Squamous cell carcinoma of the lung: Improving the detection and management of immune-related adverse events

Revealing roles of PANoptosis-related genes in prognosis and molecular subtypes in lung squamous cell carcinoma by integrated bioinformatic analyses and experiments

The purpose of current study was to reveal the role of PANoptosis-associated genes in lung squamous cell carcinoma (LUSC) and their potential as prognostic biomarkers. We analyzed RNA-seq data from TCGA-LUSC and GEO datasets to identify differentially expressed genes (DEGs) between LUSC and normal samples, followed by VENN analysis to reveal PANoptosis-related DEGs. Functional enrichment analyses were performed by clusterProfiler package. Distinct LUSC subtypes were identified by consensus clustering based on PANoptosis-related DEGs. Univariate Cox and LASSO regression were utilized to identify key prognostic genes, and a prognostic model was developed based on selected genes. Immune infiltration status was evaluated by CIBERSORT and ESTIMATE algorithms. Expression of key prognostic genes was tested in three LUSC cell lines by RT-qPCR and Western blot. Roles of TLR3 in LUSC progression were determined by functional experiments. A total of 76 PANoptosis-related DEGs were identified, with significant enrichment in apoptosis pathways. The clustering analysis revealed four subtypes, in which survival and immune microenvironment were dramatically different. From the 76 genes, four key prognostic genes (CHEK2, PDK4, TLR3, and IL1B) were identified to establish prognostic risk model, which could reflect the survival status and immune cells composition variations for LUSC patients. Besides, these four genes showed significant correlations with infiltrating levels of various immune cells. TLR3 was identified as a more weighted prognostic risk gene in LUSC. Functional assays demonstrated that genes like TLR3 modulated cell proliferation, migration, and inflammatory responses in LUSC cells. This study highlighted the potential of the four key PANoptosis genes as biomarkers or targets in LUSC, and the risk model based on these four genes provided novel insights to develop personalized treatment strategy for patients with LUSC.

Case report: Multi-organ injuries induced by tislelizumab

The use of immune checkpoint inhibitors (ICIs) often develops immune-related adverse events (irAEs). However, irAEs-induced multi-organ injuries remain a rare event. We herein report a case of multi-organ injuries induced by tislelizumab in a lung squamous cell carcinoma (LUSC) patient. A 68-year-old man had undergone neoadjuvant chemotherapy with paclitaxel, carboplatin, and tislelizumab. He presented with a 1-month history of nausea and poor appetite after the second dose of therapy. During investigations, rhabdomyolysis, liver, kidney, and thyroid damage were detected. After multi-disciplinary consultation, multi-organ injuries related to ICIs (striated muscle, liver, kidney, and thyroid) were considered to result from cumulated irAEs induced by tislelizumab. The patient was treated with levothyroxine, methylprednisolone, intravenous immunoglobulins, and continuous renal replacement therapy. After treatment, the patient recovered and was discharged from the hospital. The patient presented with multiple organ damage, not single immunity treatment adverse reactions, relatively rare. In clinical work, irAEs are likely not a single-system organ disorder and many kinds of attention need to be combined with the risk of multi-system damage.

Prognostic Significance of Glycolysis-Related Genes in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LUSC) is one of the most common malignancies. There is growing evidence that glycolysis-related genes play a critical role in tumor development, maintenance, and therapeutic response by altering tumor metabolism and thereby influencing the tumor immune microenvironment. However, the overall impact of glycolysis-related genes on the prognostic significance, tumor microenvironment characteristics, and treatment outcome of patients with LUSC has not been fully elucidated. We used The Cancer Genome Atlas (TCGA) dataset to screen glycolysis-related genes with prognostic effects in LUSC and constructed signature and nomogram models using Lasso and Cox regression, respectively. In addition, we analyzed the immune infiltration and tumor mutation load of the genes in the models. We finally obtained a total of glycolysis-associated DEGs. The signature model and nomogram model had good prognostic power for LUSC. Gene expression in the models was highly correlated with multiple immune cells in LUSC. Through this analysis, we have identified and validated for the first time that glycolysis-related genes are highly associated with the development of LUSC. In addition, we constructed the signature model and nomogram model for clinical decision-making.

