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Acute myeloid leukemia (AML) is characterized by the uncontrolled proliferation of myeloid leukemia cells in the bone marrow and other hematopoietic tissues and is highly heterogeneous. While with the progress of sequencing technology, understanding of the AML-related biomarkers is still incomplete. The purpose of this study is to identify potential biomarkers for prognosis of AML. Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases were employed for investigating potential biomarkers for the prognosis of AML. This study screened a total of 6153 genes by analyzing various changes in 103 acute myeloid leukemia (AML) samples, including gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases. Moreover, seven AML-related co-expression modules were mined by WGCNA analysis, and twelve biomarkers associated with the AML prognosis were identified from each top 10 genes of the seven co-expression modules. The AML samples were then classified into two subgroups, the prognosis of which is significantly different, based on the expression of these twelve genes. The differentially expressed 7 genes of two subgroups (HOXB-AS3, HOXB3, SLC9C2, CPNE8, MEG8, S1PR5, MIR196B) are mainly involved in glucose metabolism, glutathione biosynthesis, small G protein-mediated signal transduction, and the Rap1 signaling pathway. With the utilization of WGCNA mining, seven gene co-expression modules were identified from the TCGA database, and there are unreported genes that may be potential driver genes of AML and may be the direction to identify the possible molecular signatures to predict survival of AML patients and help guide experiments for potential clinical drug targets.

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Monocarboxylate transporters (MCTs) play an immense role in metabolically active solid tumors by regulating concentration-dependent transport of different important monocarboxylates including pyruvate and lactate and are encoded by the SLC16A family of genes. Given the vast array of functions, these transporters play in oncogenesis, our objective was to look into the association of MCT1 (SLC16A1), MCT2 (SLC16A7), MCT3 (SLC16A8), and MCT4 (SLC16A3) with Epithelial ovarian cancer (EOC) pathophysiology by exploiting various publicly available databases and web resources. Few of the in silico findings were confirmed via in vitro experiments in EOC cell lines, SKOV3 and OAW-42. MCT1 and MCT4 were found to be upregulated at the mRNA level in OC tissues compared to normal. However, only higher level of MCT4 mRNA was found to be associated with poor patient survival. MCT4 was positively correlated with gene families responsible for invasion, migration, and immune modification, proving it to be one of the most important MCTs for therapeutic intervention. We compared the effects of MCT1/2 blocker SR13800 and a broad-spectrum MCT blocker α-Cyano Hydroxy Cinnamic Acid (α-CHCA) and discovered that α-CHCA has a greater effect on diminishing the invasive behavior of the cancer cells than MCT1/2 blocker SR13800. From our study, MCT4 has emerged as a prospective marker for predicting poor patient outcomes and a potential therapeutic target.

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Lung adenocarcinoma (LUAD) is the most prevalent lung cancer and one of the leading causes of death. Previous research found a link between LUAD and Aldehyde Dehydrogenase 2 (ALDH2), a member of aldehyde dehydrogenase gene (ALDH) superfamily. In this study, we identified additional useful prognostic markers for early LUAD identification and targeting LUAD therapy by analyzing the expression level, epigenetic mechanism, and signaling activities of ALDH2 in LUAD patients. The obtained results demonstrated that ALDH2 gene and protein expression significantly downregulated in LUAD patient samples. Furthermore, The American Joint Committee on Cancer (AJCC) reported that diminished ALDH2 expression was closely linked to worse overall survival (OS) in different stages of LUAD. Considerably, ALDH2 showed aberrant DNA methylation status in LUAD cancer. ALDH2 was found to be downregulated in the proteomic expression profile of several cell biology signaling pathways, particularly stem cell-related pathways. Finally, the relationship of ALDH2 activity with stem cell-related factors and immune system were reported. In conclusion, the downregulation of ALDH2, abnormal DNA methylation, and the consequent deficit of stemness signaling pathways are relevant prognostic and therapeutic markers in LUAD.

