Identification of specific prognostic markers for lung squamous cell carcinoma based on tumor progression, immune infiltration, and stem index

LSM12 promotes the lung squamous cell carcinoma progression through mediating alternative splicing of ARRB1

Like-Smith protein 12 (LSM12), an RNA-binding protein, is highly expressed in tumor tissues of patients with lung squamous cell carcinoma (LUSC). However, the role of LSM12 in LUSC is unclear. In this study, overexpression of LSM12 promotes the proliferation, migration, and invasion and prevents the apoptosis of LUSC cells. In vivo, LSM12 accelerates the tumor growth and metastasis of LUSC cells using male BALB/c nude mice. Furthermore, we find that the Sterile alpha motif domain containing 4A (SAMD4A) is directly bound to the mRNA of LSM12 and accelerates the mRNA degradation. High-throughput omics analysis is performed to identify the potential target genes of LSM12 in LUSC cells. LSM12 regulates alternative splicing events and increases exon 13 skipped splicing of ARRB1 and mRNA expression. Our findings may provide fundamental research for the investigation of the development of LUSC and the potential role of LSM12 in LUSC cells.

Integrative machine learning model for subtype identification and prognostic prediction in lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is a leading cause of cancer-related mortality, and tumor heterogeneity could result in diverse prognostic subtypes. Traditional prognostic factors, like tumor, node, and metastasis (TNM) staging, offer limited predictive accuracy. This study aims to identify LUSC subtypes and develop predictive models that have the potential to improve prognosis prediction accuracy and support personalized treatment.

METHODS

Expression and clinical data were collected from three datasets. One dataset (TCGA-LUSC) was used as a training set, while the others (GSE30219 and GSE73403) were independent testing sets. Unsupervised clustering was applied to the training set to identify LUSC subtypes. The relationship between survival outcomes and these identified subtypes was validated in the testing sets using binary machine learning models and survival curve analysis. The impact of chemotherapy on the prognosis for subtypes was also presented. Subsequently, four survival machine learning models were developed to predict LUSC prognosis. These models were validated in the testing sets and integrated into an online tool to assist in survival prediction.

RESULTS

Two subtypes, C1 and C2, were identified in the training set. The C1 subtype was associated with poorer survival outcomes and was enriched in cancer-associated fibroblasts and macrophages. In contrast, the C2 subtype correlated with better outcomes and was enriched in CD8 + T cells. Regarding chemotherapy, the C2 subtype with chemotherapy showed the best survival outcomes compared to other groups. A 9-gene signature was derived from the model's importance values for subtype prediction and included TGM2, AOC3, TBXA2R, RGS3, DLC1, MMP19, ACVRL1, TCF21, and TIMP3. This signature outperformed 14 published signatures and clinical variables at survival prediction with the highest time-dependent AUC (tdAUC) and concordance index (C-index). Four machine learning models were developed using this signature, achieving tdAUC values of 0.712 and 0.684 and C-index values of 0.682 and 0.625 in the independent testing sets. An online tool for predicting survival probabilities for LUSC patients up to 10 years post-treatment is available at https://hznuduan.shinyapps.io/LCSP/ .

CONCLUSION

We identified two LUSC subtypes by unsupervised clustering and developed an online tool for prognosis prediction using supervised machine learning models.

Bioinformatics Analysis Reveals HIST1H2BH as a Novel Diagnostic Biomarker for Atrial Fibrillation-Related Cardiogenic Thromboembolic Stroke

