The membrane-bound and soluble form of melanotransferrin function independently in the diagnosis and targeted therapy of lung cancer

Plasma cell signatures predict prognosis and treatment efficacy for lung adenocarcinoma

PURPOSE

This study aims to identify key genes regulating tumor infiltrating plasma cells (PC) and provide new insights for innovative immunotherapy.

METHODS

Key genes related to PC were identified using machine learning in lung adenocarcinoma (LUAD) patients. A prognostic model called PC scores was developed using TCGA data and validated with GEO cohorts. We assessed the molecular background, immune features, and drug sensitivity of the high PC scores group. Real-time PCR was utilized to assess the expression of hub genes in both localized LUAD patients and LUAD cell lines.

RESULTS

We constructed PC scores based on seventeen PC-related hub genes (ELOVL6, MFI2, FURIN, DOK1, ERO1LB, CLEC7A, ZNF431, KIAA1324, NUCB2, TXNDC11, ICAM3, CR2, CLIC6, CARNS1, P2RY13, KLF15, and SLC24A4). Higher age, TNM stage, and PC scores independently predicted shorter overall survival. The AUC value of PC scores for one year, three years, and five years of overall survival were 0.713, 0.716, and 0.690, separately. The nomogram model that integrated age, stage, and PC scores showed significantly higher predictive value than stage alone (P < 0.01). High PC scores group exhibited an immune suppressing microenvironment with lower B, CD8 + T, CD4 + T, and dendritic cell infiltration. Docetaxel, gefitinib, and erlotinib had lower IC50 in high PC groups (P < 0.001). After validation through the local cohort and in vitro experiments, we ultimately confirmed three key potential targets: MFI2, KLF15, and CLEC7A.

CONCLUSION

We proposed a prediction mode which can effectively identify high-risk LUAD patients and found three novel genes closely correlated with PC tumor infiltration.

Dexamethasone Pretreatment Potentiates a Folic Acid-Functionalized Delivery System for Enhanced Lung Cancer Therapy

Folic acid (FA) has been widely engineered to promote the targeted delivery of FA-modified nanoparticles (NPs) by recognizing the folate receptor α (FRα). However, the efficacy of FA-targeted therapy significantly varied with the abundance of FRα and natural immunoglobulin levels in different tumors. Therefore, a sequential therapy of dexamethasone (Dex)-induced FRα amplification and immunosuppression combined with FA-functionalized doxorubicin (DOX) micelles to synergistically suppress tumor proliferation was proposed in this study. In brief, a pH/reduction-responsive FA-functionalized micelle (FCSD) was obtained by grafting FA, derivatization-modified cholesterol, and 2,3-dimethylmaleic anhydride onto a chitosan oligosaccharide. The obtained FCSD/DOX NPs can effectively deliver DOX in tumors, and their targeting efficiency can be further improved with Dex pretreatment to decrease the immunoglobulin M (IgM) content in serum and amplify FRα levels on the surface of M109 cells. After internalization, charge reversal and disulfide bond breakage of FCSD vectors under the stimulation of tumor extracellular pH (pHe) and intracellular glutathione (GSH) would contribute to the disintegration of vectors and the rapid release of DOX. The sequential therapy that combined Dex pretreatment and targeted chemotherapy by FCSD/DOX NPs demonstrated superior tumor suppression compared with monotherapy, which is expected to provide a potential strategy for FRα-positive lung cancer patients.

Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosis

Background

Lung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively.

Materials and methods

LUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay.

Results

A total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues.

Conclusion

Immune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients.

The E2F1/MELTF axis fosters the progression of lung adenocarcinoma by regulating the Notch signaling pathway

BACKGROUND

Lung adenocarcinoma (LUAD) represents the predominant subtype of lung cancer. MELTF, an oncogene, exhibits high expression in various cancer tissues. Nevertheless, the precise role of MELTF in the progression of LUAD remains enigmatic. This work was devised to investigate the effect of MELTF on LUAD progression and its underlying mechanism.

METHODS

mRNA expression data of LUAD were from The Cancer Genome Atlas database, and the enrichment pathway of MELTF was analyzed. The upstream transcription factors of MELTF were predicted, and the correlation between MELTF and E2F1 as well as the expression of the two in LUAD tissues were dissected by bioinformatics. The expression of MELTF and E2F1 in LUAD tissues and cells was assayed by qRT-PCR. Effects of MELTF/E2F1 on proliferation, migration, and invasion of LUAD cells were tested by CCK-8, colony formation, and Transwell assays. The binding relationship between E2F1 and MELTF was estimated by dual-luciferase reporter gene assay and ChIP assay. Western blot was utilized to assay the expression of Notch signaling pathway-related proteins in different treatment groups.

RESULTS

Bioinformatics analysis and qRT-PCR results exhibited high expression of E2F1 and MELTF in LUAD tissues and cells, respectively. Dual-luciferase reporter gene assay and ChIP assay ascertained the binding of E2F1 to MELTF. MELTF was ascertained to enrich the Notch signaling pathway by bioinformatics means. In cell experiments, MELTF was shown to foster the malignant progression of LUAD cells and promoted the expression of NOTCH1 and HES1 proteins, but RO4929097 offset the effect of MELTF on cells. Rescue assay confirmed that E2F1 activated MELTF to promote LUAD progression via the Notch signaling pathway.

