The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis

Harnessing the capacity of phytochemicals to enhance immune checkpoint inhibitor therapy of cancers: A focus on brain malignancies

Brain cancers, particularly glioblastoma multiforme (GBM), are challenging health issues with frequent unmet aspects. Today, discovering safe and effective therapeutic modalities for brain tumors is among the top research interests. Immunotherapy is an emerging area of investigation in cancer treatment. Since immune checkpoints play fundamental roles in repressing anti-cancer immunity, diverse immune checkpoint inhibitors (ICIs) have been developed, and some monoclonal antibodies have been approved clinically for particular cancers; nevertheless, there are significant concerns regarding their efficacy and safety in brain tumors. Among the various tools to modify the immune checkpoints, phytochemicals show good effectiveness and excellent safety, making them suitable candidates for developing better ICIs. Phytochemicals regulate multiple immunological checkpoint-related signaling pathways in cancer biology; however, their efficacy for clinical cancer immunotherapy remains to be established. Here, we discussed the involvement of immune checkpoints in cancer pathology and summarized recent advancements in applying phytochemicals in modulating immune checkpoints in brain tumors to highlight the state-of-the-art and give constructive prospects for future research.

Immune- and metabolism-related gene signature analysis uncovers the prognostic and immune microenvironments of hepatocellular carcinoma

BACKGROUND

Metabolic reprogramming is an emerging hallmark that influences the tumour microenvironment (TME) by regulating the behavior of cancer cells and immune cells. The relationship between metabolism and immunity remains elusive. The purpose of this study was to explore the predictive value of immune- and metabolism-related genes in hepatocellular carcinoma (HCC) and their intricate interplay with TME.

METHODS

We established the immune- and metabolism-related signature (IMRPS) based on the LIHC cohort from The Cancer Genome Atlas (TCGA) dataset. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis and Cox regression analysis confirmed the prognostic value of IMRPS. We investigated differences in immune cell infiltration, clinical features, and therapeutic response between risk groups. The quantitative real-time PCR (qPCR) was used to confirm the expression of signature genes. Immunohistochemical staining was performed to evaluate immune infiltration features in HCC tissue samples. We conducted cell experiments including gene knockout, cell counting kit-8 (CCK-8), and flow cytometry to explore the role of the IMRPS key gene UCK2 in HCC. RNA-seq was used to further investigate the potential underlying mechanism involved.

RESULTS

The IMRPS, composed of four genes, SMS, UCK2, PFKFB4 and MAPT, exhibited significant correlations with survival, immune cell infiltration, clinical features, immune checkpoints and therapeutic response. The IMRPS was shown to be an excellent predictor of HCC prognosis. It could stratify patients appropriately and characterize the TME accurately. The high-risk HCC group exhibited an immunosuppressive microenvironment with abundant M2-like macrophage infiltration, which was confirmed by the immunohistochemistry results. The results of qPCR revealed that the expression of signature genes in 20 HCC tissues was significantly greater than that in adjacent normal tissues. After the key gene UCK2 was knocked out, the proliferation of the Huh7 cell line was significantly inhibited, and monocyte-derived macrophages polarized towards an M1-like phenotype in the coculture system. RNA-seq and GSEA suggested that the phenotypes were closely related to the negative regulation of growth and regulation of macrophage chemotaxis.

CONCLUSIONS

This study established a new IMRS for the accurate prediction of patient prognosis and the TME, which is also helpful for identifying new targets for the treatment of HCC.

Prevalence and prognostic value of PD-L1 expression and tumor mutational burden in persistent, recurrent, or metastatic cervical cancer

OBJECTIVE

To evaluate the prevalence and prognostic role of programmed death ligand 1 (PD-L1) expression and tumor mutational burden (TMB) in patients with non-immunotherapy-treated advanced cervical cancer.

METHODS

Clinical data were retrospectively collected from medical records between January 1, 2008, and December 31, 2016, at Asan Medical Center (Korea); archived tumor samples were assessed for PD-L1 expression (combined positive score [CPS] ≥1) and TMB (≥175 mutations/exome). Overall survival (OS) was defined as time from advanced diagnosis or initiation of first-line or second-line systemic therapy until death/last follow-up. The association of OS with PD-L1 expression and TMB were analyzed using the log-rank test and Cox proportional hazards model adjusted for covariates.

