BACKGROUND: Lung cancer continues to be a prevalent cause of cancer-related deaths worldwide, with lung squamous carcinoma (LUSC) being a significant subtype characterized by comparatively low survival rates. Extensive molecular studies on LUSC have been conducted; however, the clinical importance of cell-cycle-associated genes has rarely been examined. This study aimed to investigate the relationship between these genes and LUSC.
METHODS: The expression trends of genes related to the cell cycle in a group of patients with LUSC were analyzed. Clinical information and mRNA expression data were obtained from The Cancer Genome Atlas via cBioportal. Multiple analyses have been performed to investigate the association between these genes and LUSC.
RESULTS: Three clusters were identified based on the mRNA expression of 124 cell cycle-associated genes. Cluster 3 exhibited the worst prognosis. A comparative analysis showed that nine expressed genes differed distinctly among all the clusters. Among these nine genes, elevated expression of CDK4 was strongly associated with positive prognosis. Furthermore, the expression of ANAPC11, ANAPC5, and ORC4 correlated with the advancement of LUSC pathological stages.
CONCLUSIONS: Gene expression profiles associated with the cell cycle across various LUSC subtypes were identified, highlighting that specific genes are related to prognosis and disease stages. Based on these results, new prognostic strategies, patient stratification, and targeted therapy trials have been conducted for LUSC.

BACKGROUND: Immune checkpoint blockade has emerged as a key strategy to the therapy landscape of non-small cell lung cancer (NSCLC). However, notable differences in immunotherapeutic outcomes exist between the two primary NSCLC subtypes: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). This disparity may stem from the tumor immune microenvironment\'s heterogeneity at the transcriptome level.
METHODS: By integrative analysis of transcriptomic characterization of 38 NSCLC patients by single-cell RNA sequencing, the present study revealed a distinct tumor microenvironment (TME) between LUAD and LUSC, with relevant results further confirmed in bulk transcriptomic and multiplex immunofluorescence (mIF) validation cohort of neoadjuvant immunotherapy patients.
RESULTS: LUAD exhibited a more active immune microenvironment compared to LUSC. This included highly expression of HLA I/II in cancer cells, reinforced antigen presentation potential of dendritic cells and enhanced cytotoxic activity observed in T/NK cells. In LUSC, cancer cells highly expressed genes belonging to the aldo-keto reductases, glutathione S-transferases and aldehyde dehydrogenase family, negatively correlating with immunotherapy outcomes in the validation cohort of our center. Further analysis revealed elevated infiltrated cancer-associated fibroblasts (CAFs) in LUSC, which was corroborated in The Cancer Genome Atlas cohort. Corresponding increased infiltration of ADH1B+ CAFs in major pathologic response (MPR) patients and the higher presence of FAP+ CAFs in non-MPR patients were demonstrated by multiplex mIF. Moreover, upregulating immunosuppressive extracellular matrix remodeling was identified in LUSC.
CONCLUSIONS: These comprehensive analyses advance the understanding of the differences in TME between LUAD and LUSC, offering insights for patient selection and developing subtype-specific treatment strategies.

