Cancer Testis Antigen, NOL4, Is an Immunogenic Antigen Specifically Expressed in Small-Cell Lung Cancer

Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis

Breast cancer has the highest diagnosis rate among all cancers. Tumor budding (TB) is recognized as a recent prognostic marker. Identifying genes specific to high-TB samples is crucial for hindering tumor progression and metastasis. In this study, we utilized an RNA sequencing technique, called TempO-Seq, to profile transcriptomic data from breast cancer samples, aiming to identify biomarkers for high-TB cases. Through differential expression analysis and mutual information, we identified seven genes (NOL4, STAR, C8G, NEIL1, SLC46A3, FRMD6, and SCARF2) that are potential biomarkers in breast cancer. To gain more relevant proteins, further investigation based on a protein-protein interaction network and the network diffusion technique revealed enrichment in the Hippo signaling and Wnt signaling pathways, promoting tumor initiation, invasion, and metastasis in several cancer types. In conclusion, these novel genes, recognized as overexpressed in high-TB samples, along with their associated pathways, offer promising therapeutic targets, thus advancing treatment and diagnosis for breast cancer.

Identification of Immune Subtypes and Candidate mRNA Vaccine Antigens in Small Cell Lung Cancer

BACKGROUND

Immune checkpoint inhibitors (ICIs) have demonstrated promising outcomes in small cell lung cancer (SCLC), but not all patients benefit from it. Thus, developing precise treatments for SCLC is a particularly urgent need. In our study, we constructed a novel phenotype for SCLC based on immune signatures.

METHODS

We clustered patients with SCLC hierarchically in 3 publicly available datasets according to the immune signatures. ESTIMATE and CIBERSORT algorithm were used to evaluate the components of the tumor microenvironment. Moreover, we identified potential mRNA vaccine antigens for patients with SCLC, and qRT-PCR were performed to detect the gene expression.

RESULTS

We identified 2 SCLC subtypes and named Immunity High (Immunity_H) and Immunity Low (Immunity_L). Meanwhile, we obtained generally consistent results by analyzing different datasets, suggesting that this classification was reliable. Immunity_H contained the higher number of immune cells and a better prognosis compared to Immunity_L. Gene-set enrichment analysis revealed that several immune-related pathways such as cytokine-cytokine receptor interaction, programmed cell death-Ligand 1 expression and programmed cell death-1 checkpoint pathway in cancer were hyperactivated in the Immunity_H. However, most of the pathways enriched in the Immunity_L were not associated with immunity. Furthermore, we identified 5 potential mRNA vaccine antigens of SCLC (NEK2, NOL4, RALYL, SH3GL2, and ZIC2), and they were expressed higher in Immunity_L, it indicated that Immunity_L maybe more suitable for tumor vaccine development.

CONCLUSIONS

SCLC can be divided into Immunity_H and Immunity_L subtypes. Immunity_H may be more suitable for treatment with ICIs. NEK2, NOL4, RALYL, SH3GL2, and ZIC2 may be act as potential antigens for SCLC.

Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution

Chromatin accessibility is a generic property of the eukaryotic genome, which refers to the degree of physical compaction of chromatin. Recent studies have shown that chromatin accessibility is cell type dependent, indicating chromatin heterogeneity across cell lines and tissues. The identification of markers used to distinguish cell types at the chromosome level is important to understand cell function and classify cell types. In the present study, we investigated transcriptionally active chromosome segments identified by sci-ATAC-seq at single-cell resolution, including 69,015 cells belonging to 77 different cell types. Each cell was represented by existence status on 20,783 genes that were obtained from 436,206 active chromosome segments. The gene features were deeply analyzed by Boruta, resulting in 3897 genes, which were ranked in a list by Monte Carlo feature selection. Such list was further analyzed by incremental feature selection (IFS) method, yielding essential genes, classification rules and an efficient random forest (RF) classifier. To improve the performance of the optimal RF classifier, its features were further processed by autoencoder, light gradient boosting machine and IFS method. The final RF classifier with MCC of 0.838 was constructed. Some marker genes such as H2-Dmb2, which are specifically expressed in antigen-presenting cells (e.g., dendritic cells or macrophages), and Tenm2, which are specifically expressed in T cells, were identified in this study. Our analysis revealed numerous potential epigenetic modification patterns that are unique to particular cell types, thereby advancing knowledge of the critical functions of chromatin accessibility in cell processes.

