Ginseng for improving the quality of life in people with cancer

  • Protocol
  • Intervention



This is the protocol for a review and there is no abstract. The objectives are as follows:

To assess the effects of ginseng supplementation for improving general quality of life and for reducing cancer-related fatigue and sleep disorders in people with cancer.


Description of the condition

An increasingly important issue in cancer health care is improvement in quality of life (QoL), whatever the type or stage of disease (Zonderman 2012). The broad spectrum of cancer symptoms and treatments may contribute to a decrease in overall Qol in people with cancer. When cancer is diagnosed, oncologists use different treatments such as surgery, chemotherapy, radiotherapy or immunotherapy to enhance survival, and it is during and after this medical treatment that physicians and patients might seek complementary treatments to improve QoL (Felce 1995).

People who have been diagnosed with cancer are willing to do everything they can to combat the disease, manage its symptoms, cope with the side effects of their treatment and enhance their overall QoL (Molassiotis 2009). Many clinicians and patients have been attracted to the use of complementary medicine and herbal drugs because popular opinion has it that herbal medicines are 'natural' and hence are not associated with significant side effects (Sievenpiper 2004). Physicians now indicate that more than 75% of their patients with cancer use complementary and alternative medicine (CAM) (Barnes 2004). Research has shown that at least two-thirds of all people with cancer worldwide are using complementary medicine (Harold 2000). In addition, up to 91% of people with cancer in Western countries use some forms of complementary and/or herbal medicine (Molassiotis 2009). A systematic review of 26 surveys across 13 countries showed that the prevalence of CAM use by people with cancer ranged from 7% to 64% (Ernst 2010). Therefore, evaluation of QoL is considered an outcome measure in some cancer treatment studies (Zonderman 2012).

Cancer can produce many different symptoms that may diminish quality of life. Fatigue, tiredness and unpleasantness have been reported as the most distressing symptoms after cancer treatment (59% to 96%) (Marchionni 2006). Fatigue in particular is multi-dimensional and includes several modes of expression in physical and cognitive functions (Smets 1993). Recent evidence describing patterns of cancer-related fatigue suggests that fatigue increases during active treatment and continues for an indefinite period afterwards (Clark 1998). People with cancer who are undergoing active treatment have stated that fatigue is a major obstacle to their everyday activities and is a factor in their diminishing QoL (Theobald 2004).

The symptoms of fatigue can be categorised into two groups: (1) somatic symptoms, including a somatic sense of heaviness, cold knees, puffiness, headaches, joint pain and muscle pain; and (2) psychological symptoms, such as depression, anxiety and restlessness (Yarbro 1996). It has been reported that fatigue can persist for years after completion of cancer treatment, suggesting that even long-term cancer survivors are in need of appropriate treatment (Minton 2008a).

The consequences of fatigue are reflected in its detrimental effects on a patient's QoL, self-care and social activities (Smets 1993). However, because mechanisms and causes of fatigue are poorly understood, treatment options might be limited (Campos 2011). The interactions between cancer-related fatigue and other cancer symptoms, including insomnia, pain, depression and anxiety, are complex, warranting treatment plans that focus on relief of specific symptoms like fatigue to improve QoL in people with cancer (Theobald 2004).

Medicinal herbs may have a biological basis for their effects, which have been shown in laboratory studies on animals and cell lines (Tan 2011). These natural products have been found to inhibit proliferation, induce apoptosis in cancer cells, suppress angiogenesis, enhance the immune system, induce cell differentiation and inhibit telomerase activities and growth of tumours (Baeshen 2012; Chen 2010). A growing number of researchers have demonstrated that medicinal herbs might enhance adjuvant therapies with anticancer activities. They may also reduce toxic side effects and as a result improve QoL (Tan 2011; Zong 2012b).

Unlike prescribed drugs, natural products are not regulated for purity and potency and adverse effects, and the potential for drug interactions should be considered, as these could be caused by impurities or batch-to-batch variability of herbal products (Cupp 1999). Therefore, a natural medicinal herb that contains a wide variety of known and unknown substances may present a risk for unwanted effects and overdosage.

