Peripheral Blood Lymphocytes and Platelets Are Prognostic in Surgical pT1 Non-Small Cell Lung Cancer

A Review of Biomarkers and Their Clinical Impact in Resected Early-Stage Non-Small-Cell Lung Cancer

The postoperative survival of early-stage non-small-cell lung cancer (NSCLC) patients remains unsatisfactory. In this review, we examined the relevant literature to ascertain the prognostic effect of related indicators on early-stage NSCLC. The prognostic effects of the epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), mesenchymal-epithelial transition (MET), C-ros oncogene 1 (ROS1), or tumour protein p53 (TP53) alterations in resected NSCLC remains debatable. Kirsten rat sarcoma viral oncogene homologue (KRAS) alterations indicate unfavourable outcomes in early-stage NSCLC. Meanwhile, adjuvant or neoadjuvant EGFR-targeted agents can substantially improve prognosis in early-stage NSCLC with EGFR alterations. Based on the summary of current studies, resected NSCLC patients with overexpression of programmed death-ligand 1 (PD-L1) had worsening survival. Conversely, PD-L1 or PD-1 inhibitors can substantially improve patient survival. Considering blood biomarkers, perioperative peripheral venous circulating tumour cells (CTCs) and pulmonary venous CTCs predicted unfavourable prognoses and led to distant metastases. Similarly, patients with detectable perioperative circulating tumour DNA (ctDNA) also had reduced survival. Moreover, patients with perioperatively elevated carcinoembryonic antigen (CEA) in the circulation predicted significantly worse survival outcomes. In the future, we will incorporate mutated genes, immune checkpoints, and blood-based biomarkers by applying artificial intelligence (AI) to construct prognostic models that predict patient survival accurately and guide individualised treatment.

Blood-Derived Systemic Inflammation Markers and Risk of Nodal Failure in Stage Ia Non-Small Cell Lung Cancer: A Multicentric Study

BACKGROUND

Unexpected spread to regional lymph nodes can be found in up to 10% of patients with early stage non-small cell lung cancer (NSCLC), thereby affecting both prognosis and treatment. Given the known relation between systemic inflammation and tumor progression, we sought to evaluate whether blood-derived systemic inflammation markers might help to the predict nodal outcome in patients with stage Ia NSCLC.

METHODS

Preoperative levels of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic inflammation score (SII, platelets × NLR) were collected from 368 patients who underwent curative lung resection for NSCLC. After categorization, inflammatory markers were subjected to logistic regression and time-event analysis in order to find associations with occult nodal spread and postoperative nodal recurrence.

RESULTS

No inflammation marker was associated with the risk of occult nodal spread. SII showed a marginal effect on early nodal recurrence at a quasi-significant level (p = 0.065). However, patients with T1c tumors and elevated PLR and/or SII had significantly shorter times to nodal recurrence compared to T1a/T1b patients (p = 0.001), while patients with T1c and normal PLR/SII did not (p = 0.128).

CONCLUSIONS

blood-derived inflammation markers had no value in the preoperative prediction of nodal status. Nevertheless, our results might suggest a modulating effect of platelet-derived inflammation markers on nodal progression after the resection of tumors larger than 2 cm.

Machine learning for identifying benign and malignant of thyroid tumors: A retrospective study of 2,423 patients

