long-term survivors, the predominant tumor subtype was determined to be the only factor influencing recurrence and metastasis. Although LVI is rare in stage IA lung adenocarcinoma, its significance cannot be discounted in terms of determining patient prognosis.

Development and validation of a nomogram for predicting pulmonary complications after video-assisted thoracoscopic surgery in elderly patients with lung cancer

Background

Postoperative pulmonary complications (PPCs) significantly increase the morbidity and mortality in elderly patients with lung cancer. Considering the adverse effects of PPCs, we aimed to derive and validate a nomogram to predict pulmonary complications after video-assisted thoracoscopic surgery in elderly patients with lung cancer and to assist surgeons in optimizing patient-centered treatment plans.

Methods

The study enrolled 854 eligible elderly patients with lung cancer who underwent sub-lobectomy or lobectomy. A clinical prediction model for the probability of PPCs was developed using univariate and multivariate analyses. Furthermore, data from one center were used to derive the model, and data from another were used for external validation. The model's discriminatory capability, predictive accuracy, and clinical usefulness were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis, respectively.

Results

Among the eligible elderly patients with lung cancer, 214 (25.06%) developed pulmonary complications after video-assisted thoracoscopic surgery. Age, chronic obstructive pulmonary disease, surgical procedure, operative time, forced expiratory volume in one second, and the carbon monoxide diffusing capacity of the lung were independent predictors of PPCs and were included in the final model. The areas under the ROC curves (AUC) of the training and validation sets were 0.844 and 0.796, respectively. Ten-fold cross-validation was used to evaluate the generalizability of the predictive model, with an average AUC value of 0.839. The calibration curve showed good consistency between the observed and predicted probabilities. The proposed nomogram showed good net benefit with a relatively wide range of threshold probabilities.

Conclusion

A nomogram for elderly patients with lung cancer can be derived using preoperative and intraoperative variables. Our model can also be accessed using the online web server https://pulmonary-disease-predictor.shinyapps.io/dynnomapp/. Combining both may help surgeons as a clinically easy-to-use tool for minimizing the prevalence of pulmonary complications after lung resection in elderly patients.

A Nomogram Based on Consolidation Tumor Ratio Combined with Solid or Micropapillary Patterns for Postoperative Recurrence in Pathological Stage IA Lung Adenocarcinoma

BACKGROUND

Patients with pathological stage IA lung adenocarcinoma (LUAD) are at risk of relapse. The value of the TNM staging system is limited in predicting recurrence. Our study aimed to develop a precise recurrence prediction model for stage IA LUAD.

MATERIALS AND METHODS

Patients with pathological stage IA LUAD who received surgical treatment at Zhongshan Hospital Fudan University were retrospectively analyzed. Multivariate Cox proportional hazards regression models were used to create nomograms for recurrence-free survival (RFS). The predictive performance of the model was assessed using calibration plots and the concordance index (C-index).

RESULTS

The multivariate Cox regression analysis revealed that CTR (0.75 < CTR ≤ 1; HR = 9.882, 95% CI: 2.036-47.959, p = 0.004) and solid/micropapillary-predominance (SMPP; >5% and the most dominant) (HR = 4.743, 95% CI: 1.506-14.933, p = 0.008) were independent prognostic factors of RFS. These risk factors were used to construct a nomogram to predict postoperative recurrence in these patients. The C-index of the nomogram for predicting RFS was higher than that of the eighth T-stage system (0.873 for the nomogram and 0.643 for the eighth T stage). The nomogram also achieved good predictive performance for RFS with a well-fitted calibration curve.

CONCLUSIONS

We developed and validated a nomogram based on CTR and SMP patterns for predicting postoperative recurrence in pathological stage IA LUAD. This model is simple to operate and has better predictive performance than the eighth T stage system, making it suitable for selecting further adjuvant treatment and follow-up.

An eleven-gene risk model associated with lymph node metastasis predicts overall survival in lung adenocarcinoma

