Comprehensive Bioinformatics Analysis Identifies Tumor Microenvironment and Immune-related Genes in Small Cell Lung Cancer

Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies

Background

Immunotherapy is an effective "precision medicine" treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma.

Methods

A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered: CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis.

Results

Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed.

Conclusions

Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.

Comprehensive analyses of N6 -methyladenosine-related long noncoding RNA profiles with prognosis, chemotherapy response, and immune landscape in small cell lung cancer

Small cell lung cancer (SCLC) is the most devastating subtype of lung cancer with no clinically available prognostic biomarkers. N⁶ -methyladenosine (m⁶ A) and noncoding RNAs play critical roles in cancer development and treatment response. However, little is known about m⁶ A-related long noncoding RNAs (lncRNAs) in SCLC. We used 206 limited-stage SCLC (LS-SCLC) samples from two cohorts to undertake the first and most comprehensive exploration of the m⁶ A-related lncRNA profile in SCLC and constructed a relevant prognostic signature. In total, 289 m⁶ A-related lncRNAs were screened out. We then built a seven-lncRNA-based signature in the training cohort with 48 RNA sequencing data using univariate and multivariate Cox regression models. The signature was well validated in an independent cohort containing 158 cases with quantitative PCR data. In both cohorts, the signature divided patients into high- and low-risk groups with significantly different survival rates (both p < 0.001). Our signature predicted chemotherapy survival benefit in patients with LS-SCLC. Receiver operating characteristic and C-index analyses indicated that the signature was better at predicting prognosis and chemotherapy benefit than other clinicopathologic features. Moreover, the signature was identified as an independent predictor of prognosis and chemotherapy response in different cohorts. Furthermore, functional analysis showed that multiple activated immune-related pathways were enriched in the low-risk group. Additionally, the signature was also closely related to various immune checkpoints and inflammatory responses. We generated the first clinically available m⁶ A-related lncRNA signature to predict prognosis and chemotherapy benefit in patients with LS-SCLC. Our findings could help optimize the clinical management of patients with LS-SCLC and inform future therapeutic targets for SCLC.

Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related deaths worldwide. Identification of gene biomarkers and their regulatory factors and signaling pathways is very essential to reveal the molecular mechanisms of NSCLC initiation and progression. Thus, the goal of this study is to identify gene biomarkers for NSCLC diagnosis and prognosis by using scRNA-seq data through bioinformatics techniques. scRNA-seq data were obtained from the GEO database to identify DEGs. A total of 158 DEGs (including 48 upregulated and 110 downregulated) were detected after gene integration. Gene Ontology enrichment and KEGG pathway analysis of DEGs were performed by FunRich software. A PPI network of DEGs was then constructed using the STRING database and visualized by Cytoscape software. We identified 12 key genes (KGs) including MS4A1, CCL5, and GZMB, by using two topological methods based on the PPI networking results. The diagnostic, expression, and prognostic potentials of the identified 12 key genes were assessed using the receiver operating characteristics (ROC) curve and a web-based tool, SurvExpress. From the regulatory network analysis, we extracted the 7 key transcription factors (TFs) (FOXC1, YY1, CEBPB, TFAP2A, SREBF2, RELA, and GATA2), and 8 key miRNAs (hsa-miR-124-3p, hsa-miR-34a-5p, hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-449a, hsa-miR-24-3p, hsa-let-7b-5p, and hsa-miR-7-5p) associated with the KGs were evaluated. Functional enrichment and pathway analysis, survival analysis, ROC analysis, and regulatory network analysis highlighted crucial roles of the key genes. Our findings might play a significant role as candidate biomarkers in NSCLC diagnosis and prognosis.