RESULTS

Of 267 patients, 76.0% had squamous cell carcinoma (SCC), 24.0% had adenocarcinoma (AC)/adenosquamous carcinoma (ASC), 64.4% had PD-L1 CPS ≥1, and 32.6% had TMB ≥175 mutations/exome. PD-L1 CPS ≥1 and TMB ≥175 mutations/exome were more prevalent in SCC than in AC/ASC (73.9% and 37.2% vs. 34.4% and 17.7%). There was no association between OS and PD-L1 expression (CPS ≥1 vs. <1: adjusted hazard ratio [HR]=1.14; 95% confidence interval [CI]=0.84-1.53 from advanced diagnosis); OS trended shorter for the subgroup with TMB ≥175 versus <175 mutations/exome (adjusted HR=1.29; 95% CI=0.95-1.75).

CONCLUSION

Retrospective analysis of non-immunotherapy-treated patients with advanced cervical cancer demonstrated a higher prevalence of PD-L1 CPS ≥1 and TMB ≥175 mutations/exome in SCC versus AC/ASC. PD-L1 CPS ≥1 was not associated with OS; TMB ≥175 mutations/exome showed a trend toward shorter OS. Additional studies are needed to confirm these findings.

Prostate cancer therapy using immune checkpoint molecules to target recombinant dendritic cells

PURPOSE

We developed immune checkpoint molecules to target recombinant dendritic cells (DCs) and verified their anti-tumor efficacy and immune response against prostate cancer.

MATERIALS AND METHODS

DCs were generated from mononuclear cells in the tibia and femur bone marrow of mice. We knocked down the programmed death ligand 1 (PD-L1) on monocyte-derived DCs through siRNA PD-L1. Cell surface antigens were immune fluorescently stained through flow cytometry to analyze cultured cell phenotypes. Furthermore, we evaluated the efficacy of monocyte-derived DCs and recombinant DCs in a prostate cancer mouse model with subcutaneous TRAMP-C1 cells. Lastly, DC-induced mixed lymphocyte and lymphocyte-only proliferations were compared to determine cultured DCs' function.

RESULTS

Compared to the control group, siRNA PD-L1 therapeutic DC-treated mice exhibited significantly inhibited tumor volume and increased tumor cell apoptosis. Remarkably, this treatment substantially augmented interferon-gamma and interleukin-2 production by stimulating T-cells in an allogeneic mixed lymphocyte reaction. Moreover, we demonstrated that PD-L1 gene silencing improved cell proliferation and cytokine production.

CONCLUSIONS

We developed monocyte-derived DCs transfected with PD-L1 siRNA from mouse bone marrow. Our study highlights that PD-L1 inhibition in DCs increases antigen-specific immune responses, corroborating previous immunotherapy methodology findings regarding castration-resistant prostate cancer.

Correlation of CT parameters and PD-L1 expression status in gastric cancer

OBJECTIVE

We aimed to explore the correlation between routine computed tomography (CT) imaging features and programmed cell death ligand-1(PD-L1) expression status in gastric cancer and evaluate the predictive value of imaging parameters for this immunotherapy biomarker.

MATERIALS AND METHODS

Patients with gastric adenocarcinoma who underwent abdominal CT three-stage enhanced scan and PD-L1 immunohistochemical testing before treatment were retrospectively examined. All diagnoses were confirmed through pathology. According to the expression status of PD-L1, they were divided into the positive (CPS ≥ 5) or negative group (CPS < 5). Baseline CT imaging features were collected. Diagnostic performances of the different variables were evaluated using receiver operating characteristic (ROC) curve.