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Cancer-associated fibroblasts (CAFs) can exert their immunosuppressive effects by secreting various effectors that are involved in the regulation of tumor-infiltrating immune cells as well as other immune components in the tumor immune microenvironment (TIME), thereby promoting tumorigenesis, progression, metastasis, and drug resistance. Although a large number of studies suggest that CAFs play a key regulatory role in the development of head and neck squamous cell carcinoma (HNSCC), there are limited studies on the relevance of CAFs to the prognosis of HNSCC. In this study, we identified a prognostic signature containing eight CAF-related genes for HNSCC by univariate Cox analysis, lasso regression, stepwise regression, and multivariate Cox analysis. Our validation in primary cultures of CAFs from human HNSCC and four human HNSCC cell lines confirmed that these eight genes are indeed characteristic markers of CAFs. Immune cell infiltration differences analysis between high-risk and low-risk groups according to the eight CAF-related genes signature hinted at CAFs regulatory roles in the TIME, further revealing its potential role on prognosis. The signature of the eight CAF-related genes was validated in different independent validation cohorts and all showed that it was a valid marker for prognosis. The significantly higher overall survival (OS) in the low-risk group compared to the high-risk group was confirmed by Kaplan-Meier (K-M) analysis, suggesting that the signature of CAF-related genes can be used as a non-invasive predictive tool for HNSCC prognosis. The low-risk group had significantly higher levels of tumor-killing immune cell infiltration, as confirmed by CIBERSORT analysis, such as CD8+ T cells, follicular helper T cells, and Dendritic cells (DCs) in the low-risk group. In contrast, the level of infiltration of pro-tumor cells such as M0 macrophages and activated Mast cells (MCs) was lower. It is crucial to delve into the complex mechanisms between CAFs and immune cells to find potential regulatory targets and may provide new evidence for subsequently targeted immunotherapy. These results suggest that the signature of the eight CAF-related genes is a powerful indicator for the assessment of the TIME of HNSCC. It may provide a new and reliable potential indicator for clinicians to predict the prognosis of HNSCC, which may be used to guide treatment and clinical decision-making in HNSCC patients. Meanwhile, CAF-related genes are expected to become tumor biomarkers and effective targets for HNSCC.

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Background: Dysregulation of long noncoding RNAs (lncRNAs) has been reported to be associated with multiple tumors where they act as tumor suppressors or accelerators. The lncRNA CYTOR was identified as an oncogene involved in many cancers, such as gastric cancer, colorectal cancer, hepatocellular carcinoma, and renal cell carcinoma. However, the role of CYTOR in bladder cancer (BCa) has rarely been reported. Methods: Using cancer datasets from The Cancer Genome Atlas (TCGA) program, we analyzed the association between CYTOR expression and prognostic value, oncogenic pathways, antitumor immunity and immunotherapy response in BCa. The influence of CYTOR on the immune infiltration pattern in the urothelial carcinoma microenvironment was further verified in our dataset. Single-cell analysis revealed the role of CYTOR in the tumor microenvironment (TME) of BCa. Finally, we evaluated the expression of CYTOR in BCa in the Peking University First Hospital (PKU-BCa) dataset and its correlation with the malignant phenotype of BCa in vitro and in vivo. Results: The results indicated that CYTOR was highly expressed in multiple cancer samples, including BCa, and increased CYTOR expression contributed to poor overall survival (OS). Additionally, elevated CYTOR expression was significantly correlated with clinicopathological features of BCa, such as female sex, advanced TNM stage, high histological grade and non-papillary subtype. Functional characterization revealed that CYTOR may be involved in immune-related pathways and the epithelial mesenchymal transformation (EMT) process. Moreover, CYTOR had a significant association with infiltrating immune cells, including M2 macrophages and regulatory T cells (Tregs). CYTOR facilitates the crosstalk between cancer-associated fibroblasts (CAFs) and macrophages, and mediates M2 polarization of macrophages. Correlation analysis revealed a positive correlation between CYTOR expression and programmed cell death-1 (PD-1)/programmed death ligand 1 (PD-L1)/expression and other targets for specific immunotherapy in BCa, which are recognized to predict the efficacy of immunotherapy. Conclusions: These results suggest that CYTOR serves as a potential biomarker for predicting survival outcome, TME cell infiltration characteristics and immunotherapy response in BCa.