Thyroid tumors, one of the common tumors in the endocrine system, while the discrimination between benign and malignant thyroid tumors remains insufficient. The aim of this study is to construct a diagnostic model of benign and malignant thyroid tumors, in order to provide an emerging auxiliary diagnostic method for patients with thyroid tumors. The patients were selected from the Chongqing General Hospital (Chongqing, China) from July 2020 to September 2021. And peripheral blood, BRAFV600E gene, and demographic indicators were selected, including sex, age, BRAFV600E gene, lymphocyte count (Lymph#), neutrophil count (Neu#), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), red blood cell distribution width (RDW), platelets count (PLT), red blood cell distribution width-coefficient of variation (RDW-CV), alkaline phosphatase (ALP), and parathyroid hormone (PTH). First, feature selection was executed by univariate analysis combined with least absolute shrinkage and selection operator (LASSO) analysis. Afterward, we used machine learning algorithms to establish three types of models. The first model contains all predictors, the second model contains indicators after feature selection, and the third model contains patient peripheral blood indicators. The four machine learning algorithms include extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), and adaptive boosting (AdaBoost) which were used to build predictive models. A grid search algorithm was used to find the optimal parameters of the machine learning algorithms. A series of indicators, such as the area under the curve (AUC), were intended to determine the model performance. A total of 2,042 patients met the criteria and were enrolled in this study, and 12 variables were included. Sex, age, Lymph#, PLR, RDW, and BRAFV600E were identified as statistically significant indicators by univariate and LASSO analysis. Among the model we constructed, RF, XGBoost, LightGBM and AdaBoost with the AUC of 0.874 (95% CI, 0.841-0.906), 0.868 (95% CI, 0.834-0.901), 0.861 (95% CI, 0.826-0.895), and 0.837 (95% CI, 0.802-0.873) in the first model. With the AUC of 0.853 (95% CI, 0.818-0.888), 0.853 (95% CI, 0.818-0.889), 0.837 (95% CI, 0.800-0.873), and 0.832 (95% CI, 0.797-0.867) in the second model. With the AUC of 0.698 (95% CI, 0.651-0.745), 0.688 (95% CI, 0.639-0.736), 0.693 (95% CI, 0.645-0.741), and 0.666 (95% CI, 0.618-0.714) in the third model. Compared with the existing models, our study proposes a model incorporating novel biomarkers which could be a powerful and promising tool for predicting benign and malignant thyroid tumors.

Prognostic factors for long-term survival following complete resection by lobectomy in stage I non-small cell lung cancer

BACKGROUND

The aim of this study was to evaluate predictors for long-term overall survival (OS) in patients with stage I non-small cell lung cancer (NSCLC).

METHODS

All patients undergoing complete resection by lobectomy for stage I NSCLC between October 2012 and December 2015 at a single center were included. Univariable and multivariable Cox regression analyses were performed to identify prognostic factors.

RESULTS

A total of 92 patients were included. Univariable and multivariable Cox regression analyses revealed preoperative neutrophil to lymphocyte ratio (NLR, p = 0.005), preoperative diffusion capacity of the lungs for carbon monoxide (DLCO, p = 0.010) and forced expiratory volume in 1 second (FEV1, p = 0.041) as well as male gender (p = 0.026) as independent prognostic factors for OS. Combining the calculated cutoff values for FEV1 (<73.0%) and NLR (>3.49) into one parameter resulted in a highly significant difference in survival times when stratified by this variable.

CONCLUSIONS

Recently, much emphasis has been put on the prognostic importance of blood biomarkers in NSCLC. In our study, NLR was an independent factor for OS, as were baseline characteristics such as DLCO, FEV1, and gender. Further studies on the association of biomarkers for systemic inflammation and lung function parameters with respect to patient survival are warranted.

Perioperative Serum Scoring Systems Predict Early Recurrence and Poor Prognosis of Resectable Pancreatic Cancer

Objective

Some patients with pancreatic ductal adenocarcinoma (PDAC) are prone to rapid recurrence or metastasis after radical resection. However, evaluation methods for effectively identifying these patients are lacking. In this study, we established perioperative serum scoring systems to screen patients with early recurrence and poor prognosis.

Methods

We systematically analysed 44 perioperative serum parameters, including systemic inflammatory parameters, coagulation system parameters, tumor markers, and 18 clinicopathological characteristics of 218 patients with radical resection in our centre. Univariate Cox regression and LASSO regression models were used to screen variables. Kaplan-Meier survival analysis was used to compare relapse-free survival and overall survival. Multivariate Cox regression was used to evaluate the independent risk variables. AUC and C-index were used to reveal the effectiveness of the models. In addition, the effectiveness was also verified in an independent cohort of 109 patients.

Results

Preoperative systemic immune coagulation cascade (SICC) (including increased neutrophil to lymphocyte ratio, decreased lymphocyte to monocyte ratio, increased platelet and fibrinogen) and increased postoperative tumor markers (TMs) (CA199, CEA and CA242) were independent risk factors for early recurrence of resectable pancreatic cancer. On this basis, we established the preoperative SICC score and postoperative TMs score models. The patients with higher preoperative SICC or postoperative TMs score were more likely to have early relapse and worse prognosis. The nomogram based on preoperative SICC, postoperative TMs, CACI, smoking index, vascular cancer embolus and adjuvant chemotherapy can effectively evaluate the recurrence rate (AUC_{1 year}: 0.763, AUC_{2 year}: 0.679, AUC_{3 year}: 0.657) and overall survival rate (AUC_{1 year}: 0.770, AUC_{3 year}: 0.804, AUC_{5 year}: 0.763).