A tumor-associated autoantibody panel for the detection of non-small cell lung cancer

Lung cancer is the second most frequent malignancy and the leading cause of cancer-associated death worldwide. Compared with patients diagnosed at advanced disease stages, early detection of lung cancer significantly improved the 5-year survival rate from 3.3% to 48.8%, which highlights the importance of early detection. Although multiple technologies have been applied to the screening and early diagnosis of lung cancer so far, some limitations still exist so they could not fully suit the needs for clinical application. Evidence show that autoantibodies targeting tumor-associated antigens(TAAs) could be found in the sera of early-stage patients, and they are of great value in diagnosis. Methods, we identified and screened TAAs in early-stage non-small cell lung cancer(NSCLC) samples using the serological analysis of recombinant cDNA expression libraries(SEREX). We measured the levels of the 36 autoantibodies targeting TAAs obtained by preliminary screening via liquid chip technique in the training set(332 serum samples from early-stage NSCLC patients, 167 samples from patients with benign lung lesions, and 208 samples from patients with no obvious abnormalities in lungs), and established a binary logistic regression model based on the levels of 8 autoantibodies to distinguish NSCLC samples. Results, We validated the diagnostic efficacy of this model in an independent test set(163 serum samples from early-stage NSCLC patients, and 183 samples from patients with benign lung lesions), the model performed well in distinguishing NSCLC samples with an AUC of 0.8194. After joining the levels of 4 serum tumor markers into its independent variables, the final model reached an AUC of 0.8568, this was better than just using the 8 autoantibodies (AUC:0.8194) or the 4 serum tumor markers alone(AUC: 0.6948). In conclusion, we screened and identified a set of autoantibodies in the sera of early-stage NSCLC patients through SEREX and liquid chip technique. Based on the levels of 8 autoantibodies, we established a binary logistic regression model that could diagnose early-stage NSCLC with high sensitivity and specificity, and the 4 conventional serum tumor markers were also suggested to be effective supplements for the 8 autoantibodies in the early diagnosis of NSCLC.

NOL4 is a novel nuclear marker of small cell carcinoma and other neuroendocrine neoplasms

Neuroendocrine neoplasms (NENs) such as small cell lung cancer (SCLC) and large cell neuroendocrine carcinoma (LCNEC) have characteristic histologies, but immunohistochemistry using neuroendocrine markers is still desirable to confirm diagnosis. CD56 is the most sensitive marker, but also stains various normal tissues and other tumors. Recently, we reported that nucleolar protein 4 (NOL4) is present in the blood of SCLC patients and found it was stained in the SCLC nuclei. In this study, we compared expressions of NOL4 and CD56, using 64 cases of SCLC, 18 cases of LCNEC, 6 cases of atypical carcinoid tumor, 7 cases of typical carcinoid tumor, 68 cases of lung adenocarcinoma, and 62 cases of lung squamous cell carcinoma. For primary lung NENs, sensitivity, specificity, positive predictive value (PPV) and negative predictive value of NOL4 were 77.5%, 95.8%, 93.2%, and 85.1%, respectively, while those of CD56 were 92.1%, 93.3%, 91.1%, and 94.1%. The specificity and PPV of NOL4 were higher than those of CD56, although the differences were not statistically significant. However, NOL4 retains its nuclear immunoreactivity in areas of crush artifact or necrosis. Furthermore, NOL4 was not expressed in adjacent normal tissues including bronchial cells and pneumocytes. Therefore, a combination of NOL4 staining with other cytoplasmic or membranous neuroendocrine markers might enhance diagnostic utility for SCLC and other NENs.

