Transforming Diagnosis and Therapeutics Using Cancer Genomics.

In past quarter of the century, much has been understood about the genetic variation and abnormal genes that activate cancer in humans. All the cancers somehow possess alterations in the DNA sequence of cancer cell's genome. In present, we are heading toward the era where it is possible to obtain complete genome of the cancer cells for their better diagnosis, categorization and to explore treatment options.

Targeted Therapy and Personalized Medicine.

Targeted therapy and personalized medicine are novel emerging disciplines of cancer research intended for treatment and prevention. One of the most significant advancements in modern oncology is the shift from an organ-centric strategy to a personalized strategy guided by deep molecular analysis. This shift in view, which focuses on the tumour's precise molecular changes, has paved the way for individualized treatment. Researchers and clinicians are using targeted therapies to select the best treatment available based on the molecular characterization of malignant cancer. In the treatment of a cancer, personalized medicine entails the use of genetic, immunological, and proteomic profiling to provide therapeutic alternatives as well as prognostic information about cancer. In this book, targeted therapies and personalized medicine have been covered for specific malignancies, including latest FDA-approved targeted therapies and it also sheds light on effective anti-cancer regimens and drug resistance. This will help to enhance our ability to conduct individualized health planning, make early diagnoses, and choose optimal medications for each cancer patient with predictable side effects and outcomes in a quickly evolving era. Various applications and tools' capacity have been improved for early diagnosis of cancer and the growing number of clinical trials that choose specific molecular targets reflects this predicament. Nevertheless, there are several limitations that must need to be addressed. Hence, in this chapter, we will discuss recent advancements, challenges, and opportunities in personalized medicine for various cancers, with a specific emphasis on target therapies in diagnostics and therapeutics.

Association of MTR A2756G and MTRR A66G Polymorphisms With Male Infertility: An Updated Meta-Analysis.

The objective of the present study was to find out the association of folate genes MTR A2756G and MTRR A66G polymorphisms with the risk of male infertility. The databases of Google Scholar, PubMed, and Science Direct were searched to find relevant studies. Data were extracted from the eligible studies and were analyzed for pooled up odds ratio (OR) with 95% confidence interval (CI). Review Manager 5.4 was used for statistical analysis. Nineteen case-control studies were included in this meta-analysis which comprised 3621 cases and 3327 controls. Pooled analysis revealed that there is a significant association between MTR A2756G polymorphism with male infertility except for the dominant model. The ORs and 95% CI for each genetic model were as follows: 1.21 [1.03-1.42] for the allele model (G vs. A), 2.31 [1.38-3.96] for the additive model (GG vs. AA), 1.17 [0.98-1.38] for the dominant model (GG+AG vs. AA) and 2.10 [1.55-2.86] for the recessive model (GG vs. AG+AA). MTRR A66G has no noticeable association with male infertility. The current meta-analysis suggests that MTR A2756G polymorphism might be a potential risk factor for male infertility. In the future, the sample size should be increased to confirm the present results.

Role of MDR1 Gene Polymorphisms in Human Male Infertility: A Meta-Analysis.

The present meta-analysis is performed to determine the association of C1236T and C3435T polymorphisms in the MDR1 gene. Google Scholar, PubMed, and Science Direct were searched. A total of 47 studies were retrieved, of which only three case-control studies, consisting of 490 cases and 423 controls, met the selection criteria. Odds ratios (ORs) for MDR1 C1236T were as follows: Allelic model (T vs. C): OR = 1.06 [0.83, 1.35]; Additive model (TT vs. CC): OR = 0.91 [0.53, 1.56]; Dominant model (TT+CT vs. CC): OR = 0.83 [0.55, 1.24]; and Recessive model (TT vs. CT+CC): OR = 1.43 [0.95, 2.17]. However, for MDR1 C3435T: Allelic model (T vs. C): OR = 1.06 [0.83, 1.35]; Additive model (TT vs. CC): OR = 1.18 [0.75, 1.88]; Dominant model (TT+CT vs. CC): OR = 1.42 [0.99, 2.04]; and Recessive model (TT vs. CT+CC): OR = 0.90 [0.61, 1.33]. None of the four models presented a significant association of either polymorphism with the risk of infertility in men (p >.05). The present study indicates that MDR1 gene polymorphisms might not be a risk factor for male infertility. Further studies with a larger sample size are needed to be conducted to confirm the findings of the present study.