Background Quality of life is an important concept which is subjective and personal; what is an acceptable quality of life to one may be 'worse than death' to another. The objective of this study was to develop and validate a questionnaire to assess relatives' perceptions and attitudes towards their terminal stage cancer patients' management (information disclosure, treatment choice, hospitalization and support-communication and care) including aspects regarding end-of-life and quality-of-life decisions. Methods The final study consisted of 146 relatives of advanced terminal stage cancer patients receiving palliation, attending a Pain Relief and Palliative Care Unit. The questionnaire incorporated 6 multi-item and 7 single-item scales, and was developed following a systematic review of measures appropriate for use in palliative care settings. Results Following analysis of the 25-item scale, the questionnaire has been validated as a shortened 21-item scale consisting of 5 multi-item and 5 single-item scales. Factor analysis was based upon information disclosure, hospitalization, and support-communication demonstrating Cronbach's alpha coefficients of 0.66, 0.5 and 0.70 respectively. Average item totals and inter-item scale correlations were between 0.62–0.70, with convergent validity correlations between 0.60–0.86. The questionnaire was well accepted by all subjects with an 8–10 minute completion time. Conclusion The shortened 21-item self-assessment questionnaire may provide acceptable and valid assessment of caregiver(s)/Greek cancer patients' relatives perceptions on palliative care.

BackgroundThe interleukin-17 (IL-17) family contains six homologous genes, IL-17A to IL-17F. Growing evidence indicates that dysregulated IL-17 family members act as major pathogenic factors in the early and late stages of cancer development and progression. However, the prevalence and predictive value of IL-17 for immune checkpoint inhibitor (ICI) therapeutic effectiveness in multiple tumor types remain largely unknown, and the associations between its expression levels and immunotherapy-associated signatures also need to be explored.MethodsThe pan-cancer dataset in The Cancer Genome Atlas (TCGA) was downloaded from UCSC Xena (http://xena.ucsc.edu/). The immunotherapeutic cohorts included IMvigor210, which were obtained from the Gene Expression Omnibus database and included in a previously published study. Other datasets, namely, the GEO dataset and PRECOG, GEO, and METABRIC databases, were also included. In 33 TCGA tumor types, a pan-cancer analysis was carried out including their expression map, clinical risk assessment, and immune subtype analysis, along with their association with the stemness indices, tumor microenvironment (TME) in pan-cancer, immune infiltration analysis, ICI-related immune indicators, and drug sensitivity. RT-PCR was also carried out to verify the gene expression levels among MCF-10A and MCF-7 cell lines.ResultsThe expression of the IL-17 family is different between tumor and normal tissue in most cancers, and consistency has been observed between gene activity and gene expression. RT-PCR results show that the expression differences in the IL-17 family of human cell (MCF-10A and MCF-7) are consistent with the bioinformatics differential expression analysis. Moreover, the expression of the IL-17 family can be a sign of patients’ survival prognosis in some tumors and varies in different immune subtypes. Moreover, the expression of the IL-17 family presents a robust correlation with immune cell infiltration, ICI-related immune indicators, and drug sensitivity. High expression of the IL-17 family is significantly related to immune-relevant pathways, and the low expression of IL-17B means a better immunotherapeutic response in BLCA.ConclusionCollectively, IL-17 family members may act as biomarkers in predicting the prognosis of the tumor and the therapeutic effects of ICIs, which provides new guidance for cancer treatment.

This is a dataset of images with or without tumor-infiltrating lymphocytes (TILs). The original images are from Abousamra et al. (2022) and Saltz et al. (2018), and the original whole slide images are from TCGA. This dataset is a subset of the data presented in Abousamra et al. (2022) (with new data partitions).

If you use this dataset, please cite the following papers, as well as this Zenodo page.

Abousamra, S., Gupta, M. D., Hou, L., Batiste, R., Zhao, T., Shankar, A., Rao, A., Chen, C., Samaras, D., Kurc, T., & Saltz, J. (2022). Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer. Frontiers in Oncology, 5971. https://doi.org/10.3389/fonc.2021.806603

Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., & Danilova, L. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Reports, 23(1), 181-193.

The acknowledgements from the Frontiers in Oncology and Cell Reports papers are included below:

This work was supported by the National Institutes of Health (NIH) and National Cancer Institute (NCI) grants UH3-CA22502103, U24-CA21510904, 1U24CA180924-01A1, 3U24CA215109-02, and 1UG3CA225021-01 as well as generous private support from Bob Beals and Betsy Barton. AR and AS were partially supported by NCI grant R37-CA214955 (to AR), the University of Michigan (U-M) institutional research funds and also supported by ACS grant RSG-16-005-01 (to AR). AS was supported by the Biomedical Informatics & Data Science Training Grant (T32GM141746). This work was enabled by computational resources supported by National Science Foundation grant number ACI-1548562, providing access to the Bridges system, which is supported by NSF award number ACI-1445606, at the Pittsburgh Supercomputing Center, and also a DOE INCITE award joint with the MENNDL team at the Oak Ridge National Laboratory, providing access to Summit high performance computing system. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.

We are grateful to all the patients and families who contributed to this study. Funding from the Cancer Research Institute is gratefully acknowledged, as is support from National Cancer Institute (NCI) through U54 HG003273, U54 HG003067, U54 HG003079, U24 CA143799, U24 CA143835, U24 CA143840, U24 CA143843, U24 CA143845,U24 CA143848, U24 CA143858, U24 CA143866, U24 CA143867, U24 CA143882, U24 CA143883, U24 CA144025, P30 CA016672, U24CA180924, U24CA210950, U24CA215109, NCI Contract HHSN261201400007C, and Leidos Biomedical Contract 14X138. A.U.K.R. and P.S were supported by CCSG Bioinformatics Shared Resource P30 CA01667, ITCR U24 Supplement 1U24CA199461-01, a gift from Agilent technologies, CPRIT RP150578, and a Research Scholar Grant from the American Cancer Society (RSG-16-005-01). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation XSEDE Science Gateways program under grant ACI-1548562 allocation TG-ASC130023. The authors would like to thank Stony Brook Research Computing and Cyberinfrastructure and the Institute for Advanced Computational Science at Stony Brook University for access to the high-performance LIred and SeaWulf computing systems, the latter of which was supported by National Science Foundation grant (#1531492).

This dataset includes 304,097 image patches. All images are 100 x 100 pixels at 0.5 micrometers per pixel. An image is TIL-positive if there are at least two TILs present.

Refer to images-tcga-tils-metadata.csv for information about each image. That spreadsheet has the following columns:

partition,study,barcode,label,path,md5

Partition specifies which partition the image is part of (train, val, test). Study is the TCGA study the image is part of (e.g., acc for TCGA-ACC). Barcode is the TCGA participant barcode. This is used during partitioning, to ensure that images from the same participant are not present in different data partitions. Label is either til-negative or til-positive. An image is til-positive if there are at least two TILs in the image. Path is the path to the PNG image. All images are stored as PNG. Md5 is the md5 hash of the image. This can be used to ensure there are no duplicate images and to verify the integrity of images.

There are study-specific directories in the directory images-tcga-tils, and there is a directory named pancancer that includes images from all the included TCGA studies. That directory uses symlinks to avoid storing duplicate data.

These data are being released in BETA version to facilitate early access to the study for research purposes. This collection has not been fully processed by NACDA or ICPSR at this time; the original materials provided by the principal investigator were minimally processed and converted to other file types for ease of use. As the study is further processed and given enhanced features by ICPSR, users will be able to access the updated versions of the study. Please report any data errors or problems to user support and we will work with you to resolve any data related issues. The National Health Interview Survey (NHIS) is conducted annually and sponsored by the National Center for Health Statistics (NCHS), which is part of the U.S. Public Health Service. The purpose of the NHIS is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive across the United States population through the collection and analysis of data on a broad range of health topics. The redesigned NHIS questionnaire introduced in 1997 (see National Health Interview Survey, 1997 [ICPSR 2954]) consists of a core that remains largely unchanged from year to year, plus an assortment of supplements varying from year to year. The 2010 NHIS Core consists of three modules: Family, Sample Adult, and Sample Child. The datasets derived from these modules include Household Level, Family Level, Person Level, Injury/Poison Episode Level, Injury/Poison Verbatim Level, Sample Adult Level, and Sample Child level. The 2010 NHIS supplements consist of stand alone datasets for Cancer Level and Quality of Life data derived from the Sample Adult core and Disability Questions Tests 2010 Level derived from the Family core questionnaire. Additional supplementary questions can be found in the Sample Child dataset on the topics of cancer, immunization, mental health, and mental health services and in the Sample Adult dataset on the topics of epilepsy, immunization, and occupational health. Part 1, Household Level, contains data on type of living quarters, number of families in the household responding and not responding, and the month and year of the interview for each sampling unit. Parts 2-5 are based on the Family Core questionnaire. Part 2, Family Level, provides information on all family members with respect to family size, family structure, health status, limitation of daily activities, cognitive impairment, health conditions, doctor visits, hospital stays, health care access and utilization, employment, income, participation in government assistance programs, and basic demographic information. Part 3, Person Level, includes information on sex, age, race, marital status, education, family income, major activities, health status, health care costs, activity limits, and employment status. Parts 4 and 5, Injury/Poisoning Episode Level and Injury/Poisoning Verbatim Level, consist of questions about injuries and poisonings that resulted in medical consultations for any family members and contains information about the external cause and nature of the injury or poisoning episode and what the person was doing at the time of the injury or poisoning episode, in addition to the date and place of occurrence. A randomly-selected adult in each family was interviewed for Part 6, Sample Adult Level, regarding specific health issues, the relation between employment and health, health status, health care and doctor visits, limitation of daily activities, immunizations, and behaviors such as smoking, alcohol consumption, and physical activity. Demographic information, including occupation and industry, also was collected. The respondents to Part 6 also completed Part 7, Cancer Level, which consists of a set of supplemental questions about diet and nutrition, physical activity, tobacco, cancer screening, genetic testing, family history, and survivorship. Part 8, Sample Child Level, provides information from an adult in the household on medical conditions of one child in the household, such as developmental or intellectual disabilities, respiratory problems, seizures, allergies, and use of special equipment like hearing aids, braces, or wheelchairs. Parts 9 through 13 comprise the additional Supplements and Paradata for the 2010 NHIS. Part 9, Disability Questions Tests 2010 Level

This study explored the introduction of a new approach to treating children with cancer at the Pediatric Cancer Center in Barcelona, Spain. Traditionally, cancer treatment focuses on conventional methods like chemotherapy and radiation, but many families are now seeking additional therapies, such as acupuncture and aromatherapy, to support their child’s care. The study aimed to assess how well these complementary therapies were accepted by patients and their families.

Background The contribution of BRCA1 and BRCA2 to the incidence of male breast cancer (MBC) in the United Kingdom is not known, and the importance of these genes in the increased risk of female breast cancer associated with a family history of breast cancer in a male first-degree relative is unclear. Methods We have carried out a population-based study of 94 MBC cases collected in the UK. We screened genomic DNA for mutations in BRCA1 and BRCA2 and used family history data from these cases to calculate the risk of breast cancer to female relatives of MBC cases. We also estimated the contribution of BRCA1 and BRCA2 to this risk. Results Nineteen cases (20%) reported a first-degree relative with breast cancer, of whom seven also had an affected second-degree relative. The breast cancer risk in female first-degree relatives was 2.4 times (95% confidence interval [CI] = 1.4–4.0) the risk in the general population. No BRCA1 mutation carriers were identified and five cases were found to carry a mutation in BRCA2. Allowing for a mutation detection sensitivity frequency of 70%, the carrier frequency for BRCA2 mutations was 8% (95% CI = 3–19). All the mutation carriers had a family history of breast, ovarian, prostate or pancreatic cancer. However, BRCA2 accounted for only 15% of the excess familial risk of breast cancer in female first-degree relatives. Conclusion These data suggest that other genes that confer an increased risk for both female and male breast cancer have yet to be found.

Raw missense data for the paper Discovering Predisposing Genes for Hereditary Breast Cancer using Deep Learning.

Unfiltered missense variants from healthy tissue of breast cancer patients from 12 high-risk families. The dataset includes 40 subjects, 39 female and one male (35 breast cancer patients, a colon cancer patient, a pancreatic cancer patient and 3 un-affected female family members). Data aligned to refrence genome hg19 (coding and non-coding regions). For additional data please refer to text.

Data from the statewide, population-based, California Cancer Registry (CCR) were analyzed to identify geographic areas with a comparatively high proportion of breast cancers diagnosed at late, or otherwise, advanced stage. Data on women from the CCR aged 40 years and older diagnosed with breast cancer between January 1, 2010 and December 31, 2014 was used. Proportional incidence ratios (PIRs) were calculated by determining the proportion of late-stage breast cancer in counties and Medical Service Study Areas (MSSAs) relative to the proportion in upper-socioeconomic, non-Hispanic white women statewide. The outcomes were mapped, providing visual representations of areas with an excess of advanced stage breast cancer.

Gene fusions involving ETS family transcription factors (mainly TMPRSS2-ERG and TMPRSS2-ETV1 fusions) have been found in ~50% of human prostate cancer cases. Although expression of TMPRSS2-ERG or TMPRSS2-ETV1 fusion alone is insufficient to initiate prostate tumorigenesis, they appear to sensitize prostate epithelial cells for cooperation with additional oncogenic mutations to drive frank prostate adenocarcinoma. To search for such ETS-cooperating oncogenic events, we focused on a well-studied prostate tumor suppressor NKX3.1, as loss of NKX3.1 is another common genetic alteration in human prostate cancer. Previous studies have shown that deletions at 8p21 (harboring NKX3.1) and 21q22 (resulting in TMPRSS2-ERG fusion) were both present in a subtype of prostate cancer cases, and that ERG can lead to epigenetic silencing of NKX3.1 in prostate cancer cells, whereas NKX3.1 can in turn negatively regulate TMPRSS2-ERG fusion expression via suppression of the TMPRSS2 promoter activity. We recently generated knockin mouse models for TMPRSS2-ERG and TMPRSS2-ETV1 fusions, utilizing the endogenous Tmprss2 promoter. We crossed these knockin models to an Nkx3.1 knockout mouse model. In Tmprss2-ERG;Nkx3.1+/- (or -/-) male mice, although we observed a slight but significant upregulation of Tmprss2-ERG fusion expression upon Nkx3.1 loss, we did not detect any significant cooperation between these two genetic events to enhance prostate tumorigenesis in vivo. Furthermore, retrospective analysis of a previously published human prostate cancer dataset revealed that within ERG-overexpressing prostate cancer cases, NKX3.1 loss or deletion did not predict biochemical relapse after radical prostatectomy. Collectively, these data suggest that although TMPRSS2-ERG fusion and loss of NKX3.1 are among the most common mutational events found in prostate cancer, and although each of them can sensitize prostate epithelial cells for cooperating with other oncogenic events, these two events themselves do not appear to cooperate at a significant level in vivo to enhance prostate tumorigenesis.

Brain tumor patients experience physical, psychological, social, and cognitive changes. These changes are challenging for both the patients and their families. These patients and their families need to adapt together on the cancer treatment path. This study aimed to identify the factors affecting adaptation in families of adult patients with brain tumors. A quantitative, cross-sectional study of 165 families of adult patients with primary brain tumors was conducted using a self-administered questionnaire. Partial least squares structural equation modeling was used to test a hypothetical model. The results showed that family stress, family functioning, and family resources influenced on family adaptation in families of adult patients with primary brain tumors. Among these factors, family resources were identified to be the strongest factor associated with family adaptation. The results of this study may be utilized as a theoretical basis in nursing to improve the family adaptation of patients with brain tumors. Regarding nursing practices, the results suggest that nurses should provide family-centered nursing interventions and promote family resources to help brain tumor patients and their families to adapt.

The role of race/ethnicity in genetic predisposition of early-onset cancers can be estimated by comparing family-based cancer concordance rates among ethnic groups. We used linked California health registries to evaluate the relative cancer risks for first degree relatives of patients diagnosed between ages 0-26, and the relative risks of developing distinct second malignancies (SPMs). From 1989-2015, we identified 29,631 cancer patients and 62,863 healthy family members. Given probands with cancer, there were increased relative risks of any cancer for siblings and mothers [standardized incidence ratio (SIR)=3.32;95% confidence interval (CI):2.54-4.35;P<0.001)]and of SPMs (SIR=7.12;95%CI:5.46-9.28;P<0.001). Higher relative risk of any cancer in siblings and mothers (P=0.001) was observed for Latinos (SIR=3.36;95%CI:2.24-5.05) compared to non-Latino White subjects (SIR=2.60;95%CI:1.66-4.06). Latinos had higher relative risks in first degree relatives and higher SPM risk compared to...

Genetic predisposition increases the risk of familial breast cancer. Recent studies indicate that genetic predisposition for familial breast cancer can be ethnic-specific. However, current knowledge of genetic predisposition for the disease is predominantly derived from Western populations. Using this existing information as the sole reference to judge the predisposition in non-Western populations is not adequate and can potentially lead to misdiagnosis. Efforts are required to collect genetic predisposition from non-Western populations. The Egyptian population has high genetic variations in reflecting its divergent ethnic origins, and incident rate of familial breast cancer in Egypt is also higher than the rate in many other populations. Using whole exome sequencing, we investigated genetic predisposition in five Egyptian familial breast cancer families. No pathogenic variants in BRCA1, BRCA2 and other classical breast cancer-predisposition genes were present in these five families. Comparison of the genetic variants with those in Caucasian familial breast cancer showed that variants in the Egyptian families were more variable and heterogeneous than the variants in Caucasian families. Multiple damaging variants in genes of different functional categories were identified either in a single family or shared between families. Our study demonstrates that genetic predisposition in Egyptian breast cancer families may differ from those in other disease populations, and supports a comprehensive screening of local disease families to determine the genetic predisposition in Egyptian familial breast cancer.

The database of germline p53 mutations is a comprehensive database covering all published cases of germline p53 mutations. The current version lists 580 tumors in 448 individuals belonging to 122 independent pedigrees. The database describes each p53 mutation (type of the mutation, exon and codon affected by the mutation, nucleotide and amino acid change), each family (family history of cancer, diagnosis of Li-Fraumeni syndrome), each affected individual (sex, generation, p53 status, from which parent the mutation was inherited) and each tumour (type, age of onset, p53 status-loss of heterozygosity, immunostaining). Each entry contains the original reference(s).

BackgroundSIGLEC family genes can also be expressed on tumor cells in different cancer types, and though it has been found that SIGLEC genes expressed by immune cells can be exploited by tumors to escape immune surveillance, functions of tumor derived SIGLEC expression in tumor microenvironment (TME) were barely investigated, which could play roles in cancer patients’ survival.MethodsUsing bioinformatic analysis, mutation status of SIGLEC family genes was explored through the cBioPortal database, and expression of them in different tumors was explored through the UALCAN database. The GEPIA database was used to compare SIGLEC family genes’ mRNA between cancers and to generate a highly correlated gene list in tumors. A KM-plotter database was used to find the association between SIGLEC genes and survival of patients. The associations between SIGLEC family genes’ expression, immune infiltration, and immune regulators’ expression in TME were generated and examined by the TIMER 2.0 database; the differential fold changes of SIGLEC family genes in specific oncogenic mutation groups of different cancer types were also yielded by TIMER 2.0. The networks of SIGLEC family genes and highly correlated genes were constructed by the STRING database, and gene ontology and pathway annotation of SIGLEC family highly correlated genes were performed through the DAVID database.ResultsSIGLEC family genes were highly mutated and amplified in melanoma, endometrial carcinoma, non-small cell lung cancer, bladder urothelial carcinoma, and esophagogastric adenocarcinoma, while deep deletion of SIGLEC family genes was common in diffuse glioma. Alteration of SIGLEC family genes demonstrated different levels in specific tumors, and oncogenic mutation in different cancer types could influence SIGLEC family genes’ expression. Most SIGLEC family genes were related to patients’ overall survival and progression free survival. Also, tumor derived SIGLEC family genes were related to tumor immune cell infiltration and may regulate TME by influencing chemokine axis.ConclusionOur computational analysis showed SIGLEC family genes expressed by tumor cells were associated with tumor behaviors, and they may also influence TME through chemokine axis, playing vital roles in patients’ survival. Further experiments targeting tumor derived SIGLEC family genes are needed to confirm their influences on tumor growth, metastasis, and immune environment regulation.

Background: High nerve density in tumors and metastasis via nerves (perineural invasion—PNI) have been reported extensively in solid tumors throughout the body including pancreatic, head and neck, gastric, prostate, breast, and colorectal cancers. Ablation of tumor nerves results in improved disease outcomes, suggesting that blocking nerve–tumor communication could be a novel treatment strategy. However, the molecular mechanisms underlying this remain poorly understood. Thus, the aim here was to identify molecular pathways underlying nerve–tumor crosstalk and to determine common molecular features between PNI-associated cancers.Results: Analysis of head and neck (HNSCC), pancreatic, and gastric (STAD) cancer Gene Expression Omnibus datasets was used to identify differentially expressed genes (DEGs). This revealed extracellular matrix components as highly dysregulated. To enrich for pathways associated with PNI, genes previously correlated with PNI in STAD and in 2 HNSCC studies where tumor samples were segregated by PNI status were analyzed. Neurodevelopmental genes were found to be enriched with PNI. In datasets where tumor samples were not segregated by PNI, neurodevelopmental pathways accounted for 12%–16% of the DEGs. Further dysregulation of axon guidance genes was common to all cancers analyzed. By examining paralog genes, a clear pattern emerged where at least one family member from several axon guidance pathways was affected in all cancers examined. Overall 17 different axon guidance gene families were disrupted, including the ephrin–Eph, semaphorin–neuropilin/plexin, and slit–robo pathways. These findings were validated using The Cancer Genome Atlas and cross-referenced to other cancers with a high incidence of PNI including colon, cholangiocarcinoma, prostate, and breast cancers. Survival analysis revealed that the expression levels of neurodevelopmental gene families impacted disease survival.Conclusion: These data highlight the importance of the tumor as a source of signals for neural tropism and neural plasticity as a common feature of cancer. The analysis supports the hypothesis that dysregulation of neurodevelopmental programs is a common feature associated with PNI. Furthermore, the data suggested that different cancers may have evolved to employ alternative genetic strategies to disrupt the same pathways. Overall, these findings provide potential druggable targets for novel therapies of cancer management and provide multi-cancer molecular biomarkers.

Background: The relationship between the interleukin 17 (IL-17) family of cytokines and breast cancer has been widely studied in recent years. Many studies have revealed increased levels of the cytokine IL-17A in estrogen receptor (ER)-negative or triple-negative breast cancer. Upregulation of IL-17A signaling is associated with increased expression of programmed cell death protein 1 (PD-1) and programmed death-ligand 1 (PD-L1) in breast cancer with low ER expression and may elevate the infiltration of CD8+ T cells in tumor tissue. This study aims to determine whether ER downregulates the expression of PD-1/PD-L1, reduces the infiltration of CD8+ T cells, and affects the immune microenvironment by decreasing T-helper 17 (Th17) cell infiltration and inhibiting IL-17 signaling in breast cancer.Methods: Samples in The Cancer Genome Atlas Breast Cancer dataset were grouped by ER status and the PAM50 intrinsic subtype. The expression of IL-17 family cytokines and Th17 cell signature cytokines were compared between groups. IL-17 signaling pathway-related genes that were differentially expressed according to the ER level were identified. The PD-1 and PD-L1 levels were compared between breast cancer samples with different ER statuses and IL-17A/IL-17F expression levels. Correlation analyses of the expression of PD-1/PD-L1 and IL-17 signaling pathway-related genes were performed. The associations of the expression of IL-17 signaling pathway-related genes with the immune microenvironment were investigated.Results: High levels of ER decreased the expression of IL-17A, IL-17C, and IL-17F but increased the expression of IL-17E (IL25), which acts as a suppressor of IL-17 signaling. The expression levels of Th17 cell signature cytokines were significantly increased in ER-negative breast cancer. The expression levels of genes encoding downstream products of IL-17A/IL-17F signaling were downregulated in breast cancer with high ER expression. Increased expression of PD-1/PD-L1 was associated with ER-negative status, IL-17A-positive status, IL-17F-positive status, and upregulation of IL-17 signaling pathway-related genes in breast cancer. Enhanced IL-17 signal transduction was associated with the elevation of CD8+ T cell infiltration and variation of the immune microenvironment of breast cancer.Conclusion: High estrogen receptor levels decrease PD-1/PD-L1 expression and CD8+ T cell infiltration by suppressing Th17 cell infiltration and IL-17 signal transduction in breast cancer.

ObjectiveTo explore the relationship between flavin-containing monooxygenases (FMOs) and peritoneal metastasis (PM) in gastric cancer (GC).Materials and methodsTIMER 2.0 was used to perform pan-cancer analysis and assess the correlation between the expression of FMOs and cancers. A dataset from The Cancer Genome Atlas (TCGA) was used to analyze the correlation between FMOs and clinicopathological features of GC. PM is well established as the most common mode of metastasis in GC. To further analyze the correlation between FMOs and PM of GC, a dataset was obtained from the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database. The results were validated by immunohistochemistry. The relationship between FMOs and PM of GC was explored, and a novel PM risk signature was constructed by least absolute shrinkage and selection operator (LASSO) regression analysis. The regression model’s validity was tested by multisampling. A nomogram was established based on the model for predicting PM in GC patients. The mechanism of FMOs in GC patients presenting with PM was assessed by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses in TCGA and GEO datasets. Finally, the potential relationship between FMOs and immunotherapy was analyzed.ResultsThe pan-cancer analysis in TCGA and GEO datasets showed that FMO1 was upregulated, while FMO2 and FMO4 were downregulated in GC. Moreover, FMO1 and FMO2 correlated positively with the T and N stage of GC in the TCGA dataset. FMO1 and FMO2 expression was a risk factor for GC (hazard ratio: 1.112 and 1.185). The overexpression of FMO1 was significantly correlated with worse disease-free-survival (DFS) and overall survival (OS). However, no relationship was found between FMO2 expression in GC and DFS and OS. PM was highly prevalent among GC patients and typically associated with a worse prognosis. FMO1 was highly expressed in GC with PM. FMO1 and FMO2 were positively correlated with PM in GC. We identified a 12-gene panel for predicting the PM risk signature by LASSO (Area Under Curve (AUC) = 0.948, 95%CI: 0.896–1.000). A 10-gene panel for PM prediction was identified (AUC = 0.932, 95%CI: 0.874–0.990), comprising FMO1 and FMO2. To establish a model for clinical application, a 7-gene panel was established (AUC = 0.927, 95% CI: 0.877–0.977) and successfully validated by multisampling. (AUC = 0.892, 95% CI: 0.878–0.906). GO and KEGG analyses suggest that FMO1 and FMO2 regulate the extracellular matrix and cell adhesion. FMO1 and FMO2 were positively correlated with the immune score of GC, and their expression was associated with the infiltration of immune cells.ConclusionPM in GC is strongly correlated with FMOs. Overall, FMO1 and FMO2 have huge prospects for application as novel diagnostic and therapeutic targets.

BackgroundThe role of Eph receptors and related ephrin (EFN) ligands (as the largest family of transmembrane-bound RTKs) in immunomodulation in many types of cancer, especially bladder cancer (BLCA), is scarcely known.MethodsA pan-cancer dataset was retrieved from The Cancer Genome Atlas (TCGA) to explore the relation between Eph receptor/EFN ligand family genes and immunomodulators and tumor-infiltrated immune cells (TIICs). Local BLCA, GSE32894, and GSE31684 cohorts were applied to validate. The IMvigor210 cohort was employed to explore the relationship between EPHB6 and immunotherapy response. Moreover, association between EPHB6 and molecular subtype was investigated to explore potential therapeutic strategies. Immunohistochemical staining of CD8 and CD68 was performed to validate the correlation between EPHB6 and TIICs.ResultsThe pan-cancer analysis revealed variations in the immunological effects of Eph receptor/EFN ligand family genes across different types of cancer. EPHB6 expression negatively correlated with the expression of the majority of immunomodulators (including HLA and immune checkpoints), and CD8 T cells and macrophages in both the TCGA-BLCA and validation BLCA cohorts, shaping a cold immune microenvironment with inhibited immunity. In the IMvigor210 cohort, patients with high-EPHB6 highly correlated with a non-inflamed, low PD-L1 expression immune phenotype, and correspondingly, with less responders to immunotherapy. The high-EPHB6 group, enriched with the basal subtype, presented significantly fewer TP53 and more FGFR3 genomic alterations. Finally, a novel EPHB6-related Genes signature, with reliable and robust ability in prognosis prediction, was constructed.ConclusionsThis study comprehensively investigated the immunological effects of Eph receptor/EFN ligand family genes pan-cancer, and specially identified the immunosuppressive role of EPHB6 in BLCA. Furthermore, EPHB6 may predict the molecular subtype and prognosis of BLCA, and serve as a novel therapeutic target to improve the sensitivity of immunotherapy.

BackgroundThe role of Eph receptors and related ephrin (EFN) ligands (as the largest family of transmembrane-bound RTKs) in immunomodulation in many types of cancer, especially bladder cancer (BLCA), is scarcely known.MethodsA pan-cancer dataset was retrieved from The Cancer Genome Atlas (TCGA) to explore the relation between Eph receptor/EFN ligand family genes and immunomodulators and tumor-infiltrated immune cells (TIICs). Local BLCA, GSE32894, and GSE31684 cohorts were applied to validate. The IMvigor210 cohort was employed to explore the relationship between EPHB6 and immunotherapy response. Moreover, association between EPHB6 and molecular subtype was investigated to explore potential therapeutic strategies. Immunohistochemical staining of CD8 and CD68 was performed to validate the correlation between EPHB6 and TIICs.ResultsThe pan-cancer analysis revealed variations in the immunological effects of Eph receptor/EFN ligand family genes across different types of cancer. EPHB6 expression negatively correlated with the expression of the majority of immunomodulators (including HLA and immune checkpoints), and CD8 T cells and macrophages in both the TCGA-BLCA and validation BLCA cohorts, shaping a cold immune microenvironment with inhibited immunity. In the IMvigor210 cohort, patients with high-EPHB6 highly correlated with a non-inflamed, low PD-L1 expression immune phenotype, and correspondingly, with less responders to immunotherapy. The high-EPHB6 group, enriched with the basal subtype, presented significantly fewer TP53 and more FGFR3 genomic alterations. Finally, a novel EPHB6-related Genes signature, with reliable and robust ability in prognosis prediction, was constructed.ConclusionsThis study comprehensively investigated the immunological effects of Eph receptor/EFN ligand family genes pan-cancer, and specially identified the immunosuppressive role of EPHB6 in BLCA. Furthermore, EPHB6 may predict the molecular subtype and prognosis of BLCA, and serve as a novel therapeutic target to improve the sensitivity of immunotherapy.

Background: Proteasome 26S subunit, ATPase gene (PSMC) family members play a critical role in regulating protein degradation and are essential for tumor development. However, little is known about the integrative function and prognostic significance of the PSMC gene family members in lung cancer.Methods: First, we assessed the expression and prognostic features of six PSMC family members in pan-cancer from The Cancer Genome Atlas (TCGA) dataset. Hence, by focusing on the relationship between PSMC genes and the prognostic, genomic, and tumor microenvironment features in lung adenocarcinoma (LUAD), a PSMC-based prognostic signature was established using consensus clustering and multiple machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO) Cox regression, CoxBoost, and survival random forest analysis in TCGA and GSE72094. We then validated it in three independent cohorts from GEO and estimated the correlation between risk score and clinical features: genomic features (alterations, tumor mutation burden, and copy number variants), immune profiles (immune score, TIDE score, tumor-infiltrated immune cells, and immune checkpoints), sensitivity to chemotherapy (GDSC, GSE42127, and GSE14814), and immunotherapy (IMvigor210, GSE63557, and immunophenoscore). Twenty-one patients with LUAD were included in our local cohort, and tumor samples were submitted for evaluation of risk gene and PD-L1 expression.Results: Nearly all six PSMC genes were overexpressed in pan-cancer tumor tissues; however, in LUAD alone, they were all significantly correlated with overall survival. Notably, they all shared a positive association with increased TMB, TIDE score, expression of immune checkpoints (CD276 and PVR), and more M1 macrophages but decreased B-cell abundance. A PSMC-based prognostic signature was established based on five hub genes derived from the differential expression clusters of PSMC genes, and it was used to dichotomize LUAD patients into high- and low-risk groups according to the median risk score. The area under the curve (AUC) values for predicting survival at 1, 3, and 5 years in the training cohorts were all >.71, and the predictive accuracy was also robust and stable in the GSE72094, GSE31210, and GSE13213 datasets. The risk score was significantly correlated with advanced tumor, lymph node, and neoplasm disease stages as an independent risk factor for LUAD. Furthermore, the risk score shared a similar genomic and immune feature as PSMC genes, and high-risk tumors exhibited significant genomic and chromosomal instability, a higher TIDE score but lower immune score, and a decreased abundance of B and CD8+ T cells. Finally, high-risk patients were suggested to be less sensitive to immunotherapy but had a higher possibility of responding to platinum-based chemotherapy. The LUAD samples from the local cohort supported the difference in the expression levels of these five hub genes between tumor and normal tissues and the correlation between the risk score and PD-L1 expression.Conclusion: Overall, our results provide deep insight into PSMC genes in LUAD, especially the prognostic effect and related immune profile that may predict therapeutic responses.