Lung adenocarcinoma (LUAD) occupies major causes of tumor death. Identifying potential prognostic risk genes is crucial to predict the overall survival of patients with LUAD. In this study, we constructed and proved an 11-gene risk signature. This prognostic signature divided LUAD patients into low- and high-risk groups. The model outperformed in prognostic accuracy at varying follow-up times (AUC for 3 years: 0.699, 5 years: 0.713, and 7 years: 0.716). Two GEO datasets also indicate the great accuracy of the risk signature (AUC = 782 and 771, respectively). Multivariate analysis identified 4 independent risk factors including stage N (HR 1.320, 95% CI 1.102-1.581, P = 0.003), stage T (HR 3.159, 95% CI 1.920-3.959, P < 0.001), tumor status (HR 5.688, 95% CI 3.883-8.334, P < 0.001), and the 11-gene risk model (HR 2.823, 95% CI 1.928-4.133, P < 0.001). The performance of the nomogram was good in the TCGA database (AUC = 0.806, 0.798, and 0.818 for 3-, 5- and 7-year survival). The subgroup analysis in different age, gender, tumor status, clinical stage, and recurrence stratifications indicated that the accuracy was high in different subgroups (all P < 0.05). Briefly, our work established an 11-gene risk model and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of LUAD patients for clinicians.

Preoperative prognostic prediction for stage I lung adenocarcinomas: Impact of the computed tomography features associated with the new histological grading system

Objectives

This study aimed to identify the computed tomography (CT) features associated with the new International Association for the Study of Lung Cancer (IASLC) three-tiered grading system to improve the preoperative prediction of disease-free survival of stage I lung adenocarcinoma patients.

Methods

The study included 379 patients. Ordinal logistic regression analysis was used to identify the independent predictors of IASLC grades. The first multivariate Cox regression model (Model 1) was based on the significant factors from the univariate analysis. The second multivariate model (Model 2) excluded the histologic grade and based only on preoperative factors.

Results

Larger consolidation tumor ratio (OR=2.15, P<.001), whole tumor size (OR=1.74, P=.002), and higher CT value (OR=3.77, P=.001) were independent predictors of higher IASLC grade. Sixty patients experienced recurrences after 70.4 months of follow-up. Model 1 consisted of age (HR:1.05, P=.003), clinical T stage (HR:2.32, P<.001), histologic grade (HR:4.31, P<.001), and burrs sign (HR:5.96, P<.001). Model 2 consisted of age (HR,1.04; P=.015), clinical T stage (HR:2.49, P<.001), consolidation tumor ratio (HR:2.49, P=.016), whole tumor size (HR:2.81, P=.022), and the burrs sign (HR:4.55, P=.002). Model 1 had the best prognostic predictive performance, followed by Model 2, clinical T stage, and histologic grade.

Conclusion

CTR (cut-off values of <25% and ≥75%) and whole tumor size (cut-off value of 17 mm) could stratify patients into different prognosis and be used as preoperative surrogates for the IASLC grading system. Integrating these CT features with clinical T staging can improve the preoperative prognostic prediction for stage I lung adenocarcinoma patients.

Development and validation of a nomogram for predicting survival time and making treatment decisions for clinical stage IA NSCLC based on the SEER database

Background

The aim of this study was to establish and validate a nomogram model for accurate prediction of patients' survival with T1aN0M0 none small cell lung cancer (NSCLC).

Methods

The patients, diagnosed with the stage IA NSCLC from 2004-2015, were identified from the Surveillance, Epidemiology and End Results (SEER) database. The variables with a P-value < 0.05 in a multivariate Cox regression were selected to establish the nomogram. The discriminative ability of the model was evaluated by the concordance index (C-index). The proximity of the nomogram prediction to the actual risk was depicted by a calibration plot. The clinical usefulness was estimated by the decision curve analysis (DCA). Survival curves were made with Kaplan-Meier method and compared by Log-Rank test.

Results

Eight variables, including treatment, age, sex, race, marriage, tumor size, histology, and grade were selected to develop the nomogram model by univariate and multivariate cox regression. The C-index was 0.704 (95% CI, 0.694-0.714) in the training set and 0.713 (95% CI, 0.697-0.728) in the test set, which performed significantly better than 8th edition AJCC TNM stage system (0.550, 95% CI, 0.408-0.683, P < 0.001). The calibration curve showed that the prediction ability of 3-years and 5-years survival rate demonstrated a high degree of agreement between the nomogram model and the actual observation. The DCA curves also proved that the nomogram-assisted decisions could improve patient outcomes.

Conclusion

We established and validated a prognostic nomogram to predict 3-years and 5-years overall survival in stage IA NSCLC.

Role of 18F-FDG PET/CT in the postoperative follow-up in patients with stage I-III NSCLC: A retrospective single-institution study

BACKGROUND

The optimal surveillance strategy in patients with resected non-small cell lung cancer (NSCLC) is unknown. Early detection of recurrences by follow-up imaging might improve survival and whole-body 18F-FDG-PET/CT might be the optimal imaging modality given its high accuracy in preoperative staging.