Atrial fibrillation (AF) is a significant precursor to cerebral embolism. Our study sought to unearth new diagnostic biomarkers for atrial fibrillation-related cerebral embolism (AF-CE) by meticulously examining multiple GEO datasets and meta-analysis. The gene expression omnibus (GEO) database provided RNA sequencing data associated with AF and stroke. We began by pinpointing genes with varied expressions in AF-CE patient blood samples. A meta-analysis was subsequently undertaken using several RNA sequencing datasets to verify these genes. LASSO regression discerned key genes for AF-CE, with their diagnostic prowess verified through ROC curve examination. Active signaling pathways within stroke patients were discerned via GO and KEGG enrichment, with PPI interactions detailing gene interplay. Differential gene analysis revealed an upregulation of sixteen genes and a downregulation of four in stroke patient blood samples. Eight genes showcased varied expression in the meta-analysis. LASSO regression zeroed in on five of these, culminating in HIST1H2BH's identification as a characteristic gene. HIST1H2BH's prowess in predicting AF-CE was confirmed through ROC. Integrin signaling, platelet activation, ECM interactions, and the PI3K-Akt pathway were found active in stroke victims. HIST1H2BH's interaction with the notably upregulated ITGA2B was spotlighted by PPI. Additionally, HIST1H2BH exhibited links with NK cells and eosinophils. HIST1H2BH emerges as an insightful diagnostic beacon for AF-CE. Its presence, post AF, potentially modulates pathways, accentuating platelet activation and consequent thrombus generation, leading to cerebral embolism.

AKAP12, mediated by transcription factor 21, inhibits cell proliferation, metastasis, and glycolysis in lung squamous cell carcinoma

A-kinase anchor protein 12 (AKAP12) has been reported to be related to lung squamous cell carcinoma (LUSC) progression. However, its role and molecular mechanisms in LUSC have not been revealed. The mRNA and protein levels of AKAP12 and transcription factor 21 (TCF21) were tested by quantitative real-time PCR and western blot. Cell counting kit 8 assay, EdU assay, flow cytometry, wound healing assay, and transwell assay were used to evaluate cell proliferation, apoptosis, migration, and invasion. Cell glycolysis was measured by testing glucose consumption and lactate production. The interaction between AKAP12 and TCF21 was assessed by ChIP assay and dual-luciferase reporter assay. A mice xenograft model was constructed to explore AKAP12 and TCF21 roles in vivo. Our data showed that AKAP12 was underexpressed in LUSC tissues and cells, and its overexpression inhibited LUSC cell growth, metastasis, and glycolysis. TCF21 had decreased expression in LUSC, which facilitated AKAP12 expression through binding to its promoter region to enhance its transcription. Furthermore, TCF21 increased AKAP12 expression to repress LUSC cell growth, metastasis, and glycolysis. In vivo experiments showed that AKAP12 upregulation reduced LUSC tumorigenesis, and TCF21 knockdown reversed this effect. In conclusion, AKAP12 might be a tumor suppressor in LUSC, which was mediated by TCF21 and could inhibit cell growth, metastasis, and glycolysis to restrain LUSC malignant progression.

Single-cell profiling and clinical characteristics analysis of lung squamous carcinoma

Lung squamous carcinoma (LUSC) is a highly heterogeneous disease. However, the tumor microenvironment (TME) landscape and clinical characteristics for LUSC have not yet been elucidated. To map the TME and clinical characteristics of LUSC, we performed single-cell RNA sequencing for 504 LUSC samples on basis of TCGA and Gene Expression Omnibus. We introduced the computational algorithms "ESTIMATE" and "CIBERSORT" to analyze immune cell infiltration and immune-checkpoint-related gene signatures in various LUSC clusters. Weighted gene co-expression network analysis was used to explore the connections between molecular characteristics and clinical traits in LUSC. A prognostic model was constructed by performing multivariate COX. Two gene clusters exhibiting disparate immune and clinical characteristics were identified. Our findings indicate that patients in cluster 2, who have a more favorable prognosis, exhibit immune characteristics such as elevated levels of immunosuppression-associated M2 macrophages, resting memory CD4 T cells, resting dendritic cells (DC), and TNFRSF4, alongside reduced infiltration of activated DC and lower expression of TNFRSF18.Whereafter, the Risk Score model was built on basis of 3-DEGs signature consisted of cystatin C (CST3), transglutaminase type 2 (TGM2), JUN, which were proved by q-PCR and immunofluorescence. Besides, high-Risk Score may be responsible for poor prognosis in LUSC patients. Our study identified that tumor-infiltrating immune cell subtypes and the Risk Score model might shed light on the heterogeneity in LUSC patients. The TME, three DEGs and Risk Score can effectively serve as biomarkers to elucidate the immune landscape and predict prognosis in LUSC patients. They may provide insights to the investigations on therapeutic strategies for LUSC.