RESULTS

In total, 67 patients (17 women and 50 men; mean age: 59.55 ± 10.22 years) with gastric adenocarcinoma were included in the study. The overall stages, probability of maximum lymph node short diameter > 1 cm and peak of lesion enhancement occurring in the arterial phase were statistically significant between the two groups (p < 0.05). Moreover, the arterial enhancement fraction (AEF) was significantly higher in the positive group than that in the negative group (p < 0.05), and ROC curve analysis showed that the AEF exhibited a high evaluation efficacy (area under the curve [AUC] = 0.724 [95% confidence interval (CI): 0.602-0.826]). The combined parameters had the best diagnostic efficacy (AUC = 0.825 [95%CI: 0.716-0.933]), sensitivity (75.00%), and specificity (81.40%).

CONCLUSIONS

These findings confirm a correlation between CT imaging features and PD-L1 expression status in gastric cancer, and AEF may help evaluate high PD-L1 expression and select patients suitable for immunotherapy.

Tumor mutation load better predicts the prognosis of patients treated with immune checkpoint inhibitors in upper gastrointestinal cancers: A systematic review and meta-analysis

BACKGROUND

Tumor mutational load (TML) has emerged as a potential biomarker for multiple solid tumors. However, data on its prognostic impact on upper gastrointestinal (UGI) cancer are limited. Therefore, the aim of this systematic review and meta-analysis was to assess the prognostic value of TML for the survival of patients with UGI cancer.

METHOD

A comprehensive search of the PubMed, Embase, Cochrane Library, and Web of Science databases was conducted up to February 13, 2023. Eleven studies met our inclusion criteria. Hazard ratios (HRs) for progression-free survival and overall survival and their 95% confidence intervals (CIs) were calculated. Subsequently, the combined HR and its 95% CI were calculated for UGI tract cancers in the high and low TML groups. I2 statistics and p-values were used to evaluate heterogeneity. Publication bias, sensitivity, and subgroup analyses were performed to determine sources of heterogeneity.

RESULTS

In total, 932 patients with UGI tract cancer from 11 publications were included. The high TML group treated with immunotherapy showed significantly improved overall survival (HR = 0.68; 95% CI: 0.53, 0.86; p = .001) and progression-free survival (HR = 0.74; 95% CI: 0.58, 0.95; p = .020) compared with the low TML group.

CONCLUSION

Our study demonstrated that patients with UGI tumors and higher TML have a better prognosis with immunotherapy, suggesting that TML is a promising predictive biomarker for immunotherapy.

REGISTRATION

The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO Registration No: CRD42023405596).

Prognostic and immunological values of SKA3 for overall survival in lung adenocarcinoma and its RNA binding protein involved mechanisms

This article aimed to investigate the correlations among SKA3 expression and prognosis, clinical relevance, tumor immunity, and RNA-binding protein (RBP)-involved mechanisms for overall survival (OS) in lung adenocarcinoma (LUAD). To explore the SKA3 expression level in LUAD by analyzing the genomic data as well as related clinical characteristics from the database of TCGA. Nomogram and gene set enrichment analysis (GSEA) were applied, respectively, to evaluate the performance of SKA3 in LUAD. Correlations between SKA3 and immunity and RBP-involved mechanisms were also performed. SKA3 had a higher expression level in LUAD samples than in adjacent normal lung samples, with shorter survival times in the high-SKA3-expressed LUAD subgroup (P < 0.05). qRT-PCR results remained consistent (P < 0.05). Uni-/multivariate Cox analyses revealed that SKA3 could have independent prognostic ability for LUAD (both P < 0.05). The nomogram model constructed with clinical pathological parameters and SKA3 expression levels predicted OS rates for LUAD and GSEA revealed SKA3-related pathways. In aspects of tumor immunity, SKA3 was significantly involved with tumor neoantigen burden, tumor mutational burden, immune cell pathways, and immune checkpoint inhibitor (ICI) molecules (all P < 0.05). The CellMiner database also found significant correlations between SKA3 and the antitumor drug sensitivity of chemotherapy, fenretinide, and PX-316. Besides, a total of nine LncRNA/RBP/SKA3 networks were revealed in LUAD for their RBP-involved mechanisms. SKA3 could serve as a potential biomarker for OS prognosis and immunotherapy in LUAD. LncRNA/RBP/SKA3 networks were identified in LUAD for their RBP-involved mechanisms, paving the way for further experimental verifications.