Dissecting Immunotherapy Strategies for Small Cell Lung Cancer: Antibodies, Ionizing Radiation and CAR-T

Small cell lung cancer (SCLC) is a highly aggressive malignancy that accounts for about 14% of all lung cancers. Platinum-based chemotherapy has been the only available treatment for a long time, until the introduction of immune checkpoint inhibitors (ICIs) recently changed first-line standard of care and shed light on the pivotal role of the immune system. Despite improved survival in a subset of patients, a lot of them still do not benefit from first-line chemo-immunotherapy, and several studies are investigating whether different combination strategies (with both systemic and local treatments, such as radiotherapy) may improve patient outcomes. Moreover, research of biomarkers that may be used to predict patients' outcomes is ongoing. In addition to ICIs, immunotherapy offers other different strategies, including naked monoclonal antibodies targeting tumor associated antigens, conjugated antibody, bispecific antibodies and cellular therapies. In this review, we summarize the main evidence available about the use of immunotherapy in SCLC, the rationale behind combination strategies and the studies that are currently ongoing in this setting, in order to give the reader a clear and complete view of this rapidly expanding topic.

Signal pathways and precision therapy of small-cell lung cancer

Small-cell lung cancer (SCLC) encounters up 15% of all lung cancers, and is characterized by a high rate of proliferation, a tendency for early metastasis and generally poor prognosis. Most of the patients present with distant metastatic disease at the time of clinical diagnosis, and only one-third are eligible for potentially curative treatment. Recently, investigations into the genomic make-up of SCLC show extensive chromosomal rearrangements, high mutational burden and loss-of-function mutations of several tumor suppressor genes. Although the clinical development of new treatments for SCLC has been limited in recent years, a better understanding of oncogenic driver alterations has found potential novel targets that might be suitable for therapeutic approaches. Currently, there are six types of potential treatable signaling pathways in SCLC, including signaling pathways targeting the cell cycle and DNA repair, tumor development, cell metabolism, epigenetic regulation, tumor immunity and angiogenesis. At this point, however, there is still a lack of understanding of their role in SCLC tumor biology and the promotion of cancer growth. Importantly optimizing drug targets, improving drug pharmacology, and identifying potential biomarkers are the main focus and further efforts are required to recognize patients who benefit most from novel therapies in development. This review will focus on the current learning on the signaling pathways, the status of immunotherapy, and targeted therapy in SCLC.

An immune-related lncRNA signature predicts prognosis and adjuvant chemotherapeutic response in patients with small-cell lung cancer

Background

Patients with small-cell lung cancer (SCLC) are burdened by limited treatment options and the disease’s dismal prognosis. Long non-coding RNAs (lncRNAs) are essential regulators of genetic alteration and are actively involved in tumor immunity. However, few studies have examined interactions between immune genes and lncRNAs in SCLC.

Methods

Immune-related lncRNA (irlncRNA) expression profiles and their clinical significance were explored. We enrolled 227 patients with SCLC, including 79 cases from GSE65002 and 148 cases from a validation cohort with corresponding qPCR data. The least absolute shrinkage and selection operator (LASSO) model was applied to identify prognostic irlncRNAs for an irlncRNA-based SCLC signature. We additionally investigated the potential mechanisms and immune landscape of the signature using bioinformatics methods.

Results

An irlncRNA signature including 8 irlncRNAs (ENOX1-AS1, AC005162, LINC00092, RPL34-AS1, AC104135, AC015971, AC126544, AP001189) was established for patients with SCLC in the training cohort. Low-risk patients were more likely to benefit from chemotherapy and achieve a favorable prognosis. The signature was also well-validated in the validation cohort and various clinical subgroups. Compared to other clinical parameters, the irlncRNA signature exhibited superior predictive performance for chemotherapy response and prognosis. The signature was as an independent prognostic factor in the training and validation cohorts. Interestingly, low-risk patients showed an activated immune phenotype.

Conclusion

We constructed the first irlncRNA-based signature for chemotherapy efficacy and outcome prediction. The irlncRNA signature is a reliable and robust prognostic classifier that could be useful for clinical management and determination of potential chemotherapy benefit for patients with SCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02357-1.