BACKGROUND: Tetrastigma hemsleyanum （T. hemsleyanum）, valued in traditional medicine for its potential to boost immunity and combat tumors, contains uncharacterized active compounds and mechanisms. This represents a significant gap in our understanding of its ethnopharmacological relevance.
OBJECTIVE: To involve the mechanism of anti-lung cancer effect of T. hemsleyanum by means of experiment and bioinformatics analysis.
METHODS: The anticancer mechanism of T. hemsleyanum against lung squamous carcinoma (LUSC) in zebrafish was investigated. The LUSC model was established by injecting NCI-H2170 cells in the zebrafish and evaluating its anti-tumor efficacy. Next, component targets and key genes were obtained by molecular complex detection (MCODE) analysis and protein-protein interaction (PPI) network analysis. Component analysis of T. hemsleyanum was performed by UPLC-Q-TOF-MS. Molecular docking was used to simulate the binding activities of key potential active components to core targets were simulated using. Prognostic and pan-cancer analyses were then performed to validate the signaling pathways involved in the prognostic genes using gene set enrichment analysis (GSEA). Subsequently, Molecular dynamics simulations were then performed for key active components and core targets. Finally, cellular experiments were used to verify the expression of glutamate metabotropic receptor 3 (GRM3) and glutamate metabotropic receptor 7 (GRM7) in the anticancer effect exerted of T. hemsleyanum.
RESULTS: We experimentally confirmed the inhibitory effect of T. hemsleyanum on LUSC by transplantation of NCI-H2170 cells into zebrafish. There are 20 main compounds in T. hemsleyanum, such as procyanidin B1, catechin, quercetin, and kaempferol, etc. A total of 186 component targets of T. hemsleyanum and sixteen hub genes were screened by PPI network and MCODE analyses. Molecular docking and molecular dynamics simulation results showed that Gingerglycolipid B and Rutin had higher affinity with GRM3 and GRM7, respectively. Prognostic analysis, Pan-cancer analysis and verification experiment also confirmed that GRM3 and GRM7 were targets for T. hemsleyanum to exert anti-tumor effects and to participate in immune and mutation processes. In vitro experiments suggested that the inhibitory effect of T. hemsleyanum on cancer cells was correlated with GRM3 and GRM7.
CONCLUSIONS: In vivo, in vitro and in silico results confirmed the potential anticancer effects against LUSC of T. hemsleyanum, which further consolidated the claim of its traditional uses.

BACKGROUND: Cancer stem cells (CSCs) are a specific subpopulation of cancer cells with the ability of self-renewal, infinite proliferation, multidifferentiation and tumorigenicity, and play critical roles in cancer progression and treatment resistance. CSCs are tightly regulated by the tumor microenvironment, such as hypoxia; however, how hypoxia regulates CSCs in non-small cell lung cancer (NSCLC) remains unclear.
METHODS: The proportion of ALDHhi cells was examined using the Aldefluor assay. Tankyrase inhibitor XAV939 and siRNA were used to inhibit β-catenin while pcDNA3-β-catenin (S33Y) plasmid enhanced the expression of β-catenin. Western blot was administered for protein detection. The mRNA expression was measured by quantitative real-time PCR.
RESULTS: We found that hypoxia led to an increase in the proportion of ALDHhi cells in lung squamous carcinoma (LUSC) H520 cells, while causing a decrease in the ALDHhi cell proportion in lung adenocarcinoma (LUAD) A549 cells. Similarly, β-catenin expression was upregulated in H520 cells but downregulated in A549 cells upon exposure to hypoxia. Mechanically, the proportion of ALDHhi cells in both cell lines was decreased by β-catenin inhibitor or siRNA knockdown, whereas increased after β-catenin overexpression. Furthermore, hypoxia treatment suppressed E-cadherin expression in H520 cells and enhanced N-cadherin and β-catenin expression, while this effect was completely opposite in A549 cells.
CONCLUSIONS: The hypoxia-EMT-β-catenin axis functions as an important regulator for the proportion of CSCs in NSCLC and could potentially be explored as therapeutic targets in the future.

Drug resistance is the major difficulty in treatment of lung squamous cell carcinoma (LUSC). This study aims to explore drug response-related miRNAs (DRmiRNAs) based on multi-omics research. We identified DRmiRNAs of LUSC with a multi-omics integrated system that combines expression data of microRNA, lncRNA, mRNA, methylation levels, somatic mutations. After identifying DRmiRNAs, we screened and validated of the target mRNAs of DRmiRNAs through Targetscan and the miRDB database. Then, Real-time PCR and Western blot assays were used to estimate the expression of DRmiRNAs and target protein, and the dual-luciferase assays were used to confirm the interaction of DRmiRNAs and target mRNA. Furthermore, CCK-8 (Cell Counting Kit-8) assays were used to evaluate cell proliferation and drug sensitivity. After integrated analysis, hsa-miR-185-5p was identified as DRmiRNA based on multi-omics data. Through Targetscan and miRDB database, the possible target mRNAs were obtained and PCDHA11 was validated as a target mRNA of miR-185-5p by real-time PCR, Western blot assays and dual-luciferase assays. CCK-8 assays and clone formation assays showed that the proliferation of miR-185-5p mimics was significantly slower than that of miR-185-5p inhibitors, which means overexpression of miR-185-5p enhanced the anticancer effects of cisplatin, whereas the downregulation of miR-185-5p reduced the effects. Furthermore, the proliferation of silencing PCDHA11 was significantly slower than that of overexpression of PCDHA11, which means PCDHA11 overexpression weakened the anticancer effects of cisplatin, and silencing PCDHA11 enhanced the effects. This study demonstrated that miR-185-5p was involved in chemoresistance of LUSC cells to cisplatin partly via down-regulating PCDHA11, which may promote understanding the underlying molecular mechanisms of drug response.