Cancer-testis antigen KK-LC-1 is a potential biomarker associated with immune cell infiltration in lung adenocarcinoma

Background

Cancer-testis antigens (CTAs) have emerged as potential clinical biomarkers targeting immunotherapy. KK-LC-1 is a member of CTAs, which has been demonstrated in a variety of tumors tissues and been found to elicit immune responses in cancer patients. However, the expression level and immune infiltration role of KK-LC-1 in lung adenocarcinoma (LUAD) remains to be elucidated.

Methods

In this study, the mRNA expression and overall survival rate of KK-LC-1 were evaluated by the TIMER and TCGA database in LUAD tissues and KK-LC-1 expression was further validated by clinical serum samples using quantitative RT-PCR. The relationship of KK-LC-1 with clinicopathologic parameters was analyzed. ROC curve result showed that miR-1825 was able to distinguish preoperative breast cancer patients from healthy people and postoperative patients. Then, the ROC curves were used to examine the ability of KK-LC-1 to distinguish preoperative LUAD patients from healthy and postoperative patients. The correlation between KK-LC-1 and infiltrating immune cells and immune marker sets was investigated via TIMER, TISIDB database, and CIBERSORT algorithm. The Kaplan-Meier plotter was used to further evaluate the prognostic value based on the expression levels of KK-LC-1 in related immune cells.

Results

The results showed that KK-LC-1 was significantly over-expressed in LUAD, and high levels of expression of KK-LC-1 were also closely correlated with poor overall survival. We also found that KK-LC-1 associated with TMN stage, NSE and CEA. The ROC curve result showed that KK-LC-1 was able to distinguish preoperative LUAD cancer patients from healthy people and postoperative patients. Moreover, KK-LC-1 had a larger AUC with higher diagnostic sensitivity and specificity than CEA. Based on the TIMER, TISIDB database, and CIBERSORT algorithm, the expression of KK-LC-1 was negatively correlated with CD4+ T cell, Macrophage, and Dendritic Cell in LUAD. Moreover, Based on the TIMER database, KK-LC-1 expression had a remarkable correlation with the type markers of Monocyte, TAM, M1 Macrophage, and M2 Macrophage. Furthermore, KK-LC-1 expression influenced the prognosis of LUAD patients by directly affecting immune cell infiltration by the Kaplan-Meier plotter analysis.

Conclusions

In conclusion, KK-LC-1 may serve as a promising diagnostic and prognostic biomarker in LUAD and correlate with immune infiltration and prognosis.

Comprehensive Transcriptomic Profiling of Murine Osteoclast Differentiation Reveals Novel Differentially Expressed Genes and LncRNAs

Osteoclasts are the sole bone resorbing cells, which undertake opposing roles to osteoblasts to affect skeletal mass and structure. However, unraveling the comprehensive molecular mechanisms behind osteoclast differentiation is necessitated to overcome limitations and scarcity of available data, particularly in relation with the emerging roles of long non-coding RNAs (LncRNAs) in gene expression. In this study, we performed comprehensive and progressive analyses of the dynamic transcriptomes of murine osteoclasts, generated in vitro. We compared the total RNA-based transcriptomes of murine bone marrow derived cells with differentiated osteoclasts, while focusing on potentially novel genes and LncRNAs, to uncover critical genes and their associated pathways, which are differentially regulated during osteoclast differentiation. We found 4,214 differentially regulated genes during osteoclast differentiation, which included various types of LncRNAs. Among the upregulated protein coding genes not previously associated with osteoclast are Pheta1, Hagh, Gfpt1 and Nol4, while downregulated genes included Plau, Ltf, Sell and Zfp831. Notably, we report Nol4 as a novel gene related to osteoclast activity since Nol4 knockout mice Nol4^{em1(International Mouse Phenotyping Consortium)J} exhibit increased bone mineral density. Moreover, the differentially expressed LncRNAs included antisense and long intergenic non-coding RNAs, among others. Overall, immune-related and metabolism-related genes were downregulated, while anatomical morphogenesis and remodeling-related genes were upregulated in early-differentiated osteoclasts with sustained downregulation of immune-related genes in mature osteoclasts. The gene signatures and the comprehensive transcriptome of osteoclast differentiation provided herein can serve as an invaluable resource for deciphering gene dysregulation in osteoclast-related pathologic conditions.