Abnormal low expression of SFTPC promotes the proliferation of lung adenocarcinoma by enhancing PI3K/AKT/mTOR signaling transduction

The abnormality of surfactant protein C (SFTPC) has been linked to the development of a number of interstitial lung diseases, according to mounting evidence. Nonetheless, the function and mechanism of SFTPC in the biological progression of lung adenocarcinoma (LUAD) remain unclear. Analysis of public datasets and testing of clinical samples suggested that SFTPC expression was abnormally low in LUAD, which was associated with the onset and poor prognosis of LUAD. The SFTPC-related risk score was derived using least absolute shrinkage and selection operator Cox regression as well as multivariate Cox regression. The risk score was highly correlated with tumor purity and tumor mutation burden, and it could serve as an independent prognostic indicator for LUAD. Low-risk LUAD patients may benefit more from CTLA-4 or/and PD-1 inhibitors. Overall, the risk score is useful for LUAD patient prognostication and treatment guidance. Moreover, in vitro and in vivo experiments demonstrated that SFTPC inhibits the proliferation of LUAD by inhibiting PI3K/AKT/mTOR signaling transduction. These results reveal the molecular mechanism by which SFTPC inhibits the proliferation of LUAD and suggest that SFTPC could be a new therapeutic target for LUAD.

The predictive value of tumor mutation burden on survival of gastric cancer patients treated with immune checkpoint inhibitors: A systematic review and meta-analysis

BACKGROUND

Tumor mutation burden (TMB) is a complement to traditional biomarkers related to the efficacy of immune checkpoint inhibitors (ICIs). The relationship between TMB and the efficacy of ICIs in gastric cancer was controversial. The systematic review and meta-analysis were conducted to investigate the predictive value of TMB on survival of gastric cancer patients treated with ICIs.

METHODS

We searched the databases PubMed, Embase, and Web of Science for articles, then screened eligible articles according to inclusion criteria. The effective data were extracted to calculate the pooled effects of hazard ratio (HR) for overall survival (OS) and progression-free survival (PFS), then perform publication bias, sensitivity analysis, and subgroup analysis by STATA 16.0.

RESULTS

The high TMB patients showed significantly longer survival than the low TMB patients (OS: HR 0.65,95% CI 0.55, 0.77, p < 0.001; PFS: HR 0.51, 95% CI 0.33, 0.77, p = 0.001). In the Asian subgroup, patients with high TMB exhibited better prognosis compared to low TMB (OS: HR 0.56, 95% CI 0.43, 0.72, p < 0.001; PFS: HR 0.45, 95% CI 0.28, 0.72, p = 0.001). In the non-Asian subgroup, the survival benefit was observed to be skewed toward patients with high TMB, but it was not statistically significant (OS:HR 0.61, 95% CI 0.32, 1.16, p = 0.133; PFS:HR 0.68, 95% CI 0.31, 1.48, p = 0.322).

CONCLUSIONS

This meta-analysis demonstrated that gastric cancer patients with high TMB showed significant benefits from ICIs compared to those with low TMB patients, particularly in Asian populations.

Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma

BACKGROUND

Melanoma is the deadliest form of skin tumor, and G protein-coupled receptors (GPCRs) play crucial roles in its carcinogenesis. Furthermore, the tumor microenvironment (TME) affects the overall survival (OS) and the response to immunotherapy. The combination of GPCRs and TME from a multi-omics perspective may help to predict the survival of the melanoma patients and their response to immunotherapy.

METHODS

Bulk-seq, single-cell RNA sequencing (scRNA-seq), gene mutations, immunotherapy responses, and clinicopathologic feature data were downloaded from public databases, and prognostic GPCRs and immune cells were screened using multiple machine learning algorithms. The expression levels of GPCRs were detected using real-time quantitative polymerase chain reaction (qPCR) in A375 and HaCaT cell lines. The GPCR-TME classifier was constructed and verified using different cohorts and multi-omics. Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and tracking tumor immunophenotype (TIP) were used to identify the key biological pathways among the GPCR-TME subgroups. Then, tumor mutational burden (TMB), vital mutant genes, antigen presentation genes, and immune checkpoints were compared among the subgroups. Finally, the differences in immunotherapy response rates among the GPCR-TME subgroups were investigated.