A panel of cancer testis antigens in squamous cell carcinoma of the lung, head and neck, and esophagus: implication for biomarkers and therapeutic targets

This study aims to investigate the expression of seven cancer testis antigens (MAGE-A1, MAGE-A4, MAGE-A10, MAGE-A11, PRAME, NY-ESO-1 and KK-LC-1) in pan squamous cell carcinoma and their prognostic value, thus assessing the potential of these CTAs as immunotherapeutic targets. The protein expression of these CTAs was evaluated by immunohistochemistry in 60 lung squamous cell carcinoma (LUSC), 62 esophageal squamous cell carcinoma (ESCA) and 62 head and neck squamous cell carcinoma (HNSC). The relationship between CTAs expression and progression-free survival (PFS) was assessed. PD-L1 expression and tumor-infiltrating lymphocytes were also collected and correlated with CTAs expression. The prognostic impact of CTAs gene expression was evaluated using the Kaplan-Meier Plotter website. CTAs expression was 0-48% in ESCA, 3%-77% in LUSC, and 3%-71% in HNSC. Analysis of PFS showed that MAGE-A1 expression in HNSC (**p < 0.01), PRAME in LUSC (p = 0.008, **p < 0.01), MAGE-A10 (p = 0.012, *p < 0.05) and PRAME (p = 0.021, *p < 0.05) in ESCA were significantly correlated with PFS. In all three cancers, coexpression of three CTAs was used as a cutoff value for grouping, and the results showed a significant difference in PFS between these two groups. Moreover, CTAs expression was significantly correlated with PD-L1 expression and T cell infiltration. These findings indicate a high incidence of CTA expression in HNSC, LUSC and ESCA, which was correlated with PD-L1 expression, T cell infiltration, and tumor progression. The results suggest that cancer testis antigens could be feasible vaccine targets in squamous cell carcinoma.

Different types of tumor microvessels in stage I-IIIA squamous cell lung cancer and their clinical significance

BACKGROUND

Lung cancer (LC) is the leading cause of morbidity and mortality among malignant neoplasms. Improving the diagnosis and treatment of LC remains an urgent task of modern oncology. Previously, we established that in gastric, breast and cervical cancer, tumor microvessels (MVs) differ in morphology and have different prognostic significance. The connection between different types of tumor MVs and the progression of LC is not well understood.

AIM

To evaluate the morphological features and clinical significance of tumor MVs in lung squamous cell carcinoma (LUSC).

METHODS

A single-center retrospective cohort study examined medical records and archival paraffin blocks of 62 and 180 patients with stage I-IIIA LUSC in the training and main cohorts, respectively. All patients underwent radical surgery (R0) at the Orenburg Regional Cancer Clinic from May/20/2009 to December/14/2021. Tumor sections were routinely processed, and routine Mayer's hematoxylin and eosin staining and immunohistochemical staining for cluster of differentiation 34 (CD34), podoplanin, Snail and hypoxia-inducible factor-1 alpha were performed. The morphological features of different types of tumor MVs, tumor parenchyma and stroma were studied according to clinicopathological characteristics and LUSC prognosis. Statistical analysis was performed using Statistica 10.0 software. Univariate and multivariate logistic regression analyses were performed to identify potential risk factors for LUSC metastasis to regional lymph nodes (RLNs) and disease recurrence. Receiver operating characteristic curves were constructed to discriminate between patients with and without metastases in RLNs and those with and without disease recurrence. The effectiveness of the predictive models was assessed by the area under the curve. Survival was analyzed using the Kaplan-Meier method. The log-rank test was used to compare survival curves between patient subgroups. A value of P < 0.05 was considered to indicate statistical significance.