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Alternative splicing (AS) events modulate certain pathways and phenotypic plasticity in cancer. Although previous studies have computationally analyzed splicing events, it is still a challenge to uncover biological functions induced by reliable AS events from tremendous candidates. To provide essential splicing event signatures to assess pathway regulation, we developed a database by collecting two datasets: (i) reported literature and (ii) cancer transcriptome profile. The former includes knowledge-based splicing signatures collected from 63,229 PubMed abstracts using natural language processing, extracted for 202 pathways. The latter is the machine learning-based splicing signatures identified from pan-cancer transcriptome for 16 cancer types and 42 pathways. We established six different learning models to classify pathway activities from splicing profiles as a learning dataset. Top-ranked AS events by learning model feature importance became the signature for each pathway. To validate our learning results, we performed evaluations by (i) performance metrics, (ii) differential AS sets acquired from external datasets, and (iii) our knowledge-based signatures. The area under the receiver operating characteristic values of the learning models did not exhibit any drastic difference. However, random-forest distinctly presented the best performance to compare with the AS sets identified from external datasets and our knowledge-based signatures. Therefore, we used the signatures obtained from the random-forest model. Our database provided the clinical characteristics of the AS signatures, including survival test, molecular subtype, and tumor microenvironment. The regulation by splicing factors was additionally investigated. Our database for developed signatures supported retrieval and visualization system.

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Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

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Transcriptome analysis of head and neck squamous cell carcinoma (HNSCC) has been pivotal to comprehending the convoluted biology of HNSCC tumors. MAPKAPK2 or MK2 is a critical modulator of the mRNA turnover of crucial genes involved in HNSCC progression. However, MK2-centric transcriptome profiles of tumors are not well known. This study delves into HNSCC progression with MK2 at the nexus to delineate the biological relevance and intricate crosstalk of MK2 in the tumor milieu. We performed next-generation sequencing-based transcriptome profiling of HNSCC cells and xenograft tumors to ascertain mRNA expression profiles in MK2-wild type and MK2-knockdown conditions. The findings were validated using gene expression assays, immunohistochemistry, and transcript turnover studies. Here, we identified a pool of crucial MK2-regulated candidate genes by annotation and differential gene expression analyses. Regulatory network and pathway enrichment revealed their significance and involvement in the HNSCC pathogenesis. Additionally, 3'-UTR-based filtering recognized important MK2-regulated downstream target genes and validated them by nCounter gene expression assays. Finally, immunohistochemistry and transcript stability studies revealed the putative role of MK2 in regulating the transcript turnover of IGFBP2, MUC4, and PRKAR2B in HNSCC. Conclusively, MK2-regulated candidate genes were identified in this study, and their plausible involvement in HNSCC pathogenesis was elucidated. These genes possess investigative values as targets for diagnosis and therapeutic interventions for HNSCC.

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E3 ubiquitin ligases (E3s) and deubiquitinating enzymes (DUBs) play key roles in protein degradation. However, a large number of E3 substrate interactions (ESIs) and DUB substrate interactions (DSIs) remain elusive. Here, we present DeepUSI, a deep learning-based framework to identify ESIs and DSIs using the rich information present in protein sequences. Utilizing the collected golden standard dataset, key hyperparameters in the process of model training, including the ones relevant to data sampling and number of epochs, have been systematically assessed. The performance of DeepUSI was thoroughly evaluated by multiple metrics, based on internal and external validation. Application of DeepUSI to cancer-associated E3 and DUB genes identified a list of druggable substrates with functional implications, warranting further investigation. Together, DeepUSI presents a new framework for predicting substrates of E3 ubiquitin ligases and deubiquitinates.

