The Prognostic Significance of the Histological Types in Patients With Nonsmall Cell Lung Cancer ≤2 cm

miRNA patterns in male LUSC patients - the 3-way mirror: Tissue, plasma and exosomes

Lung cancer remains one of the leading causes of cancer-related deaths worldwide. It is classified into two main histological groups: non-small cell lung cancer (NSCLC) and small cell lung cancer. Improving the outcome of cancer patients could be possible by enhancing the early diagnosis. In the current study, we evaluated the levels of three microRNAs - miR-21-5p, miR-155-5p, and miR-181a-5p in tumor (TT) vs adjacent normal tissue (NT), as well as their expression levels in plasma and extracellular vesicles (EVs) from plasma in lung squamous cell carcinoma (LUSC) male patients vs healthy individuals as means to identify a panel of miRNAs that could serve as novel biomarkers for the diagnosis of LUSC in male patients. Matched paired tissue samples from male LUSC (n=40) patients were used for miRNA expression analysis. MiR-21-5p and miR-155-5p in tumor tissue were overexpressed, while underexpression of miR-181a-5p was observed in LUSC TT vs NT. These results were further validated in the TCGA LUSC dataset, considering 279 male samples. These alterations of miR-21-5p, miR-181a-5p, and miR-155-5p in tumor tissue are also present in plasma and plasma extracellular vesicles in LUSC male patients. In addition, ROC curves were performed to assess the sensitivity and specificity of different combinations of these miRNAs, confirming a high diagnostic accuracy for LUSC of up to 88 % in male subjects. The expression levels in tissue samples and the abundance in plasma and plasma EVs of the three miRNAs combined - miR-21-5p, miR-155-5p and miR-181a-5p - could be considered for further studies on biomarkers for the early detection of LUSC in male subjects.

Lobe-Specific Analysis of Sublobar Lung Resection for NSCLC Patients with Tumors ≤ 2 cm

Simple Summary

With the increase of the use of sublobar resection, the optimal strategy for early-stage NSCLC patients is essential. Previous studies showed tumors in different lobes are associated with different prognoses after radiotherapy and immunotherapy, inspiring us that each lobes had a different prognosis. Thus, we investigated the best surgical procedure for sublobar resection in patients with NSCLC ≤ 2 cm based on a lobe-specific analysis using propensity score matching. The results of this study will make it easier for surgeons to screen out patients with NSCLC ≤ 2 cm for segmentectomy or wedge resection, although this must be validated in larger cohorts.

Abstract

(1) Background: Sublobar resection can be used as an alternative surgical strategy for early-stage non-small-cell lung cancer (NSCLC) patients. However, the choice between wedge resection and segmentectomy remains contentious. In this study, we investigated the optimal surgical procedure for sublobar resection in patients with NSCLC ≤ 2 cm with a lobe-specific analysis; (2) Methods: Data for patients with T1N0M0 with a diameter of ≤2 cm who had undergone sublobar resection were retrieved. Propensity score matching (PSM) was used to reduce the inherent bias, and the Kaplan–Meier method and log-rank tests were used to assess the differences in survival; (3) Results: A total of 1882 patients were identified after the PSM. Patients with NSCLC ≤ 2 cm who had undergone segmentectomy showed better survival than those who had undergone wedge resection. However, when NSCLC was ≤1 cm, there was no significant difference in OS between the two groups. This demonstrated an OS advantage of segmentectomy over wedge resection for patients with NSCLC tumors of 1–2 cm (p = 0.024). Further analysis indicated that this survival benefit was only observed in patients with right upper NSCLC of 1–2 cm, but not with NSCLC in the other four lobes; (4) Conclusions: Segmentectomy showed a greater survival benefit than wedge resection only in patients with NSCLC of 1–2 cm, particularly those with primary tumors in the right upper lobe. Therefore, we propose a lobe-specific sublobar resection strategy for early-stage NSCLC patients (tumors of 1–2 cm) who cannot tolerate lobectomy.

Surgical Treatment is Still Recommended for Patients Over 75 Years with IA NSCLC: A Predictive Model Based on Surveillance, Epidemiology and End Results Database

Background

To determine the populations who suitable for surgical treatment in elderly patients (age ≥ 75 y) with IA stage.

Methods

The clinical data of NSCLC patients diagnosed from 2010 to 2015 were collected from the SEER database and divided into surgery group (SG) and no-surgery groups (NSG). The confounders were balanced and differences in survival were compared between groups using PSM (Propensity score matching, PSM). Cox regression analysis was used to screen the independent factors that affect the Cancer-specific survival (CSS). The surgery group was defined as the patients who surgery-benefit and surgery-no benefit according to the median CSS of the no-surgery group, and then randomly divided into training and validation groups. A surgical benefit prediction model was constructed in the training and validation group. Finally, the model is evaluated using a variety of methods.

Results

A total of 7297 patients were included. Before PSM (SG: n = 3630; NSG: n = 3665) and after PSM (SG: n = 1725, NSG: n = 1725) confirmed that the CSS of the surgery group was longer than the no-surgery group (before PSM: 82 vs. 31 months, P < .0001; after PSM: 55 vs. 39 months, P < .0001). Independent prognostic factors included age, gender, race, marrital, tumor grade, histology, and surgery. In the surgery cohort after PSM, 1005 patients (58.27%) who survived for more than 39 months were defined as surgery beneficiaries, and the 720 patients (41.73%) were defined surgery-no beneficiaries. The surgery group was divided into training group 1207 (70%) and validation group 518 (30%). Independent prognostic factors were used to construct a prediction model. In training group (AUC = .678) and validation group (AUC = .622). Calibration curve and decision curve prove that the model has better performance.

Conclusions

This predictive model can well identify elderly patients with stage IA NSCLC who would benefit from surgery, thus providing a basis for clinical treatment decisions.