Description of the intervention

Although herbs cannot replace surgery, radiotherapy or chemotherapy for the treatment of cancer, ginseng has been used by both people with cancer and healthy individuals to restore and enhance vital energy and improve QoL (Sievenpiper 2004; Lee 2011). Ginseng has been used medicinally for thousands of years in many countries (Shergis 2013) and is the most popular medicinal herb in Traditional Chinese Medicine (TCM). The name ginseng comes from the Chinese 'Jen Sheng', meaning 'man-herb'. The rhizome, or root, of the plant is the part that is most commonly consumed; it has a humanoid shape. This shape represents the traditional belief that ginseng has properties to heal all aspects of the body (Hofseth 2007). In Asian countries, extracts of ginseng have been used by both healthy individuals and patients as general health elixirsand performance enhancers and for prevention and treatment of a variety of diseases, including cancer (Ernst 2010). Many clinical trials have investigated the pharmaceutical effects, efficacies and active components of ginseng (Shergis 2013). In addition, studies have proposed that ginseng reduces physical, chemical and biological stress, while increasing general vitality and immune function (Elam 2006; Molassiotis 2009; Lee 2011). Ginseng is marketed and used in many products to maintain natural energy as it increases physical and mental capacities, promotes general health and improves mood by controlling fatigue and promoting QoL (Chen 2010).

Ginseng is any one of 11 species belonging to the genus Panax of the family Araliaceae. Five main species of ginseng have been identified: American, Chinese, Korean, Japanese and Siberian (or Russian). Siberian ginseng is in the same family, but not genus, as true ginseng. The active compounds in Siberian ginseng are eleutherosides, not ginsenosides, which are the main bioactive compounds in other ginseng species (Deyama 2001). Asian ginseng-the root of Panax ginseng C.A. Meyer-and American ginseng-the root of Panax quinquefoliate L-are used most commonly (Coleman 2003; Cui 2006).

Ginseng can be used in fresh or dried forms. The fresh ginseng-white ginseng-and red ginseng present many beneficial properties, but traditional medicine has it that red ginseng has greater potency (Chang 2003). Ginseng is believed to have a higher bioactive component and is much more effective when harvested after four to five years of growth (Chang 2003).

The chemical composition of ginseng is affected by the following factors: the species, the habitat in which it grows, the maturity of the plant and the geographical origin of cultivation. In addition, harvesting, storage and post-harvest processing can affect the bioactive compound of ginseng (Qu 2009). Researchers have shown that the pharmaceutical and medicinal effects of ginseng can vary according to the species. Panax ginseng has a 'warm' property and is known to refill the 'vital energy', but Panax quinquefolius has a 'cooling' effect and is used mainly to reduce the 'internal heat' and uphold the secretion of body fluids (Chan 2000). In addition, the distribution of the main bioactive compounds in ginseng-ginsenosides or ginseng saponins-varies with the species (Cho 2012). When ginseng is used properly, it appears to be safe. In many countries, it is considered a supplement food, not a drug, and most documented side effects are related to inappropriate use. Nevertheless, reported side effects include hypertension, diarrhoea, sleeplessness, mastalgia, skin eruptions and vaginal bleeding (Nocerino 2000).

How the intervention might work

Although the mechanism of the anticancerous effect of ginseng remains to be elucidated, the constituents of the Panax genus (which are thought to contribute to its bioactivity) are a unique mix of triterpene saponins known as ginsenoside (Radix 1999). Ginsenosides have been isolated and evaluated for pharmacological effects and on the basis of their chemical structure are classified into three groups: the Panaxadiol group (Rb1, Rb2, Rb3, Rc, etc.), the Panaxatriol group (Re, Rf, Rg1, Rg2, Rh1) and the oleanolic acid group (e.g. Ro) (Scholey 2010). These ginsenosides have varying concentrations in ginseng species as a result of the different processing methods that affect deacetylation of enzymes within the raw plant. It has been well documented that the polysaccharides contained within ginseng possess various antitumour activities, including preventive and inhibitory effects against tumours, as well as enhancing immunological functions (Zong 2012b). Ginseng polysaccharides have been shown to inhibit tumour growth caused by induction of apoptosis and stimulation of macrophages (Figure 1) (Scholey 2010).

