Prognostic Significance of HMGA1 Expression in Lung Cancer Based on Bioinformatics Analysis

Deciphering key roles of B cells in prognostication and tailored therapeutic strategies for lung adenocarcinoma: a multi-omics and machine learning approach towards predictive, preventive, and personalized treatment strategies

Background

Lung adenocarcinoma (LUAD) remains a significant global health challenge, with an urgent need for innovative predictive, preventive, and personalized medicine (PPPM) strategies to improve patient outcomes. This study leveraged multi-omics and machine learning approaches to uncover the prognostic roles of B cells in LUAD, thereby reinforcing the PPPM approach.

Methods

We integrated multi-omics data, including bulk RNA, ATAC-seq, single-cell RNA, and spatial transcriptomics sequencing, to characterize the B cell landscape in LUAD within the PPPM framework. Subsequently, we developed an integrative machine learning program that generated the Scissor+ related B cell score (SRBS). This score was validated in the training and validation sets, and its prognostic value was assessed along with clinical features to develop predictive nomograms. This study further assessed the role of SRBS and SRBS genes in response to immunotherapy and identified personalized drug targets for distinct risk subgroups, with gene expression verified experimentally to ensure tailored medical interventions.

Results

Our analysis identified 79 Scissor+ B cell genes linked to LUAD prognosis, supporting the predictive aspect of PPPM. The SRBS model, which utilizes multiple machine learning algorithms, performed excellently in predicting prognosis and clinical transformation, embodying the preventive and personalized aspects of PPPM. Multifactorial analysis confirmed that SRBS was an independent prognostic factor. We observed varying biological functions and immune cell infiltration in the tumor immune microenvironment (TIME) between the high- and low-SRBS groups, underscoring personalized treatment approaches. Notably, patients with elevated SRBS may exhibit resistance to immunotherapy but show increased sensitivity to chemotherapy and targeted therapies. Additionally, we found that LDHA, as an SRBS gene with significant clinical implications, may regulate the sensitivity of LUAD cells to cisplatin.

Conclusion

This study presents a B cell-associated gene signature that serves as a prognostic marker to facilitate personalized treatment for patients with LUAD, adhering to the principles of PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00390-4.

High mobility group A1 (HMGA1) promotes the tumorigenesis of colorectal cancer by increasing lipid synthesis

Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to meet the high energy and biosynthetic demands required for their proliferation. High mobility group A1 (HMGA1) is a structural transcription factor and frequently overexpressed in human colorectal cancer (CRC). Here, we show that HMGA1 promotes CRC progression by driving lipid synthesis in a AOM/DSS-induced CRC mouse model. Using conditional knockout (Hmga1△IEC) and knock-in (Hmga1IEC-OE/+) mouse models, we demonstrate that HMGA1 enhances CRC cell proliferation and accelerates tumor development by upregulating fatty acid synthase (FASN). Mechanistically, HMGA1 increases the transcriptional activity of sterol regulatory element-binding protein 1 (SREBP1) on the FASN promoter, leading to increased lipid accumulation in intestinal epithelial cells. Moreover, a high-fat diet exacerbates CRC progression in Hmga1△IEC mice, while pharmacological inhibition of FASN by orlistat reduces tumor growth in Hmga1IEC-OE/+ mice. Our findings suggest that targeting lipid metabolism could offer a promising therapeutic strategy for CRC.

The GJB3 correlates with the prognosis, immune cell infiltration, and therapeutic responses in lung adenocarcinoma

Gap junction protein beta 3 (GJB3) has been reported as a tumor suppressor in most tumors. However, its role in lung adenocarcinoma (LUAD) remains unknown. The purpose of this study is to explore the role of GJB3 in the prognosis and tumor microenvironment of LUAD patients. The data used in this study were acquired from The Cancer Genome Atlas, Gene Expression Omnibus, and imvigor210 cohorts. We found that GJB3 expression was increased in LUAD patients and correlated with LUAD stages. LUAD patients with high GJB3 expression exhibited a worse prognosis. A total of 164 pathways were significantly activated in the GJB3 high group. GJB3 expression was positively associated with nine transcription factors and might be negatively regulated by hsa-miR-6511b-5p. Finally, we found that immune cell infiltration and immune checkpoint expression were different between the GJB3 high and GJB3 low groups. In summary. GJB3 demonstrated high expression levels in LUAD patients, and those with elevated GJB3 expression displayed unfavorable prognoses. Additionally, there was a correlation between GJB3 and immune cell infiltration, as well as immune checkpoint expression in LUAD patients.