RESULTS

A total of 12 GPCRs and five immune cell types were screened to establish the GPCR-TME classifier. No significant differences in the expression levels of the 12 GPCRs were found in the two cell lines. Patients with high GPCR score or low TME score had a poor OS; thus, the GPCRlow/TMEhigh subgroup had the most favorable OS. The scRNA-seq result revealed that immune cells had a higher GPCR score than tumor and stromal cells. The GPCR-TME classifier acted as an independent prognostic factor for melanoma. GSEA, WGCNA, and TIP demonstrated that the GPCRlow/TMEhigh subgroup was related to the activation and recruitment of anti-tumor immune cells and the positive regulation of the immune response. From a genomic perspective, the GPCRlow/TMEhigh subgroup had higher TMB, and different mutant genes. Ultimately, higher expression levels of antigen presentation genes and immune checkpoints were observed in the GPCRlow/TMEhigh subgroup, and the melanoma immunotherapy cohorts confirmed that the response rate was highest in the GPCRlow/TMEhigh cohort.

CONCLUSIONS

We have developed a GPCR-TME classifier that could predict the OS and immunotherapy response of patients with melanoma highly effectively based on multi-omics analysis.

The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma

Background

Gliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.

Methods

This study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.

Results

The TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).

Conclusion

Overall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG.

Construction and Assessment of a Prognostic Risk Model for Cervical Cancer Based on Lactate Metabolism-Related lncRNAs

Purpose

Cervical cancer (CC) has the fourth highest incidence and mortality rate among female cancers. Lactate is a key regulator promoting tumor progression. Long non-coding RNAs (lncRNAs) are closely associated with cervical cancer (CC). The study was aimed to develop a prognostic risk model for cervical cancer based on lactate metabolism-associated lncRNAs and to determine their clinical prognostic value.

Patients and Methods

In this study, CESC transcriptome data were obtained from the TCGA database. 262 lactate metabolism-associated genes were extracted from MsigDB (Molecular Characterization Database). Then, correlation analysis was used to identify LRLs. Univariate Cox regression analysis was performed afterwards, followed by least absolute shrinkage and selection operator (LASSO) regression analysis and multiple Cox regression analysis. 10 lncRNAs were finally identified to construct a risk score model. They were divided into two groups of high risk and low risk according to the median of risk scores. The predictive performance of the models was assessed by Kaplan-Meier (K-M) analysis, subject work characteristics (ROC) analysis, and univariate and multivariate Cox analyses. To assess the clinical utility of the prognostic model, we performed functional enrichment analysis, immune microenvironment analysis, mutation analysis, and column line graph generation.

Results

We constructed a prognostic model consisting of 10 LRLs at CC. We observed that high-risk populations were strongly associated with poor survival outcomes. Risk score was an independent risk factor for CC prognosis and was strongly associated with immune microenvironment analysis and tumor mutational load.

Conclusion

We developed a risk model of lncRNAs associated with lactate metabolism and used it to predict prognosis of CC, which could guide and facilitate the progress of new treatment strategies and disease monitoring in CC patients.

Comprehensive Analysis to Reveal Amino Acid Metabolism-Associated Genes as a Prognostic Index in Gastric Cancer

Background

Amino acid metabolism (AAM) is related to tumor growth, prognosis, and therapeutic response. Tumor cells use more amino acids with less synthetic energy than normal cells for rapid proliferation. However, the possible significance of AAM-related genes in the tumor microenvironment (TME) is poorly understood.

Methods

Gastric cancer (GC) patients were classified into molecular subtypes by consensus clustering analysis using AAMs genes. AAM pattern, transcriptional patterns, prognosis, and TME in distinct molecular subtypes were systematically investigated. AAM gene score was built by least absolute shrinkage and selection operator (Lasso) regression.