Figure 1.

Flowchart represents the mechanism of action of ginseng antitumour initiation, promotion and progression. VEGF: vascular endothelial growth factor; FGF: fibroblast growth factor; HPA:hypothalamic-pituitary-adrenal; NK: natural killer cell; COX-2: cyclooxygenase-2; iNOS: inducible nitric oxide synthase; NFκB: nuclear factor kappa B; cFLIP: cellular form of FLICE inhibitory protein; Bcl2: B-cell lymphoma; ROS: reactive oxygen species; and RNS: reactive nitrogen species.

Why it is important to do this review

Ginseng is popular in China and has gained significant popularity in Western societies. It was estimated that ginseng was the second top-selling herbal supplement in the United States in 2000. Ginseng has been included in the Pharmacopoeias of several Western countries, such as Germany, France, Austria and the United Kingdom (Cui 2006). Many studies have reported that ginseng promotes a wide range of pharmacological activities in different parts of the body, including the immune, cardiovascular, endocrine and central nervous systems. Ginseng has been documented to have antimutagenic and cancer-inhibitory properties in in vitro, in vivo and human clinical trials. Epidemiological studies conducted in Korea have reported that ginseng significantly decreased the risk of cancer in the respiratory tract, gastrointestinal tract, liver, pancreas and ovaries (Yun 1998). In Asian countries, patients use ginseng as a complementary therapy for cancer and cancer-related symptoms (Chang 2003). However, evidence of efficacy is sparse. Thus, it is necessary to conduct a systematic review to provide collective information for scientists and health care consumers. Moreover, treatment of late symptoms and side effects is an important aspect of care for people with cancer because of the longevity associated with successful cancer treatment. This review seeks to provide an up-to-date critical view of growing evidence on the effectiveness of ginseng in improving QoL in people with cancer.


To assess the effects of ginseng supplementation for improving general quality of life and for reducing cancer-related fatigue and sleep disorders in people with cancer.


Criteria for considering studies for this review

Types of studies

Randomised controlled trials (RCTs) and quasi-RCT studies. Quasi-experimental studies that lack the element of randomisation to the treatment or control group are included. Groups that are very different or for which outcomes are not clearly described and those with any bias in selection and administration of the intervention will be excluded.

Types of participants

Adult participants (18 years of age or over) diagnosed with cancer at any site and at any stage who are receiving chemotherapy or radiotherapy or hormone therapy and have fatigue that is related both to treatment and to the disease.

Types of interventions

Ginseng used to improved quality of life and to manage fatigue, pain and sleep disorders.

Types of outcome measures

Primary outcomes

The primary outcome will measure self-reported QoL. The tools used for assessment of QoL are inclusive of (but are not limited) EORTC QLQ-C30 (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire), FACT-G (Functional Assessment of Cancer Therapy–General) and QLACS (Quality of Life in Adult Cancer Survivors).

  • Overall health-related QoL (HRQoL) with at least three follow-up intervals after the administration of ginseng.

  • HRQoL domains at three time intervals (3 months, 6 months, 9 months), including, but not limited to:

    • physical function;

    • psychological function;

    • social function; and

    • general health perceptions.

  • Disease- or treatment-related symptoms, or both (e.g. sexual functioning, neuropathy or cognitive changes, chronic fatigue, sleep disorder).

Secondary outcomes
  • Overall survival (OS) rate

  • Improvement in side effects (i.e. sleeplessness, fatigue)

Adverse outcomes of interest will include the following:

  • Any side effect/harms associated with ginseng administration based on Common Terminology Criteria for Adverse Events (CTCAE) v4.03 (Wiffen 2010).

  • Cancer recurrence or detection of new cancer.

Search methods for identification of studies

Electronic searches

The following databases will be searched;

  • Cochrane Central Register of Controlled Trials (CENTRAL), current issue

  • MEDLINE (1946 to date)

  • EMBASE (1980 to date)

The MEDLINE search strategy is presented in Appendix 1. For databases other than MEDLINE, we will adapt the search strategy accordingly. We will identify all relevant articles on PubMed and using the 'related articles' feature and will perform further searches for newly published articles.