Exploring diverse programmed cell-death patterns to develop a novel gene signature for predicting the prognosis of lung adenocarcinoma patients

Background

Programmed cell death (PCD) plays a critical role in tumor progression and malignancy, and exploring its relationship with lung adenocarcinoma (LUAD)'s survival outcomes is important for personalized diagnosis and treatment. This study aimed to identify survival-related genes and construct an effective prognostic indicator for LUAD based on 12 forms of PCD.

Methods

A total of 1,933 candidate genes related to PCD were collected from published studies and public data center. A prognostic gene signature, called the cell death index (CDI), was established based on RNA-Seq and immunohistochemistry (IHC). IHC staining on tissue microarray was applied for the validation of protein level. Moreover, GSE42127, GSE72094 were used as validation datasets.

Results

The CDI based on expression level of nine genes (CCNB2, HMGA1, CACNA2D2, BUB1B, BTG2, KIF14, PTGDS, SERPINB5, BRCA1) was highly predictive for overall survival (OS) of LUAD in our cohort [36-month area under the curve (AUC): 0.750, 60-month AUC: 0.809]. The CDI was further validated in independent cohorts (GSE72094, 36-month AUC: 0.717, 60-month AUC: 0.737; GSE42127, 12-month AUC: 0.829, 60-month AUC: 0.663). And the CDI was found to be an independent prognostic factor after adjusting for other clinical characteristics. Furthermore, the high-CDI group was associated with upregulated tumor immune infiltration compared to the low-CDI group.

Conclusions

This study identified a 9-gene signature (CDI) based on PCD-related genes that accurately predicted the prognosis of LUAD patients. The CDI could serve as a valuable prognostic indicator and guide personalized therapeutic strategies for LUAD.

Identification of Novel Prognostic Long Non-coding RNAs in Lung Adenocarcinoma Using WGCNA Analysis

Long non-coding RNAs play crucial role in the tumorigenesis of lung adenocarcinoma (LUAD). However, the function of a large number of lncRNAs in LUAD has not been investigated yet. Weighted gene correlation network analysis (WGCNA) was applied to construct the co-expression module in the TCGA-LUAD cohort. Protein-protein interaction (PPI) network was used to explore the relationship of genes in the key module. The function of the key module on the prognosis in LUAD was analyzed using GO and KEGG analysis. Finally, we constructed the mRNA-lncRNA co-expression network in the key module to identify the hub lncRNAs that play crucial role in the prognosis in LUAD. The most highly expressed 2500 mRNAs and 2500 lncRNAs in the TCGA-LUAD cohort were clustered into 21 modules. After analyzing the correlation between the module and prognostic clinical traits, the Tan module, consisting of 130 genes, was selected as the key module on the prognosis in LUAD. And then, we found that genes in the key module were majorly enriched in ten multiple signaling pathways. Subsequently, we constructed the mRNA-lncRNA co-expression network based on the genes in the key module. Finally, we identified three lncRNAs and nineteen mRNAs that could be the promising prognostic biomarkers for LUAD. We identified three lncRNAs (MIR99AHG, ADAMTS9-AS2, and AC037459.2) and nineteen mRNAs as potential prognostic biomarkers in LUAD, which provided new insight for prognosis monitoring and therapy development in LUAD.

Single-cell RNA sequencing reveals the heterogeneity of epithelial cell and fibroblast cells from non- to metastatic lymph node OTSCC

Lymph node metastasis (LNM) is one of the common features of oral tongue squamous cell carcinoma (OTSCC). LNM is also taken as a sign of advanced OTSCC and poor survival rate. Recently, single-cell RNA sequencing has been applied in investigating the heterogeneity of tumor microenvironment and discovering the potential biomarkers for helping the diagnosis and prognosticating. Pathogenesis of LNM in OTSCC remains unknown. Specifically, cancer-associated fibroblasts (CAFs) and epithelial tumor cells could foster the progression of tumors. Thus, in this study, we aimed to comprehensively analyze the roles of subpopulations of CAFs and epithelial tumor cells in lymph node metastatic OTSCC using the integration of OTSCC single-cell RNA sequencing datasets. Four distinct subtypes of CAFs, namely vascular CAFs, myofibroblast CAFs, inflammatory CAFs, and growth arrest CAFs were successfully discovered in LNM tumor and confirmed the roles of GAS and PTN pathways in the progression of tumor metastasis. In addition, NKAIN2+ epithelial cells and FN1+ epithelial cells specifically exhibited an upregulation of PTN, NRG, MIF, and SPP1 signaling pathways in the metastatic OTSCC. In doing so, we put forth some potential biomarkers that could be utilized for the purpose of diagnosing and prognosticating OTSCC during its metastatic phase and tried to confirm by immunofluorescence assays.