Results

The study revealed that copy number variation (CNV) changes were prevalent in selected AAM-related genes, and most of these genes exhibited a high frequency of CNV deletion. Three molecular subtypes (clusters A, B, and C) were developed based on 99 AAM genes, which cluster B had better prognosis outcome. We developed a scoring system (AAM score) based on 4 AAM gene expressions to measure the AAM patterns of each patient. Importantly, we constructed a survival probability prediction nomogram. The AAM score was substantially associated with the index of cancer stem cells and sensitivity to chemotherapy intervention.

Conclusion

Overall, we detected prognostic AAM features in GC patients, which may help define TME characteristics and explore more effective treatment approaches.

Prediction and analysis of osteoarthritis hub genes with bioinformatics

Background

Osteoarthritis (OA) is the most common type of arthritis. OA can cause joint pain, stiffness, and loss of function. The pathogenesis of OA is not completely clear. Moreover, there is no effective treatment, and clinical management is limited to symptomatic relief or joint surgery. This study utilized bioinformatics to analyze normal and OA articular cartilage samples to find biomarkers and therapeutic targets for OA.

Methods

The GSE169077 gene chip dataset was downloaded from the public gene chip data platform of the National Biotechnology Information Center. The dataset included 6 samples of OA tissues and 5 samples of healthy cartilage tissues. Differentially expressed genes (DEGs) were screened using the R language "limma" function package under the threshold of log₂[fold change (FC)] ≥2 and a P value <0.05. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathways of the target genes were enriched and analyzed using the database for annotation, visualization, and integrated discovery (DAVID), and a protein-protein interaction (PPI) network was further constructed using the search tool for the retrieval of interacting genes/proteins (STRING) database. The coexpression relationship of the genes in the module was visualized and screened with Cytoscape.

Results

A total of 27 DEGs were identified, including 9 downregulated genes and 18 upregulated genes. GO signal pathway enrichment analysis showed involvement in hypoxic response, fibrous collagen trimer, and extracellular matrix structural components. KEGG analysis demonstrated associations with protein digestion and absorption, extracellular matrix receptor interaction, and the peroxisome proliferator-activated receptor signal pathway, among several other pathways. A PPI network was obtained through STRING analysis, and the results were imported into Cytoscape software. The 27 DEGs were sequenced by the cytoHubba plug-in by various calculation methods, and 5 hub genes (COL1A1, COL1A2, POSTN, BMP1, and MMP13) were finally selected. These genes were analyzed by PPI again and annotated with GO and KEGG in different colors.

Conclusions

Bioinformatics technology effectively identified differential genes in the knee cartilage tissue of healthy controls and patients with OA, providing opportunities to further explore the mechanism and treatment of OA on a transcriptional level.

Transcriptome and pan-cancer system analysis identify PM2.5-induced stanniocalcin 2 as a potential prognostic and immunological biomarker for cancers

Epidemiological studies have shown that air pollution and particulate matter (PM) are closely related to the occurrence of cancer. However, the potential prognostic and immunological biomarkers for air pollution related cancers are lacking. In this study, we proved PM2.5 exposure was correlated with lung cancer through transcriptome analysis. Importantly, we identified STC2 as a key gene regulated by PM2.5, whose expression in epithelial cells was significantly increased after PM2.5 treatment and validated by using RT-qPCR and immunofluorescence. Kaplan-Meier OS curves suggested that high STC2 expression positively correlated with a poor prognosis in lung cancer. Furthermore, we discovered that STC2 was associated with multiple cancers and pathways in cancer. Next, Pan-Cancer Expression Landscape of STC2 showed that STC2 exhibited inconsistent expression across 26 types of human cancer, lower in KIRP in cancer versus adjacent normal tissues, and significantly higher in another cancers. Cox regression results suggested that STC2 expression was positively or negatively associated with prognosis in different cancers. Moreover, STC2 expression was associated with clinical phenotypes including age, gender, stage and grade. Mutation features of STC2 were also analyzed, in which the highest alteration frequency of STC2 was presented in KIRC with amplification. Meanwhile, the effects of copy number variation (CNV) on STC2 expression were investigated across various tumor types, suggesting that STC2 expression was significantly correlated with CNV in tumors. Additionally, STC2 was closely related to tumor heterogeneity, tumor stemness and tumor immune microenvironment like immune cell infiltration. In the meantime, we analyzed methylation modifications and immunological correlation of STC2. The results demonstrated that STC2 expression positively correlated with most RNA methylation genes and immunomodulators across tumors. Taken together, the findings revealed that PM2.5-induced STC2 might be a potential prognostic and immunological biomarker for cancers related to air pollution.