We will not apply language restrictions to the electronic searches. Where possible, if relevant studies are found in languages other than English, we will make arrangements to translate non-English articles.

Searching other resources

The review authors will search all citations of relevant articles listed in MEDLINE and CINAHL and will consider handsearching, particularly when abstracts and conference proceedings of associated meetings are not available online. We will include Chinese and French language articles in the searches, and if the abstracts are appropriate, these articles will be translated. When information in a published paper is insufficient, an attempt will be made to contact primary authors for clarification or for additional information.

We will search the following for ongoing trials:

If ongoing trials that have not been published are identified through these searches, we will approach the principal investigators and major co-operative groups active in this area to ask for relevant data.

Electronic databases such as Zetoc ( and OCLC WorldCat Dissertations and Theses (WorldCatDissertations) ( will be searched for conference proceedings and abstracts.

Data collection and analysis

We will extract data from included studies as described in the Cochrane Handbook for Systematic Reviews of Interventions, Version 5.1.0 (Higgins 2011). Data will be analysed using Review Manager 5 (Review Manager 2012).

Selection of studies

We will include RCTs and quasi-randomised RCTs in the current review. All RCTs or quasi-RCTs comparing use of ginseng for people with cancer with any alternative treatments or placebo will be reviewed to evaluate effects on the outcome measure.

Data extraction and management

We will design a data extraction form. For eligible studies, at least two review authors (T. Fathi Najafi, R. Assadi) will extract the data using the agreed upon form. We will resolve discrepancies through discussion, or, if required, we will consult a third review author (S. Jahanfar) to resolve the conflict. We will enter data into Review Manager 5 software (Review Manager 2012) and will check for accuracy.

When information regarding any of the above is unclear, we will attempt to contact authors of the original reports to obtain further details.

Assessment of risk of bias in included studies

Two review authors (F. Namvar, T. Fathi Najafi) will independently assess risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will resolve any disagreement by discussion or by involving a third review author (S. Jahanfar). We will contact the authors of original reports in cases of any ambiguity in information.

Sequence generation (checking for possible selection bias)

We will describe, for each included study, the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups.

We will assess the method as:

  • low risk of bias (any truly random process, e.g. random number table; computer random number generator);

  • high risk of bias (any non-random process, e.g. odd or even date of birth; hospital or clinic record number); or

  • risk of bias unclear.

Allocation concealment (checking for possible selection bias)

We will describe for each included study the method used to conceal the allocation sequence to determine whether intervention allocation could have been foreseen in advance of, or during, recruitment, or changed after assignment.

We will assess the method as:

  • low risk of bias (e.g. telephone or central randomisation; consecutively numbered sealed opaque envelopes);

  • high risk of bias (open random allocation; e.g. unsealed or non-opaque envelopes, alternation; date of birth); or

  • risk of bias unclear.

Blinding of participants and personnel (checking for possible performance bias)

We will describe for each included study the method used, if any, to blind study participants and personnel from knowledge of which intervention a participant received. We will consider that studies are at low risk of bias if they are blinded, or if we judge that lack of blinding could not have affected the results. We will assess blinding separately for different outcomes or classes of outcomes.

We will assess the method as:

  • low, high or unclear risk of bias for participants and staff; and

  • low, high or unclear risk of bias for personnel.

Blinding of outcome assessment (checking for possible detection bias)

We will describe for each included study the method used, if any, to blind outcome assessors from knowledge of which intervention a participant received. We will assess blinding separately for different outcomes or classes of outcomes.

We will assess the method used to blind outcome assessment as:

  • low, high or unclear risk of bias.

Incomplete outcome data (checking for possible attrition bias resulting from the quantity, nature and handling of incomplete outcome data)

We will describe for each included study and for each outcome or class of outcomes the completeness of data, including attrition, and exclusions from the analysis. We will state whether attrition and exclusions are reported, the numbers included in the analysis at each stage (compared with total number of randomly assigned participants), reasons for attrition or exclusion when reported and whether missing data are balanced across groups or are related to outcomes. When sufficient information is reported or is supplied by the trial authors, we will re-include missing data in the analyses. We will assess methods as:

  • low risk of bias (e.g. no missing outcome data; missing outcome data balanced across group) or less than 10% attrition at any stage;

  • high risk of bias (e.g. numbers or reasons for missing data unbalanced across group; 'as treated, analysis done with substantial departure of intervention received from that assigned at randomisation) or greater than 20% attrition; or

  • unclear risk of bias.