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Purpose: To develop models based on radiomics and genomics for predicting the histopathologic nuclear grade with localized clear cell renal cell carcinoma (ccRCC) and to assess whether macro-radiomics models can predict the microscopic pathological changes. Method: In this multi-institutional retrospective study, a computerized tomography (CT) radiomic model for nuclear grade prediction was developed. Utilizing a genomics analysis cohort, nuclear grade-associated gene modules were identified, and a gene model was constructed based on top 30 hub mRNA to predict the nuclear grade. Using a radiogenomic development cohort, biological pathways were enriched by hub genes and a radiogenomic map was created. Results: The four-features-based SVM model predicted nuclear grade with an area under the curve (AUC) score of 0.94 in validation sets, while a five-gene-based model predicted nuclear grade with an AUC of 0.73 in the genomics analysis cohort. A total of five gene modules were identified to be associated with the nuclear grade. Radiomic features were only associated with 271 out of 603 genes in five gene modules and eight top 30 hub genes. Differences existed in the enrichment pathway between associated and un-associated with radiomic features, which were associated with two genes of five-gene signatures in the mRNA model. Conclusion: The CT radiomics models exhibited higher predictive performance than mRNA models. The association between radiomic features and mRNA related to nuclear grade is not universal.

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Head and neck squamous cell carcinoma (HNSC) is one of most common malignancies with high mortality worldwide. Importantly, the molecular heterogeneity of HNSC complicates the clinical diagnosis and treatment, leading to poor overall survival outcomes. To dissect the complex heterogeneity, recent studies have reported multiple molecular subtyping systems. For instance, HNSC can be subdivided to four distinct molecular subtypes: atypical, basal, classical, and mesenchymal, of which the mesenchymal subtype is characterized by upregulated epithelial-mesenchymal transition (EMT) and associated with poorer survival outcomes. Despite a wealth of studies into the complex molecular heterogeneity, the regulatory mechanism specific to this aggressive subtype remain largely unclear. Herein, we developed a network-based bioinformatics framework that integrates lncRNA and mRNA expression profiles to elucidate the subtype-specific regulatory mechanisms. Applying the framework to HNSC, we identified a clinically relevant lncRNA LNCOG as a key master regulator mediating EMT underlying the mesenchymal subtype. Five genes with strong prognostic values, namely ANXA5, ITGA5, CCBE1, P4HA2, and EPHX3, were predicted to be the putative targets of LNCOG and subsequently validated in other independent datasets. By integrative analysis of the miRNA expression profiles, we found that LNCOG may act as a ceRNA to sponge miR-148a-3p thereby upregulating ITGA5 to promote HNSC progression. Furthermore, our drug sensitivity analysis demonstrated that the five putative targets of LNCOG were also predictive of the sensitivities of multiple FDA-approved drugs. In summary, our bioinformatics framework facilitates the dissection of cancer subtype-specific lncRNA regulatory mechanisms, providing potential novel biomarkers for more optimized treatment of HNSC.

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Background & Aims: SCY1-like pseudokinase 3 (SCYL3) was identified as a binding partner of ezrin, implicating it in metastasis. However, the clinical relevance and functional role of SCYL3 in cancer remain uncharacterized. In this study, we aimed to elucidate the role of SCYL3 in the progression of hepatocellular carcinoma (HCC). Methods: The clinical significance of SCYL3 in HCC was evaluated in publicly available datasets and by qPCR analysis of an in-house HCC cohort. The functional significance and mechanistic consequences of SCYL3 were examined in SCYL3-knockdown/overexpressing HCC cells. In vivo tumor progression was evaluated in Tp53 KO/c-Myc OE mice using the sleeping beauty transposon system. Potential downstream pathways were investigated by co-immunoprecipitation, western blotting analysis and immunofluorescence staining. Results: SCYL3 is often overexpressed in HCC; it is preferentially expressed in metastatic human HCC tumors and is associated with worse patient survival. Suppression of SCYL3 in HCC cells attenuated cell proliferation and migration as well as in vivo metastasis. Intriguingly, endogenous SCYL3 overexpression increased tumor development and metastasis in Tp53 KO/c-Myc OE mice. Mechanistic investigations revealed that SCYL3 physically binds and regulates the stability and transactivating activity of ROCK2 (Rho kinase 2) via its C-terminal domain, leading to the increased formation of actin stress fibers and focal adhesions. Conclusions: These findings reveal that SCYL3 plays a critical role in promoting the progression of HCC and have implications for developing new therapeutic strategies to tackle metastatic HCC. Impact and implications: SCYL3 was first reported to be a binding partner of a metastasis-related gene, ezrin. To date, the clinical relevance and functional role of SCYL3 in cancer remain uncharacterized. Herein, we uncover its crucial role in liver cancer progression. We show that it physically binds and regulates the stability and transactivating activity of ROCK2 leading to HCC tumor progression. Our data provide mechanistic insight that SCYL3-mediated ROCK2 protein stability plays a pivotal role in growth and metastasis of HCC cells. Targeting SCYL3/ROCK2 signaling cascade may be a novel therapeutic strategy for treatment of HCC patients.

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Brain glioma is one of the cancer types with worst prognosis, and LMO2 has been reported to play oncogenic functions in brain gliomas. Herein, analysis of datasets from The Cancer Genome Atlas (TCGA) indicated that higher LMO2 level in patient samples indicated worse prognosis in lower grade gliomas (LGG) but not glioblastoma multiforme (GBM). Further, in tumor tissues consisting of a variety of cell types, LMO2 level indicated intratumoral endothelium and pattern recognition receptor (PRR) response in both LGGs and GBMs, and additionally indicated cytotoxic T-lymphocyte, M2 macrophage infiltration and fibroblast specifically in LGGs. Moreover, only in LGGs these aspects were significantly associated with patient survival, in either risky or protective manner, and these dissected associations can give a better prediction on patient prognosis than LMO2 alone. This study not only provided more detailed understandings of LMO2 functional representatives in brain gliomas but also demonstrated that dealing with certain gene (LMO2 in this study) in transcriptome data with the Weighted Gene Co-Expression Network Analysis (WGCNA) method was a robust strategy for dissecting exact and reasonable gene functions/associations in a complicated tumor environment.

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Objective: OsteoDIP aims to collect and provide, in a simple searchable format, curated high throughput RNA expression data related to osteoarthritis. Design: Datasets are collected annually by searching "osteoarthritis gene expression profile" in PubMed. Only publications containing patient data and a list of differentially expressed genes are considered. From 2020, the search has expanded to include non-coding RNAs. Moreover, a search in GEO for "osteoarthritis" datasets has been performed using 'Homo sapiens' and 'Expression profiling by array' filters. Annotations for genes linked to osteoarthritis have been downloaded from external databases. Results: Out of 1204 curated papers, 63 have been included in OsteoDIP, while GEO curation led to the collection of 28 datasets. Literature data provides a snapshot of osteoarthritis research derived from 1924 human samples, while GEO datasets provide expression for additional 1012 patients. Similar to osteoarthritis literature, OsteoDIP data has been created mostly from studies focused on knee, and the tissue most frequently investigated is cartilage. GEO data sets were fully integrated with associated clinical data. We showcase examples and use cases applicable for translational research in osteoarthritis. Conclusions: OsteoDIP is publicly available at http://ophid.utoronto.ca/OsteoDIP. The website is easy to navigate and all the data is available for download. Data consolidation allows researchers to perform comparisons across studies and to combine data from different datasets. Our examples show how OsteoDIP can integrate with and improve osteoarthritis researchers' pipelines.

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Based on increasing research evidence, hepatocellular carcinoma (HCC) is heterogeneous, and genetic profiling has led to the identification of multiple subtypes of this disease. To advance our knowledge and the ability to use individualized medicine in the treatment of HCC, it is essential to perform a complete and methodical characterization of various molecular subtypes. The canonical Wnt/ß-catenin pathway is an evolutionarily conserved complicated signaling mechanism that plays a role in carcinogenesis and progression of HCC. In this study, we acquired RNA sequencing, somatic mutation, and clinical data from 701 patients from The Cancer Genome Atlas and Gene Expression Omnibus databases and stratified patients into two subgroups: WNT-high and WNT-low. In general, the WNT-high subtype is associated with an immunosuppressive microenvironment, poor prognosis, cancer-related pathways, and a low response to immune checkpoint therapy. We also found that WNT3 is negatively linked to CD8+ T-cell infiltration using multiple immunofluorescence assays. Finally, we developed a WNT-related prognostic model to predict the survival time of patients with HCC. In summary, we developed a new classification scheme for HCC based on Wnt signaling signatures. This classification produced substantial clinical effects, both in terms of assessing patient prognosis and immunotherapy administered to patients with HCC.

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Because of the heterogeneity of lower-grade gliomas (LGGs), patients show various survival outcomes that are not reliably predicted by histological classification. The tumour microenvironment (TME) contributes to the initiation and progression of brain LGGs. Identifying potential prognostic markers based on the immune and stromal components in the TME will provide new insights into the dynamic modulation of these two components of the TME in LGGs. We applied ESTIMATE to calculate the ratio of immune and stromal components from The Cancer Genome Atlas database. After combined differential gene expression analysis, protein-protein interaction network construction and survival analysis, CD44 was screened as an independent prognostic factor and subsequently validated utilizing data from the Chinese Glioma Genome Atlas database. To decipher the association of glioma cell CD44 expression with stromal cells in the TME and tumour progression, RT-qPCR, cell viability and wound healing assays were employed to determine whether astrocytes enhance glioma cell viability and migration by upregulating CD44 expression. Surprisingly, M1 macrophages were identified as positively correlated with CD44 expression by CIBERSORT analysis. CD44+ glioma cells were further suggested to interact with microglia-derived macrophages (M1 phenotype) via osteopontin signalling on the basis of single-cell sequencing data. Overall, we found that astrocytes could elevate the CD44 expression level of glioma cells, enhancing the recruitment of M1 macrophages that may promote glioma stemness via osteopontin-CD44 signalling. Thus, glioma CD44 expression might coordinate with glial activities in the TME and serve as a potential therapeutic target and prognostic marker for LGGs.

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Purpose: To evaluate the clinical relevance of low-frequency copy number aberrations (CNAs) in uveal melanoma (UM) and to discern residual genomic and clinical heterogeneity within established molecular subtypes based on genome-wide CNA profiling of 921 primary tumors. Design: Retrospective single-center case series. Participants: Patients with primary UM referred for genetic testing between 2008 and 2016 (n = 921). The Cancer Genome Atlas cohort with clinical outcome data available (n = 70) was used to validate findings. Methods: Genome-wide CNAs were generated for primary tumors from 921 patients and for 19 metastatic UM (mUM) in the liver. Of the 921 patients, metastatic outcome was known for 678 patients with a median time to metastasis of 4.5 years. The primary tumors were processed on the Affymetrix arrays SNP-5.0 (n = 140), SNP-6.0 (n = 359), or CytoScanHD (n = 422), and the metastatic tumors on the CytoScanHD array (n = 19). Recurrent CNAs were identified, and the prognostic effect of individual CNAs and multiple CNA clustering strategies, including more specific molecular subgroups with rare CNAs, were evaluated. Main Outcome Measures: CNA recurrence, and effect of CNAs and derived molecular subtypes on metastatic-free survival. Results: Genomic profiling revealed CNAs associated with risk of metastasis and demonstrated a strong association between chromosomal instability and patient prognosis. Using standard prognostic CNAs, 6 clusters were detected, and inclusion of chromosome 16q deletion revealed an additional cluster. Of these 7 genomic clusters, 5 patient groups showed distinct rates of metastasis, indicating that different genomic patterns can have similar patient outcomes. A small group of patients with a significantly higher rate of metastasis was characterized by monosomy 3, 8q amplification, and deletion of 1p or 16q. Although this ultra-high-risk group accounts for only 7% of this cohort, 88% demonstrated metastasis within 4 years, compared with 45% in the second-highest risk group. Conclusions: These results suggest that 1p and 16q deletion should be incorporated in clinical assays to assess prognosis at diagnosis and to guide enrollment in clinical trials for adjuvant therapies.

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The human microbiome is a vast collection of microbial species that exist throughout the human body and regulate various bodily functions and phenomena. Of the microbial species that exist in the human microbiome, those within the archaea domain have not been characterized to the extent of those in more common domains, despite their potential for unique metabolic interaction with host cells. Research has correlated tumoral presence of bacterial microbial species to the development and progression of lung cancer; however, the impacts and influences of archaea in the microbiome remain heavily unexplored. Within the United States lung cancer remains highly fatal, responsible for over 100,000 deaths every year with a 5-year survival rate of roughly 22.9%. This project attempts to investigate specific archaeal species' correlation to lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) incidence, patient staging, death rates across individuals of varying ages, races, genders, and smoking-statuses, and potential molecular targets associated with archaea microbiome. Archaeal species abundance was assessed across lung tissue samples of 527 LUAD patients, 479 LUSC patients, and 99 healthy individuals. Nine archaeal species were found to be of significantly altered abundance in cancerous samples as compared to normal counterparts, 6 of which are common to both LUAD and LUSC subgroups. Several of these species are of the taxonomic class Thermoprotei or the phylum Euryarchaeota, both known to contain metabolic processes distinct from most bacterial species. Host-microbe metabolic interactions may be responsible for the observed correlation of these species' abundance with cancer incidence. Significant microbes were correlated to patient gene expression to reveal genes of altered abundance with respect to high and low archaeal presence. With these genes, cellular oncogenic signaling pathways were analyzed for enrichment across cancer and normal samples. In comparing gene expression between LUAD and adjacent normal samples, 2 gene sets were found to be significantly enriched in cancers. In LUSC comparison, 6 sets were significantly enriched in cancer, and 34 were enriched in normals. Microbial counts across healthy and cancerous patients were then used to develop a machine-learning based predictive algorithm, capable of distinguishing lung cancer patients from healthy normal with 99% accuracy.

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Background: High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient's own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response. Methods: Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients' TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility. Results: In experiments using WSIs from The Cancer Genome Atlas (TCGA) bladder cancer (BLCA), our findings show that computational pathology can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival. Conclusions: Computational approaches using WSIs have the potential to provide rapid and cost-effective TMB testing and TILs detection. Survival analysis illuminates potential clinical utility of spatial heterogeneity and co-organization of TMB and TILs as a prognostic biomarker in BLCA which warrants further validation in future studies.

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ADP-ribosylation factor (Arf)-GTPase-activating protein (GAP) with coiled-coil, ankyrin repeat and PH domains 1 (ACAP1) has been reported to serve as an adaptor for clathrin coat complex playing a role in endocytic recycling and cellular migration. The potential role of ACAP1 in lung adenocarcinoma (LUAD) has not been yet completely defined. We performed the comprehensive analyses, including gene expression, survival analysis, genetic alteration, function enrichment, and immune characteristics. ACAP1 was remarkably downregulated in tumor tissues, and linked with the clinicopathologic features in LUAD patients. Prognostic analysis demonstrated that low ACAP1 expression was correlated with unsatisfactory overall survival (OS) and disease specific survival (DSS) in LUAD patients. Moreover, ACAP1 could be determined as a prognostic biomarker according to Cox proportional hazard model and nomogram model. We also confirmed that ACAP1 was downregulated in two LUAD cell lines, comparing to normal lung cell. Overexpression of ACAP1 caused a profound attenuation in cell proliferation, migration, invasion, and promoted cell apoptosis. Additionally, functional enrichment analyses confirmed that ACAP1 was highly correlated with T cell activation and immune response. Then, we further conducted immune landscape analyses, including single cell RNA sequencing, immune cells infiltration, and immune checkpoints. ACAP1 expression was positively associated with the infiltrating level of immune cells in TME and the expression of immune checkpoint molecules. This study first comprehensively analyzed molecular expression, clinical implication, and immune landscape features of ACAP1 in LUAD, suggesting that ACAP1 was predictive of prognosis and could serve as a potential biomarker predicting immunotherapy response for LUAD patients.