UNASSIGNED: The proteasome is a crucial mechanism that regulates protein fate and eliminates misfolded proteins, playing a significant role in cellular processes. In the context of lung cancer, the proteasome\'s regulatory function is closely associated with the disease\'s pathophysiology, revealing multiple connections within the cell. Therefore, studying proteasome inhibitors as a means to identify potential pathways in carcinogenesis and metastatic progression is crucial in in-depth insight into its molecular mechanism and discovery of new therapeutic target to improve its therapy, and establishing effective biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized treatment for lung squamous carcinoma in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine).
UNASSIGNED: This study identified differentially expressed proteasome genes (DEPGs) in lung squamous carcinoma (LUSC) and developed a gene signature validated through Kaplan-Meier analysis and ROC curves. The study used WGCNA analysis to identify proteasome co-expression gene modules and their interactions with the immune system. NMF analysis delineated distinct LUSC subtypes based on proteasome gene expression patterns, while ssGSEA analysis quantified immune gene-set abundance and classified immune subtypes within LUSC samples. Furthermore, the study examined correlations between clinicopathological attributes, immune checkpoints, immune scores, immune cell composition, and mutation status across different risk score groups, NMF clusters, and immunity clusters.
UNASSIGNED: This study utilized DEPGs to develop an eleven-proteasome gene-signature prognostic model for LUSC, which divided samples into high-risk and low-risk groups with significant overall survival differences. NMF analysis identified six distinct LUSC clusters associated with overall survival. Additionally, ssGSEA analysis classified LUSC samples into four immune subtypes based on the abundance of immune cell infiltration with clinical relevance. A total of 145 DEGs were identified between high-risk and low-risk score groups, which had significant biological effects. Moreover, PSMD11 was found to promote LUSC progression by depending on the ubiquitin-proteasome system for degradation.
UNASSIGNED: Ubiquitinated proteasome genes were effective in developing a prognostic model for LUSC patients. The study emphasized the critical role of proteasomes in LUSC processes, such as drug sensitivity, immune microenvironment, and mutation status. These data will contribute to the clinically relevant stratification of LUSC patients for personalized 3P medical approach. Further, we also recommend the application of the ubiquitinated proteasome system in multi-level diagnostics including multi-omics, liquid biopsy, prediction and targeted prevention of chronic inflammation and metastatic disease, and mitochondrial health-related biomarkers, for LUSC 3PM practice.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13167-024-00352-w.

Background  The purpose of our study was to construct a prognostic model based on ferroptosis-related gene signature to improve the prognosis prediction of lung squamous carcinoma (LUSC). Methods  The mRNA expression profiles and clinical data of LUSC patients were downloaded. LUSC-related essential differentially expressed genes were integrated for further analysis. Prognostic gene signatures were identified through random forest regression and univariate Cox regression analyses for constructing a prognostic model. Finally, in a preliminary experiment, we used the reverse transcription-quantitative polymerase chain reaction assay to verify the relationship between the expression of three prognostic gene features and ferroptosis. Results  Fifty-six ferroptosis-related essential genes were identified by using integrated analysis. Among these, three prognostic gene signatures (HELLS, POLR2H, and POLE2) were identified, which were positively affected by LUSC prognosis but negatively affected by immune cell infiltration. Significant overexpression of immune checkpoint genes occurred in the high-risk group. In preliminary experiments, we confirmed that the occurrence of ferroptosis can reduce three prognostic gene signature expression. Conclusions  The three ferroptosis-related genes could predict the LUSC prognostic risk of antitumor immunity.