Selective reporting bias (checking for reporting bias)

We will describe for each included study how we investigate the possibility of selective outcome reporting bias and what we find.

We will assess the methods as:

  • low risk of bias (where it is clear that all of the study’s prespecified outcomes and all expected outcomes of interest to the review have been reported);

  • high risk of bias (where not all of the study’s prespecified outcomes have been reported; one or more reported primary outcomes were not prespecified; outcomes of interest are reported incompletely and so cannot be used or study fails to include results of a key outcome that would have been expected to have been reported); or

  • unclear risk of bias.

Other sources of bias (checking for bias due to problems not covered by any of the above)

We will describe for each included study any important concerns that we had about other possible sources of bias.

We will assess whether each study was free of other problems that could put it at risk of bias.

  • Low risk of other bias.

  • High risk of other bias.

  • Unclear whether risk of other bias is present.

Overall risk of bias

We will make explicit judgements about whether studies are at high risk of bias, according to the criteria given in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). With reference to the criteria discussed above, we will assess the likely magnitude and direction of the bias, and whether we consider it likely to impact the findings. We will explore the impact of the level of bias by undertaking sensitivity analyses (see "Sensitivity analysis").

Measures of treatment effect

Dichotomous data

For dichotomous data, we will present results as summary risk ratio (RR) with 95% confidence intervals (CIs), and for adverse outcomes, an RR of less than one signifies that results favour the group receiving the intervention.

Continuous data

For continuous data, we will use the mean difference if outcomes are measured in the same way between trials. We will use the standardised mean difference to combine trials that measured the same outcome but used different methods.

Unit of analysis issues

Studies will be assessed if any non-standard designs are included.

Dealing with missing data

Clinical trials will be assessed to determine whether the results are reported on an intention-to-treat (ITT) basis, and if not, the numbers and reasons for withdrawals and dropouts will be assessed.

For included studies, we will note levels of attrition. We planned to explore the impact of including studies with high levels of missing data (greater than 20% attrition) in the overall assessment of treatment effect by using sensitivity analysis.

Assessment of heterogeneity

A forest plot will be plotted together with the I2 test for heterogeneity at the 20% level. If heterogeneity is found, reasons will be explored by subanalysis based on the type of population (hospital-based or population-based) and the intervention (type of ginseng) used. If a high level of heterogeneity persists, random-effects models and sensitivity analyses will be used.

We will not investigate heterogeneity below 20%. We anticipate some variation between studies due to inclusion of various cancer types.

Heterogeneity across individual studies will be quantified by the I2 statistic method (Higgins 2011). Low, moderate and high degrees of heterogeneity will be approximated by I2 values of 25%, 50% and 75%, respectively. Reasons for heterogeneity will be investigated by eyeballing extreme RR and by performing sensitivity analysis. Subgroup analyses will be conducted according to study design, type of ginseng and year of publication. Studies with extreme RR or with the greatest weight will be removed individually and in a sequential manner to estimate their impact on the overall RR.

Assessment of reporting biases

Potential biases will be reported as detected.

If 10 or more studies are included in the meta-analysis, we plan to investigate possible sources of reporting bias (such as publication bias) by using funnel plots. For continuous outcomes, we will use the test proposed by Egger (Egger 1997), and for dichotomous outcomes, we will use the test proposed by Harbord (Harbord 2006). If we detect asymmetry in any of these tests or by visual assessment, we will perform exploratory analyses to investigate the reasons and will report the findings.