Identification of NSP3 (SH2D3C) as a Prognostic Biomarker of Tumor Progression and Immune Evasion for Lung Cancer and Evaluation of Organosulfur Compounds from Allium sativum L. as Therapeutic Candidates

The multi-domain non-structural protein 3 (NSP3) is an oncogenic molecule that has been concomitantly implicated in the progression of coronavirus infection. However, its oncological role in lung cancer and whether it plays a role in modulating the tumor immune microenvironment is not properly understood. In the present in silico study, we demonstrated that NSP3 (SH2D3C) is associated with advanced stage and poor prognoses of lung cancer cohorts. Genetic alterations of NSP3 (SH2D3C) co-occurred inversely with Epidermal Growth Factor Receptor (EGFR) alterations and elicited its pathological role via modulation of various components of the immune and inflammatory pathways in lung cancer. Our correlation analysis suggested that NSP3 (SH2D3C) promotes tumor immune evasion via dysfunctional T-cell phenotypes and T-cell exclusion mechanisms in lung cancer patients. NSP3 (SH2D3C) demonstrated a high predictive value and association with therapy resistance in lung cancer, hence serving as an attractive target for therapy exploration. We evaluated the in silico drug-likeness and NSP3 (SH2D3C) target efficacy of six organosulfur small molecules from Allium sativum using a molecular docking study. We found that the six organosulfur compounds demonstrated selective cytotoxic potential against cancer cell lines and good predictions for ADMET properties, drug-likeness, and safety profile. E-ajoene, alliin, diallyl sulfide, 2-vinyl-4H-1,3-dithiin, allicin, and S-allyl-cysteine docked well into the NSP3 (SH2D3C)-binding cavity with binding affinities ranging from -4.3~-6.70 Ă and random forest (RF) scores ranging from 4.31~5.26 pKd. However, S-allyl-cysteine interaction with NSP3 (SH2D3C) is unfavorable and hence less susceptible to NSP3 ligandability. In conclusion, our study revealed that NSP3 is an important onco-immunological biomarker encompassing the tumor microenvironment, disease staging and prognosis in lung cancer and could serve as an attractive target for cancer therapy. The organosulfur compounds from A. sativum have molecular properties to efficiently interact with the binding site of NSP3 and are currently under vigorous preclinical study in our laboratory.

Immune checkpoint inhibitors: a new landscape for extensive stage small cell lung cancer treatment

Introduction: Landscape of Extensive Stage (ES)-SCLC treatment has been unchanged over the years. Chemotherapy, mostly based on cisplatin and etoposide, remained the standard-of-care for patients with ES-SCLC for almost 40 years. Recently, immune check points inhibitors have emerged marking a turning point for ES-SCLC treatmentAreas covered: Aim of the paper is to discuss ICIs impact on ES-SCLC treatment algorithms, review current clinical trials, and explore future perspectives.Expert opinion: A growing body of evidence supports ICI-containing regimens as a new mainstay of ES-SCLC treatment. Whether subgroups of SCLC patients may have greater survival benefits from ICIs treatment needs to be better defined. Understanding the impact of tumor microenvironment and identifying reliable predictive and/or prognostic biomarkers will be fundamental to move toward a personalized treatment approach leading to improved survival.

An Immune Cell Signature Is Associated With Disease-Free Survival and Adjuvant Chemosensitivity of Patients With Resectable Gastric Cancer