Conclusion

Preoperative SICC and postoperative TMs can help identify resectable PDAC patients with early recurrence and poor prognosis.

A New Scoring System to Predict Lymph Node Metastasis and Prognosis After Surgery for Gastric Cancer

Background

Lymph node metastasis is one of the most important factors affecting the prognosis of gastric cancer patients. The purpose of this study is to develop a new scoring system to predict lymph node metastasis in gastric cancer using preoperative tests in various combinations of inflammatory factors and to assess the predictive prognosis value of the new scoring system for the postoperative gastric cancer patients.

Method

This study includes 380 gastric cancer patients, 307 in the training set and 73 in the validation set. We obtain three inflammatory markers, CRA (C-reactive protein/albumin), SIRI (systemic inflammatory response index), and PLR (platelets/lymphocytes), by calculating and comparing the results of preoperative laboratory tests. By using these three indicators, a new scoring system is developed to predict lymph node metastases, assess patients’ prognoses, and compare clinicopathological characteristics in different patient subgroups. A nomogram is constructed to show and assess the predictive efficacy of every index for lymph node metastasis and survival.

Results

In the new scoring system, higher scores are associated with more advanced pathological stage (p < 0.001), perineural invasion (p < 0.001), and vascular invasion (p = 0.001). Univariate and multivariable Cox regression analyses show that perineural invasion, vascular invasion, smoking history, and high scores on the new scoring system are significant risk factors for OS and RFS. High-scoring subgroups as an independent prognostic factor could predict overall survival (OS) and relapse-free survival (RFS). High scores on the new scoring system are significantly associated with the degree of lymph node metastasis (p < 0.001). CAR and PLR play very important roles in predicting lymph node metastasis in gastric cancer. CAR is a vital major marker in the prediction of patient survival.

Conclusions

The new scoring system can effectively predict the patients’ lymph node metastasis with gastric cancer and can independently predict the prognosis of patients.

Prognostic evaluation of colorectal cancer using three new comprehensive indexes related to infection, anemia and coagulation derived from peripheral blood

Background: Many indicators of peripheral blood in routine blood test (BRT) results of colorectal cancer (CRC) patients are related to prognosis. Currently, indexes such as NLR (Neutrophil-to- Lymphocyte Ratio), PLR (Platelet-to-Lymphocyte Ratio) and LMR (Lymphocyte-to-Monocyte ratio) evaluate the survival risk of patients by assessing the inflammatory - immune status of CRCs. These indexes are more comprehensive and accurate than independent estimates. We hope to design more effective indexes through fully considering the correlation and significance between BRT indicators and prognosis, so as to play a guiding role in clinical malignant estimation of CRCs.

Methods: 701 CRCs in training set and 256 CRCs in test set were included in the study samples, and their clinical data, tumor pathology results and peripheral blood routine results were collected. The prognosis, progression, and survival status of all patients were determined after follow-up. Above data were used for statistical analysis and designing new indexes.

Results: It was found that high NE, MONO, RDW-CV/SD and PLT in peripheral blood indicated poor prognosis of DFS and OS. Conversely, CRCs with postoperative tumor progression or death had lower LY, EO, RBC, HGB, HCT, MCV, MCH, MCHC, PDW, and P-LCR. IRR, ARR and CRR related to infection, anemia and coagulation were designed respectively using the largest AUC indicators (P<0.05) selected by ROC curve. The formula: IRR= (NE*MONO)/(LY*EO); ARR= (HGB*MCHC)/RDW-CV; CRR=PLT/PDW. Results of Kaplan‑Meier survival analysis and multivariate COX proportional hazard analysis adjusted for age, gender, TNM stage, infiltration, adhesion showed IRR, ARR, CRR were all able to be used as the evaluation standard of survival of CRC. The result was also authenticated in the test set.

Conclusion: We designed three different prognostic indexes of colorectal cancer, IRR, ARR and CRR, which could be used as risk indicators of CRC prognosis, tumor progression and survival.