Data synthesis

We will carry out statistical analysis using Review Manager 5 (Review Manager 2012). We will use fixed-effect meta-analysis for combining data when it is reasonable to assume that studies are estimating the same underlying treatment effect (i.e. when trials examined the same intervention and trial populations and methods are judged sufficiently similar). If we suspect clinical heterogeneity sufficient to expect that underlying treatment effects would differ between trials, or if we detect substantial statistical heterogeneity, we plan to use random-effects meta-analysis to produce an overall summary if an average treatment effect across trials is considered clinically meaningful.

The random-effects summary will be treated as the average range of possible treatment effects, and we will discuss the clinical implications of differing treatment effects between trials. If the average treatment effect is not clinically meaningful, we will not combine trials.

If we use random-effects analyses, the results will be presented as the average treatment effect with 95% confidence intervals and with estimates of T² and I².

Subgroup analysis and investigation of heterogeneity

If findings permit, and if we identify substantial heterogeneity, we will investigate by using subgroup analyses and sensitivity analyses. We will consider whether an overall summary is meaningful, and if it is, we will use random-effects analysis to produce it.

We plan to carry out subgroup analysis using the following criteria.

  • Severity and type of cancer.

  • Baseline physical status.

  • Type and dose of intervention.

  • Sociodemographic characteristics of target population.

  • Gender of target population.

We will use the following outcomes in subgroup analysis.

  • Improvement in quality of life.

  • Sleep disorders.

  • Physical ability.

When sufficient information is available, we will assess differences between subgroups by performing interaction tests available within Review Manager 5 (Review Manager 2012). We will report the results of subgroup analyses by quoting the the Chi2 statistic and the P value, along with the interaction test I² value.

Sensitivity analysis

We will assess risk of bias in trials and plan to carry out sensitivity analysis, temporarily omitting from the meta-analysis any trials with high risk of bias. On the basis of findings, we will consider trials at high risk of bias in sensitivity analysis if allocation concealment is unclear or if attrition is greater than 20%. We will also carry out random-effects analyses, rather than the fixed-effect analyses, on outcomes with statistical heterogeneity more than 20%.


We are grateful for the technical assistance and editorial support provided by the Cochrane Gynecological and Orphan Cancer Group.

The National Institute for Health Research (NIHR) is the largest single funder of the Cochrane Gynaecological Cancer Group. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the NIHR, NHS or the Department of Health.


Appendix 1. MEDLINE search strategy


1   exp Panax/
2   Eleutherococcus/
3   (panax* or ginseng* or eleutherococcus or jen shen* or schinseng* or ninjin* or renshen* or ren seng or shen* jen or insam or hong shen or ginsana).mp.
4   1 or 2 or 3
5   exp Neoplasms/
6   (cancer* or tumor* or tumour* or neoplas* or malignan* or carcinoma* or adenocarcinoma* or choriocarcinoma* or leukemia* or leukaemia* or metastat* or sarcoma* or teratoma*).mp.
7   5 or 6
8   4 and 7
9   randomized controlled
10 controlled clinical
11 randomized.ab.
12 placebo.ab.
13 drug therapy.fs.
14 randomly.ab.
15 trial.ab.
16 groups.ab.
17 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16
18 8 and 17
19 exp animals/ not
20 18 not 19

mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier 

What's new

27 March 2014AmendedContact details updated.

Contributions of authors

  • Draft the protocol: Farideh Namvar, Shayesteh Jahanfar

  • Develop and run the search strategy: Farideh Namvar, Reza Assadi

  • Obtain copies of trials: Farideh Namvar, Shayesteh Jahanfar

  • Select which trials to include (2 people): Farideh Namvar, Tahereh Fathi Najafi

  • Extract data from trials (2 people): Tahereh Fathi Najafi, Reza Assadi

  • Enter data into RevMan: Farideh Namvar, Tahereh Fathi Najafi

  • Carry out the analysis: Tahereh Fathi Najafi, Reza Assadi

  • Interpret the analysis: Tahereh Fathi Najafi, Shayesteh Jahanfar, Farideh Namvar

  • Draft the final review: Farideh Namvar, Shayesteh Jahanfar

  • Update the review: Farideh Namvar, Shayesteh Jahanfar, Tahereh Fathi Najafi

Declarations of interest

None known.

Sources of support

Internal sources

  • None, Not specified.

External sources

  • None, Not specified.