Increasing evidence has indicated that current tumor-node-metastasis (TNM) stage alone cannot predict prognosis and adjuvant chemotherapy benefits accurately for stages II and III gastric cancer (GC) patients after surgery. In order to improve the predictive ability of survival and adjuvant chemotherapy benefits of GC patients after surgery, this study aimed to establish an immune signature based on the composition of infiltrating immune cells. Twenty-eight types of immune cell fractions were evaluated based on the expression profiles of GC patients from the Gene Expression Omnibus (GEO) database using single-sample gene set enrichment analysis (ssGSEA). The immunoscore (IS) was constructed using a least absolute shrinkage and selection operator (LASSO) Cox regression model. Through the LASSO model, an IS classifier consisting of eight immune cells was constructed. Significant difference was found between high-IS and low-IS groups in the training cohort in disease-free survival (DFS, P < 0.0001) and overall survival (OS, P < 0.0001). Multivariate analysis showed that the IS classifier was an independent prognostic indicator. Moreover, a combination of IS and TNM stage exhibited better prognostic value than TNM stage alone. Further analysis demonstrated that low-IS patients who had more tumor-infiltrating lymphocytes had better response to adjuvant chemotherapy. More importantly, we found that patients with high-IS were more likely to benefit from a Xeloda plus cisplatin regimen after surgery. Finally, we established two nomograms to screen the stage II and III GC patients who benefitted from adjuvant chemotherapy after surgery. The combination of IS classifier and TNM stage could predict DFS and OS of GC patients. The IS model has been proven as a promising tool that can be used to identify the patients with stages II and III GC who may benefit from adjuvant chemotherapy.

[Immunotherapy Advances in Small Cell Lung Cancer]

Small cell lung cancer (SCLC) is a "refractory cancer" characterized by rapid growth and extensive early metastasis. About 70% of patients are already in the extensive stage at the time of diagnosis. Despite the high response rate to platinum-contained first-line chemotherapy, almost all patients subsequently experienced inevitable recurrence and had poor response to second-line treatment. The high mutation load and immunogenicity of SCLC suggest that immunotherapy may be effective for SCLC patients. Over the past few years, several clinical trials have evaluated the efficacy of checkpoint inhibitors [mainly cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) and programmed death 1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors] in SCLC patients and demonstrated promising survival prospects. This article reviewed the clinical studies of immune checkpoint inhibitors (ICIs) in the first-line, maintenance and second or more line treatment of SCLC. Besides, predictive biomarkers were discussed to select suitable patients for immunotherapy effectively.

The impact of TNFSF14 on prognosis and immune microenvironment in clear cell renal cell carcinoma

BACKGROUND

TNFSF14 has been proven to play an important role in various types of tumors. However, its function in renal cell carcinoma (RCC) has not yet been fully elucidated.

OBJECTIVE

In order to explore molecular mechanism of RCC, we evaluated the effect of TNFSF14 on RCC progression, prognosis and immune microenvironment.

METHODS

Using TCGA database, the differential expression of TNFSF14 and its relationships between clinicopathological features and prognosis were determined. Cox univariate and multivariate analyses were successively performed to identify whether TNFSF14 was an independent prognostic factor. The discriminating ability of TNFSF14 in RCC prognosis analysis was validated under the same clinical subgroups. Tumor mutational burden (TMB) of each RCC samples was calculated and the differential expression of TNFSF14 between high- and low-TMB groups was analyzed. The immune abundances of 22 leukocyte subtypes in each RCC samples were presented through the CIBERSORT algorithm. TIMER database was used to explore the relationships between copy number of TNFSF14 and the infiltration levels of 6 immune cells.

RESULTS

Overexpression of TNFSF14 implied adverse clinicopathological features and poor prognosis. Meanwhile, TNFSF14 was identified as an independent prognostic factor (HR = 1.047, P = 0.028) and possessed prevalent applicability in RCC prognostic analysis. TNFSF14 was upregulated in high-TMB group than that in low-TMB group (Log₂FC = 0.722). Moreover, overexpression of TNFSF14 brought alteration of immune abundance of 8 leukocyte subtypes. Besides, somatic copy number alteration (SCNA) of TNFSF14 was associated with infiltration levels of 6 immune cells.

CONCLUSIONS

TNFSF14 has crucial impact on progression, prognosis and immune microenvironment in RCC. Besides, TNFSF14 may be a potential biomarker for predicting the efficacy and response rate of RCC immunotherapy.