RESULTS

Depending on the morphology, we classified tumor vessels into the following types: normal MVs, dilated capillaries (DCs), atypical DCs, DCs with weak expression of CD34, "contact-type" DCs, structures with partial endothelial linings, capillaries in the tumor solid component and lymphatic vessels in lymphoid and polymorphocellular infiltrates. We also evaluated the presence of loose, fine fibrous connective tissue (LFFCT) and retraction clefts in the tumor stroma, tumor spread into the alveolar air spaces (AASs) and fragmentation of the tumor solid component. According to multivariate analysis, the independent predictors of LUSC metastasis in RLNs were central tumor location (P < 0.00001), the presence of retraction clefts (P = 0.003), capillaries in the tumor solid component (P = 0.023) and fragmentation in the tumor solid component (P = 0.009), whereas the independent predictors of LUSC recurrence were tumor grade 3 (G3) (P = 0.001), stage N2 (P = 0.016), the presence of LFFCT in the tumor stroma (P < 0.00001), fragmentation of the tumor solid component (P = 0.0001), and the absence of tumor spread through the AASs (P = 0.0083).

CONCLUSION

The results obtained confirm the correctness of our previously proposed classification of different types of tumor vessels and may contribute to improving the diagnosis and treatment of LUSC.

Development of m6A/m5C/m1A regulated lncRNA signature for prognostic prediction, personalized immune intervention and drug selection in LUAD

Research indicates that there are links between m6A, m5C and m1A modifications and the development of different types of tumours. However, it is not yet clear if these modifications are involved in the prognosis of LUAD. The TCGA-LUAD dataset was used as for signature training, while the validation cohort was created by amalgamating publicly accessible GEO datasets including GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081. The study focused on 33 genes that are regulated by m6A, m5C or m1A (mRG), which were used to form mRGs clusters and clusters of mRG differentially expressed genes clusters (mRG-DEG clusters). Our subsequent LASSO regression analysis trained the signature of m6A/m5C/m1A-related lncRNA (mRLncSig) using lncRNAs that exhibited differential expression among mRG-DEG clusters and had prognostic value. The model's accuracy underwent validation via Kaplan-Meier analysis, Cox regression, ROC analysis, tAUC evaluation, PCA examination and nomogram predictor validation. In evaluating the immunotherapeutic potential of the signature, we employed multiple bioinformatics algorithms and concepts through various analyses. These included seven newly developed immunoinformatic algorithms, as well as evaluations of TMB, TIDE and immune checkpoints. Additionally, we identified and validated promising agents that target the high-risk mRLncSig in LUAD. To validate the real-world expression pattern of mRLncSig, real-time PCR was carried out on human LUAD tissues. The signature's ability to perform in pan-cancer settings was also evaluated. The study created a 10-lncRNA signature, mRLncSig, which was validated to have prognostic power in the validation cohort. Real-time PCR was applied to verify the actual manifestation of each gene in the signature in the real world. Our immunotherapy analysis revealed an association between mRLncSig and immune status. mRLncSig was found to be closely linked to several checkpoints, such as IL10, IL2, CD40LG, SELP, BTLA and CD28, which could be appropriate immunotherapy targets for LUAD. Among the high-risk patients, our study identified 12 candidate drugs and verified gemcitabine as the most significant one that could target our signature and be effective in treating LUAD. Additionally, we discovered that some of the lncRNAs in mRLncSig could play a crucial role in certain cancer types, and thus, may require further attention in future studies. According to the findings of this study, the use of mRLncSig has the potential to aid in forecasting the prognosis of LUAD and could serve as a potential target for immunotherapy. Moreover, our signature may assist in identifying targets and therapeutic agents more effectively.