MATERIAL AND METHODS

Data from a single-center cohort of 205 patients with resected stage I-III NSCLC and FDG-PET/CT surveillance was retrospectively collected. Patients had preoperative FDG-positive tumors and FDG-PET/CT at 6, 12, 24 months, chest CT at 18 months. Thereafter, annual chest CT was performed for stage I-II, annual FDG-PET/CT for stage III.

RESULTS

With a median follow-up of 26.3 months (range, 4.1-60.6), the rate for recurrence and secondary primary lung cancer (SPLC) was 22 % and 8 %, respectively. Associated symptoms were present in 48 % (recurrence) and 18 % (SPLC) of patients. Overall, 83 % of recurrences, and 65 % of SPLC were detected on FDG-PET/CT. 82 % of recurrences were detected in one of the first two follow-up PET/CT scans. Second curatively intended treatment (SCIT) was possible in 37 % of patients with recurrence and 100 % with SPLC. The 2-year recurrence-free survival rate after SCIT for recurrence was 53 % [95 %CI; 31-91 %]. Non-malignant FDG-positive findings occurred in 25 % of patients (71 % possible infections).

CONCLUSION

In our cohort of patients, more than 80% of all recurrences were identified in one of the three FDG-PET/CTs performed as part of our imaging protocol during the first two years after resection. Nearly all patients with non-distant recurrence qualified for a SCIT. Further studies are needed to identify patients who might benefit from an even more intensive surveillance strategy.

Effect of Adjuvant Chemotherapy on Survival of Patients With 8th Edition Stage IB Non-Small Cell Lung Cancer

BACKGROUND

The efficacy of adjuvant chemotherapy in patients with 8th edition stage IB (tumor size ≤4 cm) non-small cell lung cancer (NSCLC) remains unclear.

METHODS

We identified 9757 eligible patients (non-chemotherapy group: n=8303; chemotherapy group: n=1454) between 2004 and 2016 from the Surveillance, Epidemiology and End Results (SEER) database. Log-rank test was used to compare overall survival (OS) between the chemotherapy and non-chemotherapy groups. Cox regression model was applied to investigate the independent prognosis factors of all surgically treated stage IB patients, and then the nomogram was constructed. Propensity score matching (PSM) was performed to reduce the confounding bias, and subgroup analyses of the matched cohort were also performed. Finally, we reviewed 184 patients with stage IB NSCLC from July 2008 to December 2016 in Jinling Hospital as a validation cohort, and compared disease-free survival (DFS) and OS between the two groups.

RESULTS

In the SEER database cohort, adjuvant chemotherapy was associated with improved OS in both unmatched and matched (1417 pairs) cohorts (all P <0.05). The survival benefit (both OS and DFS) was confirmed in the validation cohort (P <0.05). Multivariate analysis showed age, race, sex, marital status, histology, tumor location, tumor size, differentiation, surgical method, lymph nodes (LNs) examined, radiotherapy and chemotherapy were prognostic factors for resected stage IB NSCLC (all P <0.05). The concordance index and calibration curves demonstrated good prediction effect. Subgroup analyses showed patients with the following characteristics benefited from chemotherapy: old age, poor differentiation to undifferentiation, 0-15 LNs examined, visceral pleural invasion (VPI), lobectomy and no radiotherapy (all P <0.05).

CONCLUSIONS

Adjuvant chemotherapy is associated with improved survival in 8th edition stage IB NSCLC patients, especially in those with old age, poorly differentiated to undifferentiated tumors, 0-15 LNs examined, VPI, lobotomy and no radiotherapy. Further prospective trials are needed to confirm these conclusions. Besides, the nomogram provides relatively accurate prediction for the prognosis of resected stage IB NSCLC patients.

Prognostic Nomograms Based on Ground Glass Opacity and Subtype of Lung Adenocarcinoma for Patients with Pathological Stage IA Lung Adenocarcinoma

