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Lung cancer has high incidence and mortality rates, and lung squamous cell carcinoma (LUSC) is a common form of non-small-cell lung carcinoma (NSCLC). The aim of our study was to discover long non-coding RNAs (lncRNAs) associated with LUSC prognosis. RNA-sequencing data obtained from LUSC samples were extracted from The Cancer Genome Atlas database. Using the limma package, differentially expressed genes (DEGs; including differentially expressed lncRNA genes (DELs), coding genes (DECs), and other genes (DEOs)) between LUSC and control samples were analyzed. Using Kaplan-Meier survival analysis, prognosis-associated lncRNAs were further selected. Following the calculation of Pearson's correlation coefficients between DELs and other DEGs, the DEL-DEG co-expression network was visualized using Cytoscape software. Using the clusterProfiler package, potential functions for DECs co-expressed with DELs were predicted. There were 1,305 DEGs in LUSC samples, including 153 DELs, 1,109 DECs, and 43 DEOs. Based on survival analysis, 22 prognosis-associated lncRNAs (including surfactant associated 1, pseudogene (SFTA1P), long intergenic non-protein coding RNA 968 (LINC00968), GATA6 antisense RNA 1, (GATA6-AS1) TBX5 antisense RNA 1 (TBX5-AS1) and FEZF1 antisense RNA 1 (FEZF1-AS1)) in LUSC were selected from these DELs, and the associated abnormal expression levels were also verified in LUSC clinical samples. A DEL-DEG co-expression network was constructed, which involved 93 DELs. Co-expressed DECs were enriched for only 8 prognosis-associated DELs, including LINC00968, SFTA1P, and TBX5-AS1. Specifically, mitogen-activated protein kinase (MAPK) signaling pathway-associated genes were enriched in DECs co-expressed with LINC00968, SFTA1P, GATA6-AS1, TBX5-AS1 and FEZF1-AS1, which may be prognosis-associated lncRNAs in LUSC. In addition, LINC00968 may affect the outcome of patients with LUSC via the MAPK signaling pathway.

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Lung cancer has the world’s second highest cancer incidence and second highest cancer-related mortality rate. However, the mechanism underlying non-small cell lung cancer (NSCLC) remained to be unclear. Overall, this study for the first time revealed Stress Granule Regulators were mutated and dysregulated in NSCLC samples by analyzing TCGA database. Moreover, three subtypes of NSCLC were identified based on the expression levels of Stress Granule Regulators. Patients in cluster 2 showed a higher survival rate than those in clusters 1 and 3. Bioinformatics analysis indicated the cell cycle, mTOR signaling pathway, EGFR signaling, PI3K/Akt signaling and DNA damage repair signaling were significantly related to molecular subtypes. Moreover, we performed a prediction analysis of the response to the inhibitors against the aforementioned signaling. Our results showed patients in C2 NSCLC had the highest sensitivity to MK.2206 (AKT.inhibitor) and Rapamycin (mTOR inhibitor). Patients in C3 NSCLC had the highest sensitivity for Temsirolimus (PI3K/mTOR signaling), BIBW2992 (EGFR signaling), Erlotinib (EGFR signaling), PD.0332991 (CDK4/6 inhibitor), CGP.60474 (CDK inhibitor), and Gefitinib (EGFR signaling). Moreover, our results showed patients in C1 NSCLC had the highest sensitivity to AKT.inhibitor, AZD6482 (PI3K inhibitor). To evaluate the response to immune therapy of different subtypes, we analyzed the tumor immune inflation, immune regulators expression, and TIDE score in different SG related subtypes. These results showed that C2 and C3 may be more sensitive to immune therapy. To better predict the prognosis of NSCLC, we analyzed the correlation between stress granule regulator expression and overall survival time in NSCLC and constructed a Stress Granule Score including EIF2S1, CTSG, EIF4G1, IGF2BP1, PABPC1 to predict the prognosis of NSCLC. Overall, this study for the first time uncovers the effect of stress particles on drug response, immune response, and prognosis, laying a new theoretical foundation for the NSCLC prognosis and treatment.

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