Establishment of a prognosis prediction model for lung squamous cell carcinoma related to PET/CT: basing on immunogenic cell death-related lncRNA

BACKGROUND

Immunogenic cell death (ICD) stimulates adaptive immunity and holds significant promise in cancer therapy. Nevertheless, the influence of ICD-associated long non-coding RNAs (lncRNAs) on the prognosis of patients with lung squamous cell carcinoma (LUSC) remains unexplored.

METHODS

We employed data from the The Cancer Genome Atlas (TCGA)database to identify ICD-related lncRNAs associated with the prognosis of LUSC using univariate Cox regression analysis. Subsequently, we utilized the LOSS regression model to construct a predictive risk model for assessing the prognosis of LUSC patients based on ICD-related lncRNAs. Our study randomly allocated187 TCGA patients into a training group and 184 patients for testing the predictive model. Furthermore, we conducted quantitative polymerase chain reaction (qPCR) analysis on 43 tumor tissues from LUSC patients to evaluate lncRNA expression levelsPearson correlation analysis was utilized to analyze the correlation of risk scores with positron emission tomography/computed tomography (PET/CT) parameters among LUSC patients.

RESULTS

The findings from the univariate Cox regression revealed 16 ICD-associated lncRNAs linked to LUSC prognosis, with 12 of these lncRNAs integrated into our risk model utilizing the LOSS regression. Survival analysis indicated a markedly higher overall survival time among patients in the low-risk group compared to those in the high-risk group. The area under the Receiver operating characteristic (ROC) curve to differentiate high-risk and low-risk patients was 0.688. Additionally, the overall survival rate was superior in the low-risk group compared to the high-risk group. Correlation analysis demonstrated a positive association between the risk score calculated based on the ICD-lncRNA risk model and the maximum standard uptake value (SUVmax) (r = 0.427, P = 0.0043) as well as metabolic volume (MTV)of PET-CT (r = 0.360, P = 0.0177) in 43 LUSC patients.

CONCLUSION

We have successfully developed a risk model founded on ICD-related lncRNAs that proves effective in predicting the overall survival of LUSC patients.

miR-139-5p and miR-451a as a Diagnostic Biomarker in LUSC

Background

Lung squamous cell carcinoma (LUSC) is a type of lung cancer that originates from segmental or subsegmental bronchial mucosa. There is evidence that miRNA plays an important role in the occurrence and progression of tumors.

Methods

In this study, plasma samples of patients with early LUSC and healthy volunteers were subjected to miRNA sequencing, and the levels of differentially expressed miRNAs (DEMs) in LUSC tissues were analyzed using R language. Cox regression and Kaplan-Meier (K-M) survival curve analyses were performed to determine the relationship between DEMs and prognosis in LUSC, and PCR method was verified for the plasma expression level of DEMs in patients with LUSC. The levels of CYFRA21-1 and SCC-Ag in plasma were measured, and area under curve (AUC) was used to evaluate the diagnostic value of the DEMs.

Results

A total of 21 DEMs were screened out by sequencing. The expression levels of DEMs in tissue samples in the TCGA database were analyzed, and four DEMs with consistent expression levels were further screened from plasma and tissue samples. Regression analysis and K-M curve were performed to select two DEMs (miR-139-5p, miR-451a) that were correlated with the prognosis. PCR verification results showed that the levels of miR-451a and miR-139-5p were low in patients, and the level of miR-139-5p in late stages III & IV with the patients of LUSC was higher than that in stages I & II. The AUC values of the four indicators (SCC-Ag, CYFRA21-1, miR-451a and miR-139-5p) in the diagnosis of LUSC, early and late cases were 0.884, 0.935 and 0.778, respectively.

Conclusion

The detection of miR-139-5p and miR-451a levels in plasma has a certain potential in the non-invasive diagnosis, especially in patients with early stages of LUSC.