The value of lung adenocarcinoma (LUAD) subtypes and ground glass opacity (GGO) in pathological stage IA invasive adenocarcinoma (IAC) has been poorly understood, and reports of their association with each other have been limited. In the current study, we retrospectively reviewed 484 patients with pathological stage IA invasive adenocarcinoma (IAC) at Sun Yat-sen University Cancer Center from March 2011 to August 2018. Patients with at least 5% solid or micropapillary presence were categorized as high-risk subtypes. Independent indicators for disease-free survival (DFS) and overall survival (OS) were identified by multivariate Cox regression analysis. Based on these indicators, we developed prognostic nomograms of OS and DFS. The predictive performance of the two nomograms were assessed by calibration plots. A total of 412 patients were recognized as having the low-risk subtype, and 359 patients had a GGO. Patients with the low-risk subtype had a high rate of GGO nodules (p < 0.001). Multivariate Cox regression analysis showed that the high-risk subtype and GGO components were independent prognostic factors for OS (LUAD subtype: p = 0.002; HR 3.624; 95% CI 1.263–10.397; GGO component: p = 0.001; HR 3.186; 95% CI 1.155–8.792) and DFS (LUAD subtype: p = 0.001; HR 2.284; 95% CI 1.448–5.509; GGO component: p = 0.003; HR 1.877; 95% CI 1.013–3.476). The C-indices of the nomogram based on the LUAD subtype and GGO components to predict OS and DFS were 0.866 (95% CI 0.841–0.891) and 0.667 (95% CI 0.586–0.748), respectively. Therefore, the high-risk subtype and GGO components were potential prognostic biomarkers for patients with stage IA IAC, and prognostic models based on these indicators showed good predictive performance and satisfactory agreement between observational and predicted survival.

Prognostic Value of Inflammatory Biomarkers in Patients With Stage I Lung Adenocarcinoma Treated With Surgical Dissection

Objective

Inflammation plays a crucial role in tumorigenesis and progression. Our purpose was to investigate the prognostic value of neutrophil-to-lymphocyte ratio (NLR), systemic inflammation response index (SIRI) and systemic immune-inflammation index (SII), and develop a nomogram to predict the cancer-specific survival (CSS) and disease-free survival (DFS) of stage I lung adenocarcinoma patients.

Methods

1431 patients undergoing surgical resection with pathologically confirmed stage I lung adenocarcinoma were reviewed. The optimal cut-off values for NLR, SII, and SIRI were defined by the receiver operating characteristic (ROC) curve. Cox proportional hazards regression analyses were performed to recognize factors significantly correlated with CSS and DFS to construct the nomogram. The value of adjuvant chemotherapy on model-defined high-risk and low-risk patients was further explored.

Results

The cohort had a median follow-up time of 63 months. Multivariate analysis revealed that higher NLR (≥2.606), higher SIRI (≥0.705), higher SII (≥580.671), later T stage, histological pattern with solid or micropapillary components and radiologic features with solid nodules were significantly associated with worse CSS and DFS. The concordance index (C-index) of the nomogram established by all these factors was higher than that of the TNM staging system both in CSS (validation set 0.778 vs 0.652) and DFS (validation set 0.758 vs 0.695). Furthermore, the value of the established nomogram on risk stratification in stage I lung adenocarcinoma patients was validated.

Conclusions

Higher NLR, SII and SIRI pretreatment were associated with worse survival outcomes. A practical nomogram based on these three inflammatory biomarkers may help clinicians to precisely stratify stage I lung adenocarcinoma patients into high- and low-risk and implement individualized treatment.

Signature identification of relapse-related overall survival of early lung adenocarcinoma after radical surgery

Background

The widespread use of low-dose chest CT screening has improved the detection of early lung adenocarcinoma. Radical surgery is the best treatment strategy for patients with early lung adenocarcinoma; however, some patients present with postoperative recurrence and poor prognosis. Through this study, we hope to establish a model that can identify patients that are prone to recurrence and have poor prognosis after surgery for early lung adenocarcinoma.

Materials and Methods

We screened prognostic and relapse-related genes using The Cancer Genome Atlas (TCGA) database and the GSE50081 dataset from the Gene Expression Omnibus (GEO) database. The GSE30219 dataset was used to further screen target genes and construct a risk prognosis signature. Time-dependent ROC analysis, calibration degree analysis, and DCA were used to evaluate the reliability of the model. We validated the TCGA dataset, GSE50081, and GSE30219 internally. External validation was conducted in the GSE31210 dataset.

Results

A novel four-gene signature (INPP5B, FOSL2, CDCA3, RASAL2) was established to predict relapse-related survival outcomes in patients with early lung adenocarcinoma after surgery. The discovery of these genes may reveal the molecular mechanism of recurrence and poor prognosis of early lung adenocarcinoma. In addition, ROC analysis, calibration analysis and DCA were used to verify the genetic signature internally and externally. Our results showed that our gene signature had a good predictive ability for recurrence and prognosis.

Conclusions

We established a four-gene signature and predictive model to predict the recurrence and corresponding survival rates in patients with early lung adenocarcinoma after surgery. These may be helpful for reforumulating post-operative consolidation treatment strategies.