CONCLUSION

Together, our outcomes demonstrated that E2F1 fostered LUAD progression by activating MELTF via the Notch signaling activity. Hence, MELTF emerged as a feasible target for treating LUAD.

MFI2 upregulation promotes malignant progression through EGF/FAK signaling in oral cavity squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is the predominant histological type of the head and neck squamous cell carcinoma (HNSCC). By comparing the differentially expressed genes (DEGs) in OSCC-TCGA patients with copy number variations (CNVs) that we identify in OSCC-OncoScan dataset, we herein identified 37 dysregulated candidate genes. Among these potential candidate genes, 26 have been previously reported as dysregulated proteins or genes in HNSCC. Among 11 novel candidates, the overall survival analysis revealed that melanotransferrin (MFI2) is the most significant prognostic molecular in OSCC-TCGA patients. Another independent Taiwanese cohort confirmed that higher MFI2 transcript levels were significantly associated with poor prognosis. Mechanistically, we found that knockdown of MFI2 reduced cell viability, migration and invasion via modulating EGF/FAK signaling in OSCC cells. Collectively, our results support a mechanistic understanding of a novel role for MFI2 in promoting cell invasiveness in OSCC.

Tumor immune microenvironment of self-identified African American and non-African American triple negative breast cancer

Differences in the tumor immune microenvironment may result in differences in prognosis and response to treatment in cancer patients. We hypothesized that differences in the tumor immune microenvironment may exist between African American (AA) and NonAA patients, due to ancestry-related or socioeconomic factors, that may partially explain differences in clinical outcomes. We analyzed clinically matched triple-negative breast cancer (TNBC) tissues from self-identified AA and NonAA patients and found that stromal TILs, PD-L1 IHC-positivity, mRNA expression of immune-related pathways, and immunotherapy response predictive signatures were significantly higher in AA samples (p < 0.05; Fisher's Exact Test, Mann-Whitney Test, Permutation Test). Cancer biology and metabolism pathways, TAM-M2, and Immune Exclusion were significantly higher in NonAA samples (p < 0.05; Permutation Test, Mann-Whitney Test). There were no differences in somatic tumor mutation burden. Overall, there is greater immune infiltration and inflammation in AA TNBC and these differences may impact response to immune checkpoint inhibitors and other therapeutic agents that modulate the immune microenvironment.

KRAS-G12D mutation drives immune suppression and the primary resistance of anti-PD-1/PD-L1 immunotherapy in non-small cell lung cancer

BACKGROUND

Although immune checkpoint inhibitors (ICIs) against programmed cell death protein 1 (PD-1) and its ligand PD-L1 have demonstrated potency towards treating patients with non-small cell lung carcinoma (NSCLC), the potential association between Kirsten rat sarcoma viral oncogene homolog (KRAS) oncogene substitutions and the efficacy of ICIs remains unclear. In this study, we aimed to find point mutations in the KRAS gene resistant to ICIs and elucidate resistance mechanism.

METHODS

The association between KRAS variant status and the efficacy of ICIs was explored with a clinical cohort (n = 74), and confirmed with a mouse model. In addition, the tumor immune microenvironment (TIME) of KRAS-mutant NSCLC, such as CD8⁺ tumor-infiltrating lymphocytes (TILs) and PD-L1 level, was investigated. Cell lines expressing classic KRAS substitutions were used to explore signaling pathway activation involved in the formation of TIME. Furthermore, interventions that improved TIME were developed to increase responsiveness to ICIs.

RESULTS

We observed the inferior efficacy of ICIs in KRAS-G12D-mutant NSCLC. Based upon transcriptome data and immunostaining results from KRAS-mutant NSCLC, KRAS-G12D point mutation negatively correlated with PD-L1 level and secretion of chemokines CXCL10/CXCL11 that led to a decrease in CD8⁺ TILs, which in turn yielded an immunosuppressive TIME. The analysis of cell lines overexpressing classic KRAS substitutions further revealed that KRAS-G12D mutation suppressed PD-L1 level via the P70S6K/PI3K/AKT axis and reduced CXCL10/CXCL11 levels by down-regulating high mobility group protein A2 (HMGA2) level. Notably, paclitaxel, a chemotherapeutic agent, upregulated HMGA2 level, and in turn, stimulated the secretion of CXCL10/CXCL11. Moreover, PD-L1 blockade combined with paclitaxel significantly suppressed tumor growth compared with PD-L1 inhibitor monotherapy in a mouse model with KRAS-G12D-mutant lung adenocarcinoma. Further analyses revealed that the combined treatment significantly enhanced the recruitment of CD8⁺ TILs via the up-regulation of CXCL10/CXCL11 levels. Results of clinical study also revealed the superior efficacy of chemo-immunotherapy in patients with KRAS-G12D-mutant NSCLC compared with ICI monotherapy.

CONCLUSIONS

Our study elucidated the molecular mechanism by which KRAS-G12D mutation drives immunosuppression and enhances resistance of ICIs in NSCLC. Importantly, our findings demonstrate that ICIs in combination with chemotherapy may be more effective in patients with KRAS-G12D-mutant NSCLC.