UNASSIGNED: To investigate the impact that TTN mutation had on the gene heterogeneity expression and prognosis in patients with lung adenocarcinoma.
UNASSIGNED: In this study, the Cancer Genome Atlas (TCGA) dataset was used to analyze the TTN mutations in lung adenocarcinoma. Lung adenocarcinoma data was collected from the TCGA database, clinical information of patients was analyzed, and bioinformatics statistical methods were applied for mutation analysis and prognosis survival analysis. The results were verified using the GEO dataset.
UNASSIGNED: The incidence of TTN mutations in lung adenocarcinoma was found to be 73%, and it was related to the prognosis of lung adenocarcinoma. Ten genes were screened with significant contributions to prognosis. A prognosis model was constructed and verified by LASSO COX analysis in the TCGA and GEO datasets based on these ten beneficial factors. The independent prognostic factor H2BC9 for TTN mutation-driven gene heterogeneity expression was screened through multi-factor COX regression analysis.
UNASSIGNED: Our data showed that the gene heterogeneity expression, which was driven by TTN mutations, prolonged the survival of lung adenocarcinoma patients and provided valuable clues for the prognosis of TTN gene mutations in lung adenocarcinoma.

There is still no ideal predictive biomarker for immunotherapy response among patients with non-small cell lung cancer. Costimulatory molecules play a role in anti-tumor immune response. Hence, they can be a potential biomarker for immunotherapy response. The current study comprehensively investigated the expression of costimulatory molecules in lung squamous carcinoma (LUSC) and identified diagnostic biomarkers for immunotherapy response. The costimulatory molecule gene expression profiles of 627 patients were obtained from the The Cancer Genome Atlas, GSE73403, and GSE37745 datasets. Patients were divided into different clusters using the k-means clustering method and were further classified into two discrepant tumor microenvironment (TIME) subclasses (hot and cold tumors) according to the immune score of the ESTIMATE algorithm. A high proportion of activated immune cells, including activated memory CD4 T cells, CD8 T cells, and M1 macrophages. Five CMGs (FAS, TNFRSF14, TNFRSF17, TNFRSF1B, and TNFSF13B) were considered as diagnostic markers using the Least Absolute Shrinkage and Selection Operator and the Support Vector Machine-Recursive Feature Elimination machine learning algorithms. Based on the five CMGs, a diagnostic nomogram for predicting individual tumor immune microenvironment subclasses in the TCGA dataset was developed, and its predictive performance was validated using GSE73403 and GSE37745 datasets. The predictive accuracy of the diagnostic nomogram was satisfactory in all three datasets. Therefore, it can be used to identify patients who may benefit more from immunotherapy.

At present, immunotherapy is a very promising treatment method for lung cancer patients, while the factors affecting response are still controversial. It is crucial to predict the efficacy of lung squamous carcinoma patients who received immunotherapy.
In our retrospective study, we enrolled lung squamous carcinoma patients who received immunotherapy at Beijing Chest Hospital from January 2017 to November 2021. All patients were grouped into two cohorts randomly, the training cohort (80% of the total) and the test cohort (20% of the total). The training cohort was used to build neural network models to assess the efficacy and outcome of immunotherapy in lung squamous carcinoma based on clinical information. The main outcome was the disease control rate (DCR), and then the secondary outcomes were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS).
A total of 289 patients were included in this study. The DCR model had area under the receiver operating characteristic curve (AUC) value of 0.9526 (95%CI, 0.9088-0.9879) in internal validation and 0.9491 (95%CI, 0.8704-1.0000) in external validation. The ORR model had AUC of 0.8030 (95%CI, 0.7437-0.8545) in internal validation and 0.7040 (95%CI, 0.5457-0.8379) in external validation. The PFS model had AUC of 0.8531 (95%CI, 0.8024-0.8975) in internal validation and 0.7602 (95%CI, 0.6236-0.8733) in external validation. The OS model had AUC of 0.8006 (95%CI, 0.7995-0.8017) in internal validation and 0.7382 (95%CI, 0.7366-0.7398) in external validation.
The neural network models show benefits in the efficacy evaluation of immunotherapy to lung squamous carcinoma patients, especially the DCR and ORR models. In our retrospective study, we found that neoadjuvant and adjuvant immunotherapy may bring greater efficacy benefits to patients.