Correlation of preclinical and clinical biomarkers with efficacy and toxicity of cancer immunotherapy

Immune checkpoint inhibitors (ICIs) have revealed significant clinical values in different solid tumors and hematological malignancy, changing the landscape for the treatment of multiple types of cancer. However, only a subpopulation of patients has obvious tumor response and long-term survival after ICIs treatment, and many patients may experience other undesirable clinical features. Therefore, biomarkers are critical for patients to choose exact optimum therapy. Here, we reviewed existing preclinical and clinical biomarkers of immunotherapeutic efficacy and immune-related adverse events (irAEs). Based on efficacy prediction, pseudoprogression, hyperprogressive disease, or irAEs, these biomarkers were divided into cancer cell-derived biomarkers, tumor microenvironment-derived biomarkers, host-derived biomarkers, peripheral blood biomarkers, and multi-modal model and artificial intelligence assessment-based biomarkers. Furthermore, we describe the relation between ICIs efficacy and irAEs. This review provides the overall perspective of biomarkers of immunotherapeutic outcome and irAEs prediction during ICIs treatment.

Signature based on RNA-binding protein-related genes for predicting prognosis and guiding therapy in non-small cell lung cancer

Background: Studies have reported that RNA-binding proteins (RBPs) are dysregulated in multiple cancers and are correlated with the progression and prognosis of disease. However, the functions of RBPs in non-small cell lung cancer (NSCLC) remain unclear. The present study aimed to explore the function of RBPs in NSCLC and their prognostic and therapeutic value.

Methods: The mRNA expression profiles, DNA methylation data, gene mutation data, copy number variation data, and corresponding clinical information on NSCLC were downloaded from The Cancer Genome Atlas, Gene Expression Omnibus, and the University of California Santa Cruz Xena databases. The differentially expressed RBPs were identified between tumor and control tissues, and the expression and prognostic value of these RBPs were systemically investigated by bioinformatics analysis. A quantitative polymerase chain reaction (qPCR) was performed to validate the dysregulated genes in the prognostic signature.

Results: A prognostic RBP-related signature was successfully constructed based on eight RBPs represented as a risk score using least absolute shrinkage and selection operator (LASSO) regression analysis. The high-risk group had a worse overall survival (OS) probability than the low-risk group (p &lt; 0.001) with 1-, 3-, and 5-year area under the receiver operator characteristic curve values of 0.671, 0.638, and 0.637, respectively. The risk score was associated with the stage of disease (p &lt; 0.05) and was an independent prognostic factor for NSCLC when adjusted for age and UICC stage (p &lt; 0.001, hazard ratio (HR): 1.888). The constructed nomogram showed a good predictive value. The P53, focal adhesion, and NOD-like receptor signaling pathways were the primary pathways in the high-risk group (adjusted p value &lt;0.05). The high-risk group was correlated with increased immune infiltration (p &lt; 0.05), upregulated relative expression levels of programmed cell death 1 (PD1) (p = 0.015), cytotoxic T-lymphocyte-associated protein 4 (CTLA4) (p = 0.042), higher gene mutation frequency, higher tumor mutational burden (p = 0.034), and better chemotherapy response (p &lt; 0.001). The signature was successfully validated using the GSE26939, GSE31210, GSE30219, and GSE157009 datasets. Dysregulation of these genes in patients with NSCLC was confirmed using the qPCR in an independent cohort (p &lt; 0.05).

Conclusion: An RBP-related signature was successfully constructed to predict prognosis in NSCLC, functioning as a reference for individualized therapy, including immunotherapy and chemotherapy.