Association between duration of smoking abstinence before non-small-cell lung cancer diagnosis and survival: a retrospective, pooled analysis of cohort studies

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

The chemical memory of smoking tobacco

The concentration in urine of N-acetyl-hydroxy-propyl-cisteine (3HPMA), an acrolein metabolite, has been employed as a marker of the risk of illness of smokers and the relative concentration of creatinine has been evaluated to verify the effect of moving from the practice of burning tobacco to nicotine vaping. From the results concerning the urine samples of 38 subjects, collected from 2021 to 2023 and analyzed by LC-MS/MS, corresponding to 5 active smokers, 13 previously heavy smokers who replaced traditional tobacco by vaping, and 20 non-smokers, a dramatic reduction was found in 3HPMA/creatinine in urine. 3HPMA varied from values of 2150-3100 μg gcreatinine-1 to levels of 225-625 μg gcreatinine-1 found for non-smokers, with the time decay described by the equation y = 0.3661x2 - 94.359x + 6246.4 (R2: 0.757), providing a time of approximately 10 years for tobacco memory after the cessation of the consumption of burned tobacco.

Impact of smoking cessation duration on lung cancer mortality: A systematic review and meta-analysis

BACKGROUND

Smoking history is a heterogeneous situation for different populations, and numerous studies suggest that smoking cessation is conducive to reduce the mortality of lung cancer. However, no quantitative meta-analysis regarding smoking cessation duration based on different populations has demonstrated it clearly.

METHODS

We systematically searched four electronic databases (PubMed, Embase, the Cochrane Central Register of Controlled Trials, and Scoups) till February 2023. Eligible studies reported the association between lung cancer survival and duration of smoking cessation. Additionally, we stratified the study population according to whether they had lung cancer at the time they quit smoking. Studies were pooled with the random-effects model.

RESULTS

Out of the 11,361 potential studies initially identified, we included 24 studies involving 969,560 individuals in our analysis. Lung cancer mortality varied across two groups: general quitters and peri-diagnosis quitters. For general quitters, those who had quit smoking for less than 10 years exhibited an RR of 0.64 (95% CI [0.55-0.76]), while those who quit for 10-20 years had an RR of 0.33 (0.25-0.43), over 20 years had an RR of 0.16 (0.11-0.24), and never-smokers had an RR at 0.11 (0.07-0.15). Among peri-diagnosis quitters, the 1-year Overall Survival (OS) showed an RR of 0.80 (0.67-0.96), the 2-year OS had an RR of 0.89 (0.80-0.98), the 3-year OS had an RR of 0.93 (0.84-1.03), and the 5-year OS had an RR of 0.85 (0.76-0.96).

CONCLUSIONS

Earlier and longer smoking cessation is associated with reduced lung cancer mortality, no matter in which cessation stage for two different populations.

Nunavimmi puvakkut kaggutimik aanniaqarniq: Qanuilirqitaa? Lung cancer in Nunavik: How are we doing? A retrospective matched cohort study

BACKGROUND

Whether Inuit in Canada experience disparities in lung cancer survival remains unknown. When requiring investigation and treatment for lung cancer, all residents of Nunavik, the Inuit homeland in Quebec, are sent to the McGill University Health Centre (MUHC), in Montréal. We sought to compare survival among patients with lung cancer at the MUHC, who were residents of Nunavik and Montréal, Quebec, respectively.

METHODS

We conducted a retrospective cohort study. Using lung cancer registry data, we identified Nunavik residents with histologically confirmed lung cancer diagnosed between 2005 and 2017. We aimed to match 2 Montréal residents to each Nunavik resident on sex, age, calendar year of diagnosis, and histology (non-small cell lung cancer v. small cell lung cancer). We reviewed medical records for data on additional patient characteristics and treatment, and obtained vital status from a provincial registry. We compared survival using Kaplan-Meier analysis and Cox proportional hazards regression.

RESULTS

We included 95 residents of Nunavik and 185 residents of Montréal. For non-small cell lung cancer, median survival times were 321 (95% confidence interval [CI] 184-626) days for Nunavik (n = 71) and 720 (95% CI 536-1208) days for Montréal residents (n = 141). For small cell lung cancer, median survival times were 190 (95% CI 159-308) days for Nunavik (n = 24) and 270 (95% CI 194-766) days for Montréal residents (n = 44). Adjusting for matching variables, stage, performance status, and comorbidity, Nunavik residents had a higher hazard of death (hazard ratio 1.68, 95% CI 1.17-2.41).

INTERPRETATION

Nunavik residents experience disparities in survival after lung cancer diagnosis. Although studies in other Inuit Nunangat regions are needed, our findings point to an urgent need to ensure that interventions aimed at improving lung cancer survival, including lung cancer screening, are accessible to Inuit Nunangat residents.

Lung cancer in patients who have never smoked - an emerging disease

Lung cancer is the most common cause of cancer-related deaths globally. Although smoking-related lung cancers continue to account for the majority of diagnoses, smoking rates have been decreasing for several decades. Lung cancer in individuals who have never smoked (LCINS) is estimated to be the fifth most common cause of cancer-related deaths worldwide in 2023, preferentially occurring in women and Asian populations. As smoking rates continue to decline, understanding the aetiology and features of this disease, which necessitate unique diagnostic and treatment paradigms, will be imperative. New data have provided important insights into the molecular and genomic characteristics of LCINS, which are distinct from those of smoking-associated lung cancers and directly affect treatment decisions and outcomes. Herein, we review the emerging data regarding the aetiology and features of LCINS, particularly the genetic and environmental underpinnings of this disease as well as their implications for treatment. In addition, we outline the unique diagnostic and therapeutic paradigms of LCINS and discuss future directions in identifying individuals at high risk of this disease for potential screening efforts.

Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis

BACKGROUND

Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results.

METHODS

A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical-radiological semantic features; (2) The radiomics models (radiomics+ and radiomics-) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite-) incorporated both above.

RESULTS

In the testing set, the radiomics+/- models and the composite+/- models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/- models and the composite+/- models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/- and composite+/- models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics-, composite+ model vs. composite-) (p > 0.05).

CONCLUSIONS

The present study established a machine learning-based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability.