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Testicular Cancer
Testicular Cancer
Testicular cancer is a malignant neoplasm that originates in the testis.
It is the most common cancer in young men and is highly treatable, especially when detected early.
Symptoms may include a painless lump or swelling in the testicle.
Risk factors include undescended testicle, family history, and certain genetic conditions.
Effective treatment options include surgery, chemotherapy, and radiation therapy.
PubCompare.ai helps researchers optimize testicular cancer research by locating the best protocols from literature, pre-prints, and patents, enhancing reproducibility and accuracy to ensure the most effective approaches for their studies.
It is the most common cancer in young men and is highly treatable, especially when detected early.
Symptoms may include a painless lump or swelling in the testicle.
Risk factors include undescended testicle, family history, and certain genetic conditions.
Effective treatment options include surgery, chemotherapy, and radiation therapy.
PubCompare.ai helps researchers optimize testicular cancer research by locating the best protocols from literature, pre-prints, and patents, enhancing reproducibility and accuracy to ensure the most effective approaches for their studies.
Most cited protocols related to «Testicular Cancer»
Alleles
Amino Acids
Antigens
Arhinia, choanal atresia, and microphthalmia
Diploid Cell
Epitopes
Fingers
Genes
Genotype
Germ Line
Histocompatibility Testing
HLA Typing
Mutation
Neoplasms
Neutrophil
Patients
Testicular Cancer
Tissues
A full list of genes on the X chromosome was downloaded from University of California, Santa Cruz (UCSC)’s HG19.knownGene table browser [29 (link)]. The table was condensed manually from having an entry for each transcription start site to having an entry for each gene. XCI calls from the studies were added to the table, matching alternate gene names from the National Center for Biotechnology Information (NCBI) [30 (link)] along with using the in silico PCR tool in UCSC [31 (link)] with published primers [9 (link)].
Genes were placed into eight categories for an overall XCI status call. If all of a gene’s calls from different studies were the same, then the gene was placed in a category for all subjects, all escapes or all variable escapes. If the majority of studies (2 out of 3 or 3 out of 4) gave the same call, then the gene was placed in the mostly subject, mostly escape or mostly variable escape categories. Genes that had one-call subject or one-call escape and a variable escape call which leaned towards the same call (variable escape in a study, with less than 34 % or greater than 65 % of samples escaping XCI) were also placed in the mostly subject and mostly escape categories. The Cotton DNAm study gave some calls that were escape + variable escape or subject + variable escape; for my categorization, these genes were considered to be whichever call was given in the most tissues, this was usually subject or escape. Genes that had no calls in any of the studies were designated as the no call category, while genes that did not fit any of these other categories were placed in the discordant category. Discordant genes had either an even split of different calls or had one of each call (subject, escape, and variable escape from XCI).
Genes were sorted by their transcript type (mRNA, micro RNA (miRNA), ncRNA, snRNA, transfer ribonucleic acid (tRNA)) as determined by UCSC’s HG19.kgXref table [29 (link)] and if still unknown, a search of NCBI. A list of cancer-testis antigen genes was taken from CTdatabase [32 (link)].
To determine the source of discordancies, genes with three or four calls and only one study giving a different call from the other studies were examined. The study which gave the discordant call was noted, along with the call it gave and the call agreed upon by the other studies.
Genes were placed into eight categories for an overall XCI status call. If all of a gene’s calls from different studies were the same, then the gene was placed in a category for all subjects, all escapes or all variable escapes. If the majority of studies (2 out of 3 or 3 out of 4) gave the same call, then the gene was placed in the mostly subject, mostly escape or mostly variable escape categories. Genes that had one-call subject or one-call escape and a variable escape call which leaned towards the same call (variable escape in a study, with less than 34 % or greater than 65 % of samples escaping XCI) were also placed in the mostly subject and mostly escape categories. The Cotton DNAm study gave some calls that were escape + variable escape or subject + variable escape; for my categorization, these genes were considered to be whichever call was given in the most tissues, this was usually subject or escape. Genes that had no calls in any of the studies were designated as the no call category, while genes that did not fit any of these other categories were placed in the discordant category. Discordant genes had either an even split of different calls or had one of each call (subject, escape, and variable escape from XCI).
Genes were sorted by their transcript type (mRNA, micro RNA (miRNA), ncRNA, snRNA, transfer ribonucleic acid (tRNA)) as determined by UCSC’s HG19.kgXref table [29 (link)] and if still unknown, a search of NCBI. A list of cancer-testis antigen genes was taken from CTdatabase [32 (link)].
To determine the source of discordancies, genes with three or four calls and only one study giving a different call from the other studies were examined. The study which gave the discordant call was noted, along with the call it gave and the call agreed upon by the other studies.
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Antigens
Genes
Genes, vif
Gossypium
MicroRNAs
Oligonucleotide Primers
RNA, Messenger
RNA, Untranslated
Small Nuclear RNA
Testicular Cancer
Tissues
Transcription Initiation Site
Transfer RNA
X Chromosome
Some variation in death rates for amenable causes are due to differences in behavioural and environmental risk exposure rather than differences in personal health-care access and quality.48 (link), 54 (link), 55 (link) Using the wide range of risk factors assessed by GBD,48 (link) we risk-standardised death rates to the global level of risk exposure.48 (link) We did not risk-standardise for variations in metabolic risk factors directly targeted by personal health care: systolic blood pressure, total cholesterol, and fasting plasma glucose. For example, stroke deaths due to high systolic blood pressure are amenable to primary care management of hypertension.
To risk-standardise death rates, we removed the joint effects of national behavioural and environmental risk levels calculated in GBD, and added back the global levels of risk exposure:
where mjascy is the death rate from cause j in age a, sex s, location c, and year y; mrjascy is the risk-standardised death rate; JPAFjascy is the joint population attributable fraction (PAF) for cause j, in age a, sex s, country c, and year y for all behavioural and environmental risks included in GBD; and JPAFjasgy is the joint PAF for cause j, in age a, sex s, and year y at the global level.
GBD provides joint PAF estimation for multiple risks combined, which takes into account the mediation of different risks through each other. Further detail on joint PAF computation is available in theappendix (pp 5–8) .
We used the GBD world population standard to calculate age-standardised risk-standardised death rates from each cause regarded as amenable to health care.47 (link) We did not risk-standardise death rates from diarrhoeal diseases as mortality attributable to unsafe water and sanitation was not computed for high-SDI locations; such standardisation could lead to higher risk-standardised death rates in those countries compared with countries where mortality was attributed to unsafe water and sanitation.48 (link) With all causes for which no PAFs are estimated in GBD, such as neonatal disorders and testicular cancer, risk-standardised death rates equalled observed death rates.
The effects of risk-standardisation are highlighted by comparing the log of age-standardised mortality rates to the log of age-standardised risk-standardised mortality rates for amenable causes (appendix p 14 ). For each SDI quintile, many countries had differing levels of age-standardised mortality rates but their risk-standardised mortality rates were similar, demonstrating how underlying local risk exposure can skew measures of mortality amenable to personal health care.
To risk-standardise death rates, we removed the joint effects of national behavioural and environmental risk levels calculated in GBD, and added back the global levels of risk exposure:
where mjascy is the death rate from cause j in age a, sex s, location c, and year y; mrjascy is the risk-standardised death rate; JPAFjascy is the joint population attributable fraction (PAF) for cause j, in age a, sex s, country c, and year y for all behavioural and environmental risks included in GBD; and JPAFjasgy is the joint PAF for cause j, in age a, sex s, and year y at the global level.
GBD provides joint PAF estimation for multiple risks combined, which takes into account the mediation of different risks through each other. Further detail on joint PAF computation is available in the
We used the GBD world population standard to calculate age-standardised risk-standardised death rates from each cause regarded as amenable to health care.47 (link) We did not risk-standardise death rates from diarrhoeal diseases as mortality attributable to unsafe water and sanitation was not computed for high-SDI locations; such standardisation could lead to higher risk-standardised death rates in those countries compared with countries where mortality was attributed to unsafe water and sanitation.48 (link) With all causes for which no PAFs are estimated in GBD, such as neonatal disorders and testicular cancer, risk-standardised death rates equalled observed death rates.
The effects of risk-standardisation are highlighted by comparing the log of age-standardised mortality rates to the log of age-standardised risk-standardised mortality rates for amenable causes (
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Cerebrovascular Accident
Cholesterol
Diarrhea
Environmental Exposure
Essential Hypertension
Glucose
High Blood Pressures
Joints
Neonatal Diseases
Plasma
Population at Risk
Systole
Systolic Pressure
Testicular Cancer
For immunohistochemical analysis, 4 μm TMA sections were automatically pre-treated using the PT-link system (DAKO, Glostrup, Denmark) and then stained in an Autostainer Plus (DAKO) with the affinity-purified polyclonal anti-PODXL antibody HPA 2110 (Atlas Antibodies, Stockholm, Sweden, diluted 1:250). The Envision Flex/HRP (K8010) kit (DAKO) was used for visualisation of the staining. The specificity of this antibody, originally generated within the Human Protein Atlas (HPA) project, has been validated using western blotting and protein arrays and PODXL protein expression has been mapped by immunohistochemistry in 48 types of normal tissues and 20 common cancers (Uhlen et al, 2005 (link); Ponten et al, 2008 (link)) (www.proteinatlas.org ). The same antibody has also been used to detect PODXL expression in testicular carcinoma in a recent study (Cheung et al, 2011 ).
To control for heterogenous expression patterns, IHC was also performed on full-face sections from 10 randomly selected cases denoted as having negative PODXL expression and 10 cases with high (score 3–4) PODXL expression.
To control for heterogenous expression patterns, IHC was also performed on full-face sections from 10 randomly selected cases denoted as having negative PODXL expression and 10 cases with high (score 3–4) PODXL expression.
Antibodies
Antibodies, Anti-Idiotypic
Antibody Specificity
Face
Genetic Heterogeneity
Histocompatibility Testing
Immunoglobulins
Immunohistochemistry
Malignant Neoplasms
NR4A2 protein, human
PODXL protein, human
Protein Arrays
Proteins
Testicular Cancer
The UKB is a population-based cohort of 502,611 individuals in the United Kingdom. Study participants were aged 40–69 at recruitment between 2006 and 2010, at which time all participants provided detailed information about lifestyle and health-related factors and provided biological samples57 (link). GERA participants were drawn from adult Kaiser Permanente Northern California (KPNC) health plan members who provided a saliva sample for the Research Program on Genes, Environment and Health (RPGEH) between 2008 and 2011. Individuals included in this study were selected from the 102,979 RPGEH participants who were successfully genotyped as part of GERA and answered a baseline survey concerning lifestyle and medical history58 (link),59 (link).
Cancer cases in the UKB were identified via linkage to various national cancer registries established in the early 1970s57 (link). Data in the cancer registries are compiled from hospitals, nursing homes, general practices, and death certificates, among other sources. The latest cancer diagnosis in our data from the UKB occurred in August 2015. GERA cancer cases were identified using the KPNC Cancer Registry, including all diagnoses captured through June 2016. Following SEER standards, the KPNC Cancer Registry contains data on all primary cancers (i.e., cancer diagnoses that are not secondary metastases of other cancer sites; excluding non-melanoma skin cancer) diagnosed or treated at any KPNC facility since 1988.
In both cohorts, individuals with at least one recorded prevalent or incident diagnosis of a borderline, in situ, or malignant primary cancer were defined as cases for our analyses. Individuals with multiple cancer diagnoses were classified as a case only for their first cancer. For the UKB, all diagnoses described by International Classification of Diseases (ICD)-9 or ICD-10 codes were converted into ICD-O-3 codes; the KPNC Cancer Registry already included ICD-O-3 codes. We then classified cancers according to organ site using the SEER site recode paradigm60 . We grouped all esophageal and stomach cancers and, separately, all oral cavity and pharyngeal cancers to ensure sufficient statistical power. The 18 most common cancer types (except non-melanoma skin cancer) were examined. Testicular cancer data were obtained from the UKB only due to the small number of cases in GERA.
Controls were restricted to individuals who had no record of any cancer in the relevant registries, who did not self-report a prior history of cancer (other than non-melanoma skin cancer), and, if deceased, who did not have cancer listed as a cause of death. Individuals whose first cancer diagnosis was for a cancer not among our 18 cancers of interest were excluded. For analyses of sex-specific cancer sites (breast, cervix, endometrium, ovary, prostate, and testis), controls were restricted to individuals of the appropriate sex.
Cancer cases in the UKB were identified via linkage to various national cancer registries established in the early 1970s57 (link). Data in the cancer registries are compiled from hospitals, nursing homes, general practices, and death certificates, among other sources. The latest cancer diagnosis in our data from the UKB occurred in August 2015. GERA cancer cases were identified using the KPNC Cancer Registry, including all diagnoses captured through June 2016. Following SEER standards, the KPNC Cancer Registry contains data on all primary cancers (i.e., cancer diagnoses that are not secondary metastases of other cancer sites; excluding non-melanoma skin cancer) diagnosed or treated at any KPNC facility since 1988.
In both cohorts, individuals with at least one recorded prevalent or incident diagnosis of a borderline, in situ, or malignant primary cancer were defined as cases for our analyses. Individuals with multiple cancer diagnoses were classified as a case only for their first cancer. For the UKB, all diagnoses described by International Classification of Diseases (ICD)-9 or ICD-10 codes were converted into ICD-O-3 codes; the KPNC Cancer Registry already included ICD-O-3 codes. We then classified cancers according to organ site using the SEER site recode paradigm60 . We grouped all esophageal and stomach cancers and, separately, all oral cavity and pharyngeal cancers to ensure sufficient statistical power. The 18 most common cancer types (except non-melanoma skin cancer) were examined. Testicular cancer data were obtained from the UKB only due to the small number of cases in GERA.
Controls were restricted to individuals who had no record of any cancer in the relevant registries, who did not self-report a prior history of cancer (other than non-melanoma skin cancer), and, if deceased, who did not have cancer listed as a cause of death. Individuals whose first cancer diagnosis was for a cancer not among our 18 cancers of interest were excluded. For analyses of sex-specific cancer sites (breast, cervix, endometrium, ovary, prostate, and testis), controls were restricted to individuals of the appropriate sex.
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Adult
Biopharmaceuticals
Breast
Cancer of Pharynx
Cervix Uteri
Diagnosis
Endometrium
Familial Atypical Mole-Malignant Melanoma Syndrome
Gastric Cancer
Gender
Genes
Genes, vif
Health Planning
Malignant Neoplasms
Neoplasm Metastasis
Oral Cavity
Ovary
Prostate
Saliva
Testicular Cancer
Testis
Wellness Programs
Most recents protocols related to «Testicular Cancer»
Input data from GBD 2019 was utilized to generate the estimation of genitourinary cancers incidence and burden. In the study, the data source for genitourinary cancers included hospital records, emergency department records, insurance claims, surveys, and vital registration systems globally. The methodology of data inputting, mortality estimation, and modeling for GBD 2019 has been comprehensively reviewed in previously published articles (8 (link), 9 (link)). The definition of genitourinary cancers counts on the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). In the GBD study, genitourinary cancers in the study have four dimensions: kidney cancer, bladder cancer, prostate cancer, and testicular cancer, their ICD-10 codes are stated as follows: C64-C65.9, D30.0-D30.1, and D41.0-D41.1 for kidney cancer; C67-C67.9, D09.0, D30.3, D41.4-D41.8, and D49.4 for bladder cancer; C61-C61.9, D07.5, D29.1, and D40.0 for prostate cancer; C62-C62.9, D29.2-D29.8, and D40.1-D40.8 for testicular cancer (Table 1S ).
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Cancer of Bladder
Cancer of Kidney
Genitourinary Cancer
Prostate Cancer
Testicular Cancer
Testicular tissue samples and clinical data from prepubertal boys aged up to 14 years, who participated in fertility preservation programmes prior to chemo-/radiotherapy treatment at Amsterdam UMC (AUMC) between 2011 and 2018 and Universitair Ziekenhuis Brussel (UZB) between 2002 and 2017 were included. The older boys in our cohort were offered testicular tissue cryopreservation, because they could not produce an ejaculate. At AUMC, prepubertal boys were offered fertility preservation before the initiation of known gonadotoxic therapy, irrespective of the infertility risk. At UZB, only children at high risk for infertility (above 80%), which was estimated based on the selected treatment, were included in the fertility preservation programme. Prepubertal patients considered to be at high risk of infertility include the ones receiving high-dose alkylating and platinum-based agents, total body irradiation, or testicular radiotherapy, yet the criteria are not strictly established (Goossens et al., 2020 (link); Delgouffe et al., 2022 ).
At both centers, children with a history of testicular torsion or cryptorchidism or with testicular malignancies were excluded from the fertility preservation programmes. Testicular malignancies were excluded because of the risk of reintroducing cancer cells during auto-transplantation of the testicular biopsy and because testicular malignancies are known to negatively affect gonadal function (Petersen et al., 1998 (link)) and can possibly negatively affect future fertility treatment. Patients diagnosed with Klinefelter syndrome were also excluded from this study.
At both centers, children with a history of testicular torsion or cryptorchidism or with testicular malignancies were excluded from the fertility preservation programmes. Testicular malignancies were excluded because of the risk of reintroducing cancer cells during auto-transplantation of the testicular biopsy and because testicular malignancies are known to negatively affect gonadal function (Petersen et al., 1998 (link)) and can possibly negatively affect future fertility treatment. Patients diagnosed with Klinefelter syndrome were also excluded from this study.
Biopsy
Boys
Cells
Child
Cryopreservation
Cryptorchidism
Fertility
Fertility Preservation
Gonads
Klinefelter Syndrome
Malignant Neoplasms
Only Child
Patients
Platinum
Radiotherapy
Spermatic Cord Torsion
Sterility, Reproductive
Testicular Cancer
Testis
Tissues
Transplantation
Whole-Body Irradiation
The histological diagnosis was performed at the Pathology Unit of the Campus Bio-Medico University Hospital Foundation (Rome, Italy), according to WHO criteria. Clinically relevant molecular predictive markers (EGFR, KRAS, NRAS, BRAF, BRCA1, BRCA2, HRAS, MSI, MMR, c-KIT, PDGFRA, ALK, ROS1, HER2, and PD-L1) were evaluated in-house in 111/184 patient samples, according to a clinical request to establish target therapies (i.e., non-small cell lung cancer (NSCLC), colon cancer, breast cancer, melanoma, etc.) For 73/184 patients, in-house molecular evaluations were not performed, because they were not requested for therapeutic purposes at the time of diagnosis (i.e., cholangiocarcinoma, testicular cancer, hepatocellular carcinoma, etc.).
In-house molecular-marker evaluations included the following predictive markers: EGFR, KRAS, NRAS, BRAF, BRCA1, BRCA2, HRAS, c-KIT, PDGFRA, HER2 (single nucleotide variants (SNVs)), and ALK (SNVs). These were assayed with q-PCR or DNA-based NGS. MSI was assayed with q-PCR. ALK (rearrangements), ROS−1, MET, and RET were assayed with RNA-based NGS or RT-PCR. ALK (rearrangements), ROS-1, and HER2 (amplification) were assayed with FISH. ALK, ROS1, MMR, and PD-L1 were assayed with immunohistochemistry. Different techniques were adopted based on the features of the tumor sample. Histological diagnosis and in-house molecular data were extracted from the clinical reports.
This study was conducted in accordance with the Declaration of Helsinki. The committee of Medical Ethics of the Campus Bio-Medico University Hospital Foundation (Roma, Italy) approved this study. The protocol code is 2015–003385-84.
In-house molecular-marker evaluations included the following predictive markers: EGFR, KRAS, NRAS, BRAF, BRCA1, BRCA2, HRAS, c-KIT, PDGFRA, HER2 (single nucleotide variants (SNVs)), and ALK (SNVs). These were assayed with q-PCR or DNA-based NGS. MSI was assayed with q-PCR. ALK (rearrangements), ROS−1, MET, and RET were assayed with RNA-based NGS or RT-PCR. ALK (rearrangements), ROS-1, and HER2 (amplification) were assayed with FISH. ALK, ROS1, MMR, and PD-L1 were assayed with immunohistochemistry. Different techniques were adopted based on the features of the tumor sample. Histological diagnosis and in-house molecular data were extracted from the clinical reports.
This study was conducted in accordance with the Declaration of Helsinki. The committee of Medical Ethics of the Campus Bio-Medico University Hospital Foundation (Roma, Italy) approved this study. The protocol code is 2015–003385-84.
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Biological Markers
BRAF protein, human
BRCA1 protein, human
Cancer of Colon
CD274 protein, human
Cholangiocarcinoma
Diagnosis
EGFR protein, human
ERBB2 protein, human
Ethics Committees, Clinical
Fishes
Gene, BRCA2
Gene Rearrangement
Gypsies
Hepatocellular Carcinomas
Immunohistochemistry
K-ras Genes
Malignant Neoplasm of Breast
Melanoma
Neoplasms
Non-Small Cell Lung Carcinoma
NRAS protein, human
Nucleotides
Patients
Proto-Oncogene Protein c-kit
Renal Adysplasia
Reverse Transcriptase Polymerase Chain Reaction
ROS1 protein, human
Testicular Cancer
Therapeutics
We utilized data from the Danish Cancer Registry and the Danish Register for Causes of Death to identify three types of cancer outcomes: i) first-time cancer incidence defined as first cancer diagnosis, ii) cancer mortality rate defined as death due to cancer, and iii) 5-year case fatality rate defined as any cause of death within five years of primary cancer diagnosis. Cancer was defined as cancer of all sites excluding non-melanoma skin cancer (C44) in accordance with the classifications used by the Association of the Nordic Cancer registries (www.nordcan.iarc.fr/en ) using the following ICD-10 codes: C00–C43, C45–C99, D09.0–D09.1, D30.1–D30.9 D32–D33, D35.2–D35.4, D41.0–D41.9, D42–D43, D44.3–D44.5, D45–D47. For cancer incidence, we also assessed the four most common types of cancer among women (malignant melanoma, C43; breast cancer, C50; cancers of the brain and central nervous system (CNS), C70–C72, C75.1–C75.3, D32–D33, D35.2–D35.4, D42–D43, D44.3–D44.5; cervical cancer, C53)) and men (testicular cancer, C62; malignant melanoma; cancers of the brain and central nervous system; Hodgkin's lymphoma, C81). Mortality is very low in this age group, so we were unable to assess cancer mortality and case-fatality for subtypes of cancer.
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Age Groups
Brain Neoplasm, Malignant
Central Nervous System
Cervical Cancer
Diagnosis
Familial Atypical Mole-Malignant Melanoma Syndrome
Hodgkin Disease
Malignant Neoplasm of Breast
Malignant Neoplasms
Testicular Cancer
Woman
The abundances of 22 immune cells were estimated via the CIBERSORT algorithm from the gene expression profile of TCGA-LIHC.16 (link) In this analysis, the validated leukocyte gene signature containing 547 genes was used to distinguish 22 cell phenotypes, including seven T-cell types, naïve and memory B cells, plasma cells, natural killer (NK) cells, and myeloid subsets. Additionally, different parts involved in the immune escape were investigated. First, single-nucleotide variant (SNV) neoantigens, Indel neoantigens, cancer-testis antigens (CTA) score, intratumor heterogeneity, number of segments, fraction altered, number or fraction of segments with loss of heterozygosity (LOH), homologous recombination deficiency (HRD), aneuploidy score, T-cell receptor (TCR) diversity (Shannon Entropy and Richness) score, and the expression of immunomodulatory molecules17 (link) were enrolled or calculated for the investigation of potential immune escape mechanisms in the two clusters.
Aneuploidy
Antigens
Cells
Entropy
Genes
Genetic Heterogeneity
Homologous Recombination
Immunomodulation
INDEL Mutation
Leukocytes
Loss of Heterozygosity
Memory B Cells
Natural Killer Cells
Nucleotides
Phenotype
Plasma Cells
T-Cell Receptor
T-Lymphocyte
Testicular Cancer
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More about "Testicular Cancer"
Testicular cancer, also known as testicular neoplasm or germ cell tumor, is a type of cancer that originates in the testes, the male reproductive glands.
It is the most common form of cancer in young men between the ages of 15 and 35.
Despite its prevalence, testicular cancer is highly treatable, especially when detected early.
Symptoms of testicular cancer may include a painless lump or swelling in the testicle, although some cases may be asymptomatic initially.
Risk factors for developing testicular cancer include undescended testicle (cryptorchidism), family history, and certain genetic conditions like Klinefelter syndrome.
Effective treatment options for testicular cancer include surgery (orchiectomy), chemotherapy, and radiation therapy.
Depending on the stage and type of cancer, a combination of these therapies may be employed.
Researchers can utilize tools like PubCompare.ai to optimize their testicular cancer studies by identifying the best protocols from literature, preprints, and patents, enhancing reproducibility and accuracy.
Other relevant techniques and materials that may be used in testicular cancer research include cell culture media like DMEM, statistical software like SPSS version 21, antibodies like Anti-BAM-490, and genomic analysis tools like the NCounter Digital Analyzer and SureSelect Human All Exon v.2 Kit.
Transfection reagents like FuGENE 6 may also be employed to study genetic factors in testicular cancer.
Researchers can leverage reference management software like EndNote vX7.1 to organize their literature and citations.
By understanding the latest advancements in testicular cancer research and utilizing the appropriate tools and techniques, researchers can contribute to the ongoing efforts to improve the diagnosis, treatment, and prevention of this highly treatable form of cancer.
It is the most common form of cancer in young men between the ages of 15 and 35.
Despite its prevalence, testicular cancer is highly treatable, especially when detected early.
Symptoms of testicular cancer may include a painless lump or swelling in the testicle, although some cases may be asymptomatic initially.
Risk factors for developing testicular cancer include undescended testicle (cryptorchidism), family history, and certain genetic conditions like Klinefelter syndrome.
Effective treatment options for testicular cancer include surgery (orchiectomy), chemotherapy, and radiation therapy.
Depending on the stage and type of cancer, a combination of these therapies may be employed.
Researchers can utilize tools like PubCompare.ai to optimize their testicular cancer studies by identifying the best protocols from literature, preprints, and patents, enhancing reproducibility and accuracy.
Other relevant techniques and materials that may be used in testicular cancer research include cell culture media like DMEM, statistical software like SPSS version 21, antibodies like Anti-BAM-490, and genomic analysis tools like the NCounter Digital Analyzer and SureSelect Human All Exon v.2 Kit.
Transfection reagents like FuGENE 6 may also be employed to study genetic factors in testicular cancer.
Researchers can leverage reference management software like EndNote vX7.1 to organize their literature and citations.
By understanding the latest advancements in testicular cancer research and utilizing the appropriate tools and techniques, researchers can contribute to the ongoing efforts to improve the diagnosis, treatment, and prevention of this highly treatable form of cancer.