m6A-mediated lncRNA NEAT1 plays an oncogenic role in non-small cell lung cancer by upregulating the HMGA1 expression through binding miR-361-3p

BACKGROUND

Lung cancer is the most common primary malignant tumor of the lung, and 85% of lung cancer is non-small cell lung cancer (NSCLC). The N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) have been widely reported to participate in the development of non-small cell lung cancer.

OBJECTIVE

However, the potential molecular mechanisms of m6A-regulated lncRNAs in NSCLC still need further investigation.

METHODS

The expression levels and the role of lncRNA NEAT1 in NSCLC tissues or cells were measured by RT-qPCR, Western blot, cell counting kit 8 (CCK-8), flow cytometry assay. RNA immunoprecipitation (RIP) was used to measure the levels of m6A modification of NEAT1. Bioinformatics analysis and dual-luciferase reporter gene assay were detected the relationship between miR-361-3p and NEAT1/HMGA1. Mouse xenograft tumor models were established to confirm the effects of lncRNA NEAT1 in vivo.

RESULTS

In this study, we verified whether m6A-modified lncRNA nuclear enriched abundant transcript 1 (NEAT1) is involved in NSCLC progression via miR-361-3p/HMGA1 axis. Firstly, we found that lncRNA NEAT1 was upregulated in NSCLC, and was associated with a poor survival in NSCLC patients. Methyltransferase like 3 (METTL3)-mediated m6A modification stabilized and upregulated NEAT1 expression. Next, function experiment indicated that depletion of METTL3 and NEAT1 induced cell apoptosis and inhibited cell proliferation, epithelial-mesenchymal transition (EMT). Likewise, in vivo experiments further supported the oncogenic role of NEAT1 in NSCLC. In addition, the molecular mechanism was uncovered in our study, and we found that lncRNA NEAT1 promoted the expression of high-mobility group AT-hook 1 (HMGA1) by sponging miR-361-3p and then promoted tumorigenesis of NSCLC.

CONCLUSION

In conclusion, our findings demonstrated that METTL3-mediated m6A modification accelerated NSCLC progression by regulating the NEAT1/miR-361-3p/HMGA1 axis, which provides important targets for the treatment of NSCLC.

LMNB1 targets FOXD1 to promote progression of prostate cancer

Forkhead box D1 (FOXD1) expression is upregulated in various types of human cancer. To the best of our knowledge, the roles of FOXD1 in prostate cancer (PC) remain largely unknown. The Cancer Genome Atlas dataset was used for the bioinformatics analysis of FOXD1 in PC. FOXD1 expression levels in normal immortalized human prostate epithelial cells (RWPE-1) and prostate cancer cells were detected by reverse transcription-quantitative PCR. PC cell viability was detected using Cell Counting Kit-8 assay. Transwell assays were performed to assess the migration and invasion of PC cells. Luciferase reporter gene assay was used to validate the association between FOXD1 and lamin (LMN)B1. LMNB1 is an important part of the cytoskeleton, which serves an important role in the process of tumor occurrence and development, regulating apoptosis and DNA repair. FOXD1 expression was upregulated in PC tissues, with its high expression being associated with clinical stage and survival in PC. Knockdown of FOXD1 inhibited viability, migration and invasion of PC cells. FOXD1 positively regulated LMNB1 expression. The effect of FOXD1 knockdown on PC cells was reversed by LMNB1 overexpression. In conclusion, FOXD1, positively regulated by LMNB1, served as an oncogene in PC and may be a potential biomarker and treatment target for PC.

AP-2δ Is the Most Relevant Target of AP-2 Family-Focused Cancer Therapy and Affects Genome Organization

Formerly hailed as "undruggable" proteins, transcription factors (TFs) are now under investigation for targeted therapy. In cancer, this may alter, inter alia, immune evasion or replicative immortality, which are implicated in genome organization, a process that accompanies multi-step tumorigenesis and which frequently develops in a non-random manner. Still, targeting-related research on some TFs is scarce, e.g., among AP-2 proteins, which are known for their altered functionality in cancer and prognostic importance. Using public repositories, bioinformatics tools, and RNA-seq data, the present study examined the ligandability of all AP-2 members, selecting the best one, which was investigated in terms of mutations, targets, co-activators, correlated genes, and impact on genome organization. AP-2 proteins were found to have the conserved "TF_AP-2" domain, but manifested different binding characteristics and evolution. Among them, AP-2δ has not only the highest number of post-translational modifications and extended strands but also contains a specific histidine-rich region and cleft that can receive a ligand. Uterine, colon, lung, and stomach tumors are most susceptible to AP-2δ mutations, which also co-depend with cancer hallmark genes and drug targets. Considering AP-2δ targets, some of them were located proximally in the spatial genome or served as co-factors of the genes regulated by AP-2δ. Correlation and functional analyses suggested that AP-2δ affects various processes, including genome organization, via its targets; this has been eventually verified in lung adenocarcinoma using expression and immunohistochemistry data of chromosomal conformation-related genes. In conclusion, AP-2δ affects chromosomal conformation and is the most appropriate target for cancer therapy focused on the AP-2 family.

HMGA1 gene expression level in cancer tissue and blood samples of non-small cell lung cancer (NSCLC) patients: preliminary report

The study aimed to assess the HMGA1 gene expression level in NSCLC patients and to evaluate its association with selected clinicopathological features and overall survival of patients. The expression of the HMGA1, coding non-histone transcription regulator HMGA1, was previously proved to correlate with the ability of cancer cells to metastasize the advancement of the disease. The prognostic value of the HMGA1 expression level was demonstrated in some neoplasms, e.g., pancreatic, gastric, endometrial, hepatocellular cancer, but the knowledge about its role in non-small cell lung cancer (NSCLC) is still limited. Thus, the HMGA1 expression level was evaluated by real-time PCR method in postoperative tumor tissue and blood samples collected at the time of diagnosis, 100 days and 1 year after surgery from 47 NSCLC patients. Mean HMGA1 expression level in blood decreased systematically from the time of cancer diagnosis to 1 year after surgery. The blood HMGA1 expression level 1 year after surgery was associated with the tobacco smoking status of patients (p= 0.0230). Patients with high blood HMGA1 expression levels measured 100 days after surgery tend to have worse overall survival than those with low expression levels (p= 0.1197). Tumor HMGA1 expression level was associated with neither features nor the overall survival of NSCLC patients. Moreover, no correlation between HMGA1 expression level measured in tumor tissue and blood samples was stated. Blood HMGA1 mRNA level could be a promising factor in the prognostication of non-small cell lung cancer patients.

Integrated single-cell and transcriptome sequencing analyses determines a chromatin regulator-based signature for evaluating prognosis in lung adenocarcinoma

BACKGROUND

Accumulating evidence has highlighted the significance of chromatin regulator (CR) in pathogenesis and progression of cancer. However, the prognostic role of CRs in LUAD remains obscure. We aim to detect the prognostic value of CRs in LUAD and create favorable signature for assessing prognosis and clinical value of LUAD patients.

METHODS

The mRNA sequencing data and clinical information were obtained from TCGA and GEO databases. Gene consensus clustering analysis was utilized to determine the molecular subtype of LUAD. Cox regression methods were employed to set up the CRs-based signature (CRBS) for evaluating survival rate in LUAD. Biological function and signaling pathways were identified by KEGG and GSEA analyses. In addition, we calculated the infiltration level of immunocyte by CIBERSORT algorithm. The expressions of model hub genes were detected in LUAD cell lines by real-time polymerase chain reaction (PCR).

RESULTS

KEGG analysis suggested the CRs were mainly involved in histone modification, nuclear division and DNA modification. Consensus clustering analysis identified a novel CRs-associated subtype which divided the combined LUAD cohort into two clusters (C1 = 217 and C2 = 296). We noticed that a remarkable discrepancy in survival rate among two clusters. Then, a total of 120 differentially expressed CRs were enrolled into stepwise Cox analyses. Four hub CRs (CBX7, HMGA2, NPAS2 and PRC1) were selected to create a risk signature which could accurately forecast patient outcomes and differentiate patient risk. GSEA unearthed that mTORC1 pathway, PI3K/Akt/mTOR and p53 pathway were greatly enriched in CRBS-high cohort. Moreover, the infiltration percentages of macrophage M0, macrophage M2, resting NK cells, memory B cells, dendritic cells and mast cells were statistically significantly different in the two groups. PCR assay confirmed the differential expression of four model biomarkers.

CONCLUSIONS

Altogether, our project developed a robust risk signature based on CRs and offered novel insights into